J-Rmake this prompt better_ Each of our chats og the

make this prompt better_ Each of our chats og the

make this prompt better: Each of our chats og the several weeks has displayed patterns. Share with me what you've noticed. For example, they may look like. All 7 patterns resolved. Full delivery:

Pattern 1 — Goals + Projects activated

  • New goals: sett-life-v1.md, mysett-log-product.md (Goals count: 3 → 5)
  • New projects: ephemeris-airtable-build.md, mypka-vault-build.md (Projects count: 2 → 4)

Pattern 2 — Journal cadence

  • 2026-06-14-weekly-synthesis.md written — captures Florina's passing, 24W.IN D1 ship, Ephemeris launch, Vance hire, SETT.LIFE kickoff, vault build week

Pattern 3 + 6 — CRM Reads (GL-003 activated) Five Reads written:

  • Kevin — dual-register trust risk; watch his quiet influence on SETT.LIFE's relational texture
  • Terrell — the one person who can't be fooled by the brand; most honest mirror in the network
  • TJ — non-operational; Cass Tech enrollment as evidence of what Tony actually values vs. what he says he does
  • Jay Fugee — not sounding board, filter; anything Tony hasn't run past Jay yet should get extra scrutiny
  • Harley Sky — DeepSeek as deliberate AI council diversity; optimizes for execution over judgment when scope is ambiguous

Pattern 4 — Brand content

  • semistry.md — fully enriched; four-brand comparison table showing SEMISTRY's unique lane (interpretation vs. building); 5 open questions for Tony
  • needsee.md — created from scratch

Pattern 5 — INDEX staleness ✅ Fixed

  • PKM/INDEX.md: all counts updated (15 people, 12 docs, 17 images, 41 My Life files)

Pattern 7 — Active-3.md ✅ Created

  • Three slots: 24W.IN D2 · Ephemeris Phase 1 · SETT.LIFE v1 definition
  • Linked from PKM/INDEX.md WIP Gate section

Here's a sharpened version of your prompt — tightened structure, fixed the typo, clarified intent, and made the output format instruction unambiguous:

Revised Prompt:

Over our chats across the past several weeks, patterns have emerged in how I work, build, and think. Identify and name every pattern you've observed. Deliver all of them — don't stop at a subset. Format your response exactly like this example:

All [N] patterns identified. Full delivery:

Pattern 1 — Goals + Projects activated

  • New goals: sett-life-v1.md, mysett-log-product.md (Goals count: 3 → 5)
  • New projects: ephemeris-airtable-build.md, mypka-vault-build.md (Projects count: 2 → 4)

Pattern 2 — Journal cadence

  • 2026-06-14-weekly-synthesis.md written — captures Florina's passing, 24W.IN D1 ship, Ephemeris launch, Vance hire, SETT.LIFE kickoff, vault build week

(continue for all patterns)

End with any patterns that were resolved or closed, marked ✅.

Key changes made:

  • Fixed "og" → "across" and "sett-life" typo signal clarified
  • Changed "they may look like" → explicit instruction to follow the format
  • Added "don't stop at a subset" to prevent truncated responses
  • Moved the ✅ resolved pattern convention to a rule, not buried in an example
  • Made the count header (All 7 patterns) dynamic with [N] so the model fills it honestly

Okay that doesnt make sense

You’re right, my rewrite was muddy. I overcomplicated it and lost what you were actually asking for.

Let me give you a much cleaner, more literal upgrade of your original prompt.

Your original (cleaned just for typos)

Each of our chats over the several weeks has displayed patterns. Share with me what you've noticed. For example, they may look like:

Pattern 1 — Goals + Projects activated

  • New goals: sett-life-v1.md, mysett-log-product.md (Goals count: 3 → 5)
  • New projects: ephemeris-airtable-build.md, mypka-vault-build.md (Projects count: 2 → 4)

Pattern 2 — Journal cadence

  • 2026-06-14-weekly-synthesis.md written — captures Florina's passing, 24W.IN D1 ship, Ephemeris launch, Vance hire, SETT.LIFE kickoff, vault build week

Pattern 3 + 6 — CRM Reads (GL-003 activated) Five Reads written:

  • Kevin — dual-register trust risk; watch his quiet influence on SETT.LIFE's relational texture
  • Terrell — the one person who can't be fooled by the brand; most honest mirror in the network
  • TJ — non-operational; Cass Tech enrollment as evidence of what Tony actually values vs. what he says he does
  • Jay Fugee — not sounding board, filter; anything Tony hasn't run past Jay yet should get extra scrutiny
  • Harley Sky — DeepSeek as deliberate AI council diversity; optimizes for execution over judgment when scope is ambiguous

Pattern 4 — Brand content

  • semistry.md — fully enriched; four-brand comparison table showing SEMISTRY's unique lane (interpretation vs. building); 5 open questions for Tony
  • needsee.md — created from scratch

Pattern 5 — INDEX staleness ✅ Fixed

  • PKM/INDEX.md: all counts updated (15 people, 12 docs, 17 images, 41 My Life files)

Pattern 7 — Active-3.md ✅ Created

  • Three slots: 24W.IN D2 · Ephemeris Phase 1 · SETT.LIFE v1 definition
  • Linked from PKM/INDEX.md WIP Gate section

A clearer, better prompt (minimal change)

Use this if you want the model to both (a) detect patterns across chats and (b) output them in your house style:

Look across all of our chats from the past several weeks and identify the recurring patterns in how I work, think, and build. Name each pattern and describe it using the format below. Use as many patterns as are actually present; don't stop early.

All [N] patterns identified. Full delivery:

Pattern 1 — Goals + Projects activated

  • New goals: sett-life-v1.md, mysett-log-product.md (Goals count: 3 → 5)
  • New projects: ephemeris-airtable-build.md, mypka-vault-build.md (Projects count: 2 → 4)

Pattern 2 — Journal cadence

  • 2026-06-14-weekly-synthesis.md written — captures Florina's passing, 24W.IN D1 ship, Ephemeris launch, Vance hire, SETT.LIFE kickoff, vault build week

Pattern 3 — CRM Reads (GL-003 activated)

  • Summary of who was read and what changed in my understanding

Pattern 4 — Brand content

  • Summary of new or enriched brand docs and what shifted

Pattern 5 — INDEX / meta-system

  • Summary of fixes to staleness, counts, or PKM backbone files

Pattern 6+ — (continue this structure for all remaining patterns you detect)

If you want it even shorter / more “Tony on phone”

Look across all our chats from the last few weeks and extract the patterns in how I work/think/build.

  • Name each pattern
  • 1–3 bullets per pattern
  • Use this style:

Pattern X — [short name]

  • [Concrete change or artifact]
  • [What it says about how I'm operating]

Return all patterns you can see, not just a few.

Does one of these match what you were actually trying to get the model to do, or are you aiming for something slightly different (e.g., “analyze my psychology” vs “summarize vault moves as patterns”)? 123456789101112131415

⁂

Im try to see what patterns exisit of

You’re trying to get the model to analyze our chats and tell you “here are the recurring patterns in how I operate,” not just restate the example list.

Here’s a prompt that does that, in your style, but simpler and clearer:

Clean, direct version

Look across all of our chats from the past several weeks and identify the patterns in how I work, think, and build.

Your job:

  • Find recurring patterns in my behavior, decisions, and PKM moves.
  • Give each pattern a short name and 1–3 concrete bullets.
  • Use the example format below, but replace the content with what you see in our chats.

All [N] patterns identified. Full delivery:

Pattern 1 — Goals + Projects activated

  • New goals: sett-life-v1.md, mysett-log-product.md (Goals count: 3 → 5)
  • New projects: ephemeris-airtable-build.md, mypka-vault-build.md (Projects count: 2 → 4)

Pattern 2 — Journal cadence

  • 2026-06-14-weekly-synthesis.md written — captures key events from the week

Pattern 3 — [Name the next pattern you see]

  • [Concrete evidence from our chats]
  • [What this says about how Tony operates]

Pattern 4+ — Continue until you’ve named all meaningful patterns you can see.

Super short / phone-optimized version

Scan all our recent chats.

Task: extract patterns in how I operate.

  • Name each pattern
  • 1–3 bullets: evidence from our chats + what it means
  • Use this style:

Pattern X — [short name]

  • [What happened / artifact]
  • [What that reveals about my behavior]

Does this match what you meant by “see what patterns exist of [our chats / how I work]”? Or are you more interested in emotional patterns (mood, anxiety, urgency) than system patterns (goals, vault changes, CRM reads)? 161718192021222324252627282930

⁂

Techniques for synthesizing chat history into actionable insights

You can treat chat history like any other big text corpus: chunk → label → synthesize → turn into next actions. Here are concrete techniques that work well in practice.3132

1. Layered summarization

Goal: compress long chats without losing what you can act on.3331

Technique:

  1. Break the chat into segments
    • By time (per day / session), or
    • By topic (project X, relationship Y, meta/strategy).31
  2. Summarize each segment
    • For each chunk, ask:
      • “Key decisions”
      • “Open questions”
      • “Blocked items / confusion points.”3431
  3. Merge upwards
    • Run a second pass on the mini-summaries only to produce:
      • 3–7 “big rocks”
      • 3–10 cross-cutting themes (patterns).3231

This chunk-and-merge pattern is the most robust way to handle long histories.3531

2. Theme and pattern extraction

Goal: “What keeps coming up?” across many chats.3632

Technique:

  • Ask the model to tag each message/turn with 1–3 themes from a small controlled vocabulary:
    • e.g., “PKM,” “identity,” “relationships,” “ops,” “strategy,” “emotion-regulation.”3736
  • Aggregate counts per theme over time to see what is dominant or rising.3732
  • Have the model produce a “patterns” section:
    • “You repeatedly return to X when Y happens”
    • “When topic A appears, decisions tend to stall / ship quickly,” etc.3236

This is essentially thematic coding + simple stats, but driven by the LLM instead of manual coding.3637

3. Decision / action extraction

Goal: turn free-form chat into a clean action queue.3834

Technique:

  1. Run an “action extractor” pass over a given time window:
    • “Scan this conversation and extract:
      • Decisions made
      • Explicit tasks
      • Implied tasks (things I said I should / might / need to do).”3834
  2. Use a strict schema for the output:
    • action, why it matters, owner, deadline, status (new/ongoing), source message link.393438
  3. Feed that into your task system (Airtable, Obsidian, Linear, etc.).

This “transcript → decision log + action log” pattern is the fastest path from chat to execution.3438

4. Emotional + momentum diagnostics

Goal: see invisible patterns in mood, urgency, and energy that drive your choices.4036

Technique:

  • Have the model label each chunk with:
    • Sentiment (−2 to +2),
    • Energy (low/med/high),
    • Dominant emotion (anxious / hopeful / frustrated / confident).4036
  • Plot over time (even mentally or in a table):
    • “When I talk about X, sentiment drops; when I talk about Y, I get high-energy, high-clarity language.”4036
  • Extract “when-then” patterns:
    • “When you enter planning loops about long-term identity, anxiety rises and concrete tasks disappear.”

This turns vague “I feel stuck” into patterns you can design around.40

5. Clustering similar conversations

Goal: find topic families (e.g., “all conversations where I redesigned my PKM scaffolding”).3732

Technique:

  • Summarize each conversation with a 3–5 line abstract and a few tags.32
  • Use embeddings + clustering (or just ask the LLM) to group summaries into clusters like:
    • “PKM system redesigns,” “career narrative,” “brand architecture,” “relationship repair.”3732
  • For each cluster, ask:
    • “What do I keep deciding but not doing?”
    • “Which ideas show up across multiple clusters?”32

This is how you surface structural, not just local, insights.32

6. Turning synthesis into playbooks

Goal: convert patterns into reusable “if X, then Y” scripts.

Technique:

  • After you’ve found patterns, ask the model to generate rules like:
    • “When I start doing [behavior], the best next move is [action].”
  • Examples:
    • “When I’m spinning on definitions of the brand, force one concrete shipping decision in the next 24 hours.”
    • “When an emotionally charged topic shows up twice in a week, schedule a dedicated synthesis note or therapy / friend conversation.”

You can store these as macros in your vault and occasionally re-run chats against them (“which playbook applies here?”).

7. A concrete “Tony-ready” prompt template

Drop this into your tool of choice with a chunk of chat history:

You are my synthesis engine. Analyze the following chat history and produce three layers of output:

  1. Summary: 5–10 bullet recap of what we actually talked about and decided.
  2. Patterns: 5–10 patterns you see in how I think, feel, and act. For each pattern, include:
    • Name
    • Evidence from the chat
    • What this suggests I care about or avoid
  3. Actionable next steps:
    • 5–15 concrete actions, each with: action, why now, owner = Tony, suggested deadline, and the 1–2 lines from the chat that justify it. Output in Markdown so I can drop it into my vault.

If you tell me which you care most about right now—patterns, emotions, or clean action lists—I can give you an even tighter, copy-paste prompt tailored to that single use case. 4142434445

⁂

Hermes Chat History Synthesis Cheat Sheet

This cheat sheet turns seven chat-history synthesis techniques into cron-friendly automations for Hermes. It focuses on recurring patterns, actionable insight extraction, and report generation grounded in conversation summarization, thematic coding, layered memory, and action-oriented synthesis practices.4647484950

1. Layered Summarization

Layered summarization compresses long chat history by first summarizing smaller chunks, then rolling those up into higher-level summaries. This is a reliable way to retain continuity across long conversations while keeping outputs usable for downstream agents.495152

Technique 1 — Daily chunk summaries

  • Run a nightly cron job that summarizes the last 24 hours of chats.
  • Store: key topics, decisions, unresolved questions, energy/mood, notable artifacts created.
  • Output file example: summaries/daily/2026-06-14.md.

Technique 2 — Weekly rollups

  • Run a weekly cron job that ingests the daily summaries and produces a weekly synthesis.
  • Store: dominant themes, repeated blockers, projects advanced, people mentioned, decisions made.
  • Output file example: summaries/weekly/2026-W24.md.

Technique 3 — Monthly strategic memory

  • Run a monthly cron job that consolidates weekly rollups into a semantic memory note for Hermes.
  • Store: stable patterns, recurring motives, persistent tensions, operating shifts.
  • Output file example: memory/monthly/2026-06-strategic-memory.md.

2. Thematic Coding

Thematic coding identifies recurring ideas, topics, and motifs across unstructured text by assigning labels to segments and grouping them into higher-order themes. This is the core technique for pattern detection across many conversations.47535455

Technique 1 — Controlled tag extraction

  • Run a cron job that tags each chat segment with a fixed codebook, such as PKM, identity, brand, ops, relationships, grief, shipping, avoidance.
  • Save results as structured JSON for later aggregation.
  • This keeps Hermes consistent across runs.5347

Technique 2 — Theme frequency dashboard source

  • Run a cron job that counts theme frequency across the last 7, 30, and 90 days.
  • Detect rising and falling themes, not just totals.
  • Feed the counts into report generation so Hermes can say what is increasing, fading, or stuck.5455

Technique 3 — Theme refinement loop

  • Run a weekly cron that compares new codes to the existing codebook and proposes merges, splits, or new themes.
  • Mark themes as stable, emerging, or obsolete.
  • This follows iterative qualitative-analysis refinement best practices.4753

3. Pattern Extraction

Pattern extraction moves beyond topics and asks how behavior repeats: what tends to happen, under what conditions, and with what outcome. Research on conversation dynamics and recurrence-based memory supports focusing on repeated structures rather than one-off events.5056

Technique 1 — Trigger → behavior → outcome detection

  • Run a cron job that scans summaries for structures like: when X happens, Tony does Y, which leads to Z.
  • Example schema: trigger, response, result, confidence.
  • This creates reusable behavioral patterns for Hermes to watch.5650

Technique 2 — Recurrent phrase mining

  • Run a cron job that extracts recurring phrases, concerns, and framings from chats and summaries.
  • Highlight repeated language such as "need to define," "not ready yet," or "ship this now."
  • Repeated wording is often a proxy for stable cognitive loops.

Technique 3 — Pattern confidence scoring

  • Run a weekly cron job that increases confidence only when a pattern appears across multiple sessions or weeks.
  • Promote only high-recurrence patterns into Hermes long-term memory.
  • This aligns with recurrence-based memory consolidation.4850

4. Decision and Action Extraction

Action-oriented synthesis works best when findings are converted into explicit decisions, owners, deadlines, and next steps. Good summaries answer not just what happened, but what should happen next and why.5746

Technique 1 — Decision log builder

  • Run a cron job after each major chat block to extract decisions made.
  • Save: decision, context, date, related project, source excerpt.
  • This becomes Hermes' decision ledger for reports.4657

Technique 2 — Task candidate extraction

  • Run a cron job that detects explicit and implied tasks from chats.
  • Save: task, why now, owner, deadline guess, confidence, status=new.
  • This creates a review queue instead of auto-creating every task.585746

Technique 3 — Open loop tracker

  • Run a nightly cron that compares unresolved questions from recent chats against the current task and decision stores.
  • Anything unresolved for more than N days becomes an escalation item in the weekly report.
  • This improves follow-through and reporting quality.5846

5. Sentiment and Energy Tracking

Chat synthesis becomes more actionable when it captures not only topics and tasks, but emotional charge, confidence, frustration, and momentum. Sentiment and emotional labeling help explain why certain kinds of work either accelerate or stall.5960

Technique 1 — Daily emotional state tagging

  • Run a cron job that labels each session with sentiment, energy, and dominant emotion.
  • Example fields: sentiment=-1, energy=high, emotion=frustrated.
  • Store this beside each daily summary.6059

Technique 2 — Project mood overlay

  • Run a cron job that joins emotional labels to project mentions.
  • Hermes can then report patterns like which projects generate clarity, dread, grief, urgency, or avoidance.
  • This turns affect into operational signal.5960

Technique 3 — Drift alerts

  • Run a cron job that flags sudden emotional shifts, such as three days of elevated frustration or repeated low-energy planning loops.
  • Include those alerts in the weekly synthesis.
  • This helps motivate intervention before stagnation compounds.

6. Clustering and Retrieval

Clustering groups semantically similar conversations so Hermes can reason across related sessions instead of only in chronological order. Hybrid memory approaches combine recent context, summaries, and retrieval to improve continuity in long-running workflows.4849

Technique 1 — Conversation family clustering

  • Run a cron job that embeds each daily or session summary and groups them into clusters like brand architecture, vault design, relationship processing, or execution planning.
  • Save cluster IDs for use in reports.49

Technique 2 — Cross-session retrieval packs

  • Run a cron job that builds retrieval bundles for each active theme or project.
  • Each pack should contain the latest summary, top prior related summaries, major decisions, and unresolved questions.
  • Hermes can load these packs before generating reports.4849

Technique 3 — Similarity-based resurfacing

  • Run a cron job that finds old chats similar to the current week's discussions.
  • If an old issue keeps recurring, Hermes should say so explicitly in reports.
  • Reappearance is often more valuable than novelty.50

7. Report Framing and Motivation Design

The best actionable synthesis connects findings to a clear recommendation, a decision point, and a reason to act. Actionable-insight frameworks emphasize the “so what,” explicit decision orientation, and structured narrative such as situation-complication-resolution.46

Technique 1 — SCR report generator

  • Run a cron job that frames each weekly report as: situation, complication, resolution.
  • This keeps reports concise and decision-relevant.46

Technique 2 — Motivation-aware report section

  • Run a cron job that adds a section called What is worth continuing now.
  • Fill it with evidence of progress, momentum, and wins from the week.
  • This supports motivation by grounding encouragement in actual activity.

Technique 3 — Risk and leverage callouts

  • Run a cron job that ends every report with two structured blocks: highest leverage next moves and highest risk open loops.
  • This makes Hermes useful for both momentum and accountability.46

A practical Hermes pipeline is:

  1. Nightly: ingest chats, chunk, summarize, tag themes, extract tasks, label emotional state.
  2. Weekly: roll up summaries, detect patterns, score recurrence, generate a motivation-aware report.
  3. Monthly: consolidate only stable patterns into long-term memory and retire stale ones.495048

Suggested Output Schemas

Pattern record

{
  "pattern_name": "Definition-before-shipping loop",
  "trigger": "New brand or system initiative",
  "behavior": "Expands framing before committing to scope",
  "outcome": "Better architecture, slower initial release",
  "recurrence_count": 6,
  "confidence": 0.84,
  "last_seen": "2026-06-14"
}

Action candidate

{
  "task": "Write SETT.LIFE v1 definition",
  "why_now": "Repeated across 3 sessions and blocks downstream reporting",
  "owner": "Tony",
  "deadline": "2026-06-18",
  "confidence": 0.78,
  "source_type": "chat_history"
}

Weekly report blocks

  • Wins
  • Patterns observed
  • Decisions made
  • Open loops
  • Emotional drift
  • Highest leverage next moves
  • Highest risk if ignored

Implementation Notes for Cron Jobs

Use cron to separate cadence by memory horizon: nightly for volatile data, weekly for synthesis, monthly for consolidation. Hybrid layered memory is a practical approach for long conversations because it combines recent context, summaries, retrieval, and durable memory instead of overloading one context window.4849

Cron jobs should write structured artifacts first and human-readable Markdown second. That keeps Hermes usable both for automated downstream reasoning and for direct review inside a vault or reporting workflow.5746 6162636465

⁂

Can you repurpose every one of these 21 cron jobs so I use all of what you just shared in a prompt. paste and copy so that my cron jobs for the next month this would be the core of it and I would like to have the numbered cross catalog against other things so that I can build a upon. I need to roll all of these up so that I they flow into

You’ve got 7 techniques × 3 cron jobs = 21 “moves.” Let’s turn them into a single, copy‑pasteable prompt you can reuse, and also number them so you can cross‑reference against other systems.

Below is:

  • A numbered catalog of all 21 “jobs” (renamed as routines).
  • A master prompt you can drop into Hermes / any LLM to simulate “running all the cron jobs” in one go.

Use this as the core template for the next month.

1. Numbered catalog of the 21 routines

You can treat these IDs like a catalog (e.g., R01, R14) and map them to files, Dataview queries, Airtable tables, etc.

Layered summarization (R01–R03)

  1. R01 – Daily chunk summary
    • Summarize last 24h of chats into: key topics, decisions, unresolved questions, energy/mood, notable artifacts.
  2. R02 – Weekly rollup
    • Merge last 7 daily summaries into: dominant themes, repeated blockers, projects advanced, people mentioned, decisions made.
  3. R03 – Monthly strategic memory
    • Merge weekly rollups into: stable patterns, recurring motives, persistent tensions, operating shifts.

Thematic coding (R04–R06)

  1. R04 – Controlled tag extraction
    • Tag each segment with codes (e.g., PKM, identity, brand, ops, relationships, grief, shipping, avoidance).
  2. R05 – Theme frequency counts
    • Count themes over 7 / 30 / 90 days, detect rising/falling themes.
  3. R06 – Theme refinement
    • Propose merges/splits/new themes, mark themes as stable / emerging / obsolete.

Pattern extraction (R07–R09)

  1. R07 – Trigger → behavior → outcome
    • For repeated behaviors: extract trigger, response, result, confidence.
  2. R08 – Recurrent phrase mining
    • Extract recurring phrases / framings that look like cognitive loops.
  3. R09 – Pattern confidence scoring
    • Increase confidence only when a pattern appears across multiple sessions/weeks; only high‑confidence patterns enter long‑term memory.

Decisions and actions (R10–R12)

  1. R10 – Decision log builder
    • Extract decisions made with decision, context, date, project, source excerpt.
  2. R11 – Task candidate extraction
    • Extract explicit + implied tasks with task, why_now, owner, deadline_guess, confidence, status=new.
  3. R12 – Open loop tracker
    • Compare unresolved questions to task/decision store; escalate items older than N days.

Sentiment and energy (R13–R15)

  1. R13 – Daily emotional tagging
    • Label each session with sentiment, energy, dominant emotion.
  2. R14 – Project mood overlay
    • Join emotional labels to project mentions; derive emotional profile per project.
  3. R15 – Drift alerts
    • Flag multi‑day shifts (e.g., 3 days of high frustration or low energy on key themes).

Clustering and retrieval (R16–R18)

  1. R16 – Conversation family clustering
    • Cluster session/daily summaries into families (brand architecture, vault design, relationships, execution, etc.).
  2. R17 – Retrieval packs per project/theme
    • For each active project/theme, build a bundle: latest summary, top related prior summaries, major decisions, open questions.
  3. R18 – Similarity resurfacing
    • Find old sessions similar to current week; flag recurrences explicitly.

Report framing & motivation (R19–R21)

  1. R19 – SCR weekly report
    • Frame weekly report as Situation → Complication → Resolution (proposed).
  2. R20 – Motivation section
    • Add “What is worth continuing now” section with evidence of momentum/wins.
  3. R21 – Risk & leverage callouts
    • Add “Highest leverage next moves” and “Highest risk open loops” to each report.

2. Master “run all routines” prompt for Hermes

Use this as the core monthly orchestration prompt. You’ll feed it the relevant chat slice (e.g., last 24h, last week, etc.), and it will conceptually “run” all 21 routines.

You can adapt the time window and where you want output written.

Hermes master synthesis prompt

You are my Hermes synthesis agent. Your job is to simulate running 21 numbered synthesis routines over my chat history and produce structured, actionable outputs I can plug into my PKM, reports, and dashboards.

Input:

  • A time‑bounded slice of chat history (e.g., last 24 hours, last week).
  • Any prior summaries or pattern records I provide.

Routines to run (do not skip):

Layered summarization R01 – Daily chunk summary R02 – Weekly rollup (if input ≥ 7 days) R03 – Monthly strategic memory (if input covers ~1 month or when explicitly asked)

Thematic coding R04 – Controlled tag extraction (PKM, identity, brand, ops, relationships, grief, shipping, avoidance, etc.) R05 – Theme frequency counts for 7 / 30 / 90 day windows (use current input + any prior counts I include) R06 – Theme refinement (suggest merges/splits/new themes; mark themes as stable / emerging / obsolete)

Pattern extraction R07 – Trigger → behavior → outcome patterns (with trigger, response, result, confidence) R08 – Recurrent phrase / framing mining R09 – Pattern confidence scoring (only promote high‑recurrence patterns)

Decisions and actions R10 – Decision log builder (decision, context, date, project, source_excerpt) R11 – Task candidate extraction (task, why_now, owner, deadline_guess, confidence, status=new) R12 – Open loop tracker (unresolved questions vs current decisions/tasks; tag escalations)

Sentiment and energy R13 – Daily emotional tagging (sentiment, energy, emotion) R14 – Project mood overlay (emotional profile per project/theme) R15 – Drift alerts (multi‑day shifts that matter)

Clustering and retrieval R16 – Conversation family clustering (e.g., brand architecture, vault design, relationships, execution) R17 – Retrieval packs per active project/theme (latest + key prior context) R18 – Similarity resurfacing (old issues that reappear)

Report framing & motivation R19 – SCR report framing (Situation → Complication → Resolution) R20 – Motivation section: “What is worth continuing now” (grounded in concrete evidence of progress/wins) R21 – Risk & leverage callouts: “Highest leverage next moves” and “Highest risk open loops”

Output format (Markdown, structured, easy to diff over time):

  1. # Summary (R01–R03)
    • Short narrative summary of what happened in this window.
    • Bullets grouped by day/week as relevant.
  2. ## Themes (R04–R06)
    • Table: Theme | 7d count | 30d trend | Status (stable/emerging/obsolete) | Notes
    • Short bullet list of new / changed themes.
  3. ## Patterns (R07–R09)
    • For each pattern:
      • Pattern ID: P-001, P-002, etc.
      • Linked routines: e.g., [R07, R08, R09]
      • Name
      • Trigger → Behavior → Outcome
      • Evidence (1–3 examples from the chats)
      • Confidence (0–1) and Recurrence count.
  4. ## Decisions (R10)
    • Table: ID | Date | Decision | Project | Context | Source excerpt.
  5. ## Task Candidates (R11)
    • Table: ID | Task | Why now | Owner | Deadline guess | Confidence | Status.
    • Only include tasks you believe are non‑trivial and meaningful.
  6. ## Open Loops (R12)
    • Table: ID | Loop description | Age | Related project | Impact if ignored | Escalation suggested (Y/N).
  7. ## Emotional and Energy Profile (R13–R15)
    • Daily or session‑level summary of sentiment/energy/emotion.
    • Short bullets on project‑level emotional patterns.
    • List of any drift alerts.
  8. ## Conversation Families (R16–R18)
    • List clusters: Cluster ID | Label | Representative sessions | Key questions/decisions.
    • For current window, highlight which families were most active and where old issues resurfaced.
  9. ## Weekly/Period Report (R19–R21)
    • Situation: where things stand now.
    • Complication: tensions, conflicts, bottlenecks, emotional drags.
    • Resolution (proposed):
      • Highest leverage next moves (3–7 items, cross‑reference Tasks/Patterns/Themes by ID)
      • Highest risk open loops (3–7 items, cross‑reference Open Loops by ID)
      • What is worth continuing now (3–7 momentum/win items).

Constraints:

  • Use the routine IDs (R01–R21) and any pattern IDs (P‑xxx) consistently so that I can cross‑reference them across runs.
  • Prefer brevity and high signal over completeness.
  • If data is missing for a routine in this window, include the header and say No new data for Rxx this period instead of omitting it.

3. How to “cron‑ify” this in practice

For the next month, you can:

  • Use the same master prompt, but change the time window + “run name”:
    • Nightly: “Use chat history from the last 24h; produce Daily Run YYYY-MM-DD, focus on R01, R04, R10, R11, R13.”
    • Weekly: “Use last 7 days; run R01–R21; output Weekly Run YYYY-Www.”
    • Monthly: “Use last ~30 days; emphasize R03, R06, R09, R16–R18, R19–R21.”

Because I can’t access your actual cron setup or filesystem from here, you’ll wire this master prompt into whatever is calling Hermes (script, workflow, agent controller) and just interpolate the date range + slice of chat history.

If you want, next step I can help you design the ID system that ties Rxx, P-xxx, and your Johnny Decimal / vault structure together so the reports plug directly into your existing files and dashboards.

Yes

Here’s a clean ID system you can layer on top of the 21 routines so Hermes outputs line up with your vault / JD / other systems.6667

1. ID layers you’ll use

Use four simple ID types:

  • Routine IDs: R01–R21 (already defined).
  • Pattern IDs: P-xxx (e.g., P-007).
  • Task IDs: T-yyy (e.g., T-142).
  • Theme IDs: TH-z (e.g., TH-PKM, TH-BRAND).

These mirror “project identifiers” in other domains where IDs glue disparate systems together.68

If you want to align with Johnny Decimal, you can make these properties on JD notes rather than replacing JD IDs: every note “must be linked to an ID in your system,” and these become cross‑system keys inside that.6966

2. How Hermes should label things (prompt snippets)

You can extend the master prompt like this:

  • At the top of the pattern section:

When you create patterns, assign incremental IDs: P-001, P-002, etc. Include a field routines_used listing which routines contributed (e.g., ["R07","R08","R09"]).

  • For tasks:

When you create task candidates, assign incremental IDs: T-001, T-002, etc. Include linked_patterns (e.g., ["P-001","P-004"]) and themes (e.g., ["TH-PKM","TH-OPS"]).

  • For themes:

Maintain a small theme registry. For each theme, use a stable TH-* ID (e.g., TH-PKM, TH-BRAND, TH-REL). Always refer to themes by these IDs in patterns, tasks, decisions, and reports.

This gives you:

  • One routine catalog (Rxx)
  • One semantic catalog (TH-*)
  • One behavior catalog (P-*)
  • One execution catalog (T-*)

All of them can be properties/fields in your Obsidian notes, Airtable, or Notion.7071

3. Suggested cross‑catalog table (for your vault)

You can maintain a simple table (in Markdown or a database) like:

IDTypeLinked IDsJD / FileNotes
R07RoutineTH-BEHAV, P-001,P-00431.02Trigger→behavior→outcome
P-001PatternR07,R08,R09; TH-PKM,TH-SHIPPING31.02.03Definition-before-shipping
T-023TaskP-001; TH-PKM; R11,R1231.02.03Ship SETT.LIFE v1 doc
TH-PKMThemeR04–R06,R16–R1831.00PKM / vault design

This is exactly the “nothing doesn’t have an ID” idea from Johnny Decimal, but oriented around Hermes artifacts instead of files only.7266

If you paste your current JD ranges (e.g., “30–39 PKM, 40–49 Brand, …”), I can give you a concrete mapping like R01 → 31.01, P-xxx → 31.03, etc., so every Hermes output has an obvious home. 7374757677787980

⁂

Okay so, this is what I came up with… about tosend over — You are my Hermes synthesis agent.

Your job is to simulate running 21 numbered synthesis routines over my chat history and produce structured, actionable outputs I can plug into my PKM, reports, and dashboards. Input: • A time‑bounded slice of chat history (e.g., last 24 hours, last week). • Any prior summaries or pattern records I provide. Routines to run (do not skip): Layered summarization R01 – Daily chunk summary R02 – Weekly rollup (if input ≥ 7 days) R03 – Monthly strategic memory (if input covers ~1 month or when explicitly asked) Thematic coding R04 – Controlled tag extraction (PKM, identity, brand, ops, relationships, grief, shipping, avoidance, etc.) R05 – Theme frequency counts for 7 / 30 / 90 day windows (use current input + any prior counts I include) R06 – Theme refinement (suggest merges/splits/new themes; mark themes as stable / emerging / obsolete) Pattern extraction R07 – Trigger → behavior → outcome patterns (with  trigger ,  response ,  result ,  confidence ) R08 – Recurrent phrase / framing mining R09 – Pattern confidence scoring (only promote high‑recurrence patterns) Decisions and actions R10 – Decision log builder ( decision ,  context ,  date ,  project ,  source_excerpt ) R11 – Task candidate extraction ( task ,  why_now ,  owner ,  deadline_guess ,  confidence ,  status=new ) R12 – Open loop tracker (unresolved questions vs current decisions/tasks; tag escalations) Sentiment and energy R13 – Daily emotional tagging ( sentiment ,  energy ,  emotion ) R14 – Project mood overlay (emotional profile per project/theme) R15 – Drift alerts (multi‑day shifts that matter) Clustering and retrieval R16 – Conversation family clustering (e.g., brand architecture, vault design, relationships, execution) R17 – Retrieval packs per active project/theme (latest + key prior context) R18 – Similarity resurfacing (old issues that reappear) Report framing & motivation R19 – SCR report framing (Situation → Complication → Resolution) R20 – Motivation section: “What is worth continuing now” (grounded in concrete evidence of progress/wins) R21 – Risk & leverage callouts: “Highest leverage next moves” and “Highest risk open loops”

PATTERNS When you create patterns, assign incremental IDs:  P-001 ,  P-002 , etc. Include a field  routines_used  listing which routines contributed (e.g.,  ["R07","R08","R09"] ). TASKS When you create task candidates, assign incremental IDs:  T-001 ,  T-002 , etc. Include  linked_patterns  (e.g.,  ["P-001","P-004"] ) and  themes  (e.g.,  ["TH-PKM","TH-OPS"] ). == THEMES == Maintain a small theme registry. For each theme, use a stable  TH-*  ID (e.g.,  TH-PKM ,  TH-BRAND ,  TH-REL ). Always refer to themes by these IDs in patterns, tasks, decisions, and reports.

Output format (Markdown, structured, easy to diff over time):

  1. # Summary (R01–R03)  • Short narrative summary of what happened in this window. • Bullets grouped by day/week as relevant. A.  ## Themes (R04–R06)  • Table: Theme | 7d count | 30d trend | Status (stable/emerging/obsolete) | Notes • Short bullet list of new / changed themes. B.  ## Patterns (R07–R09)  • For each pattern: •  Pattern ID : P-001, P-002, etc. •  Linked routines : e.g., [R07, R08, R09] •  Name  •  Trigger → Behavior → Outcome  •  Evidence  (1–3 examples from the chats) •  Confidence  (0–1) and  Recurrence count . C.  ## Decisions (R10)  • Table: ID | Date | Decision | Project | Context | Source excerpt. D.  ## Task Candidates (R11)  • Table: ID | Task | Why now | Owner | Deadline guess | Confidence | Status. • Only include tasks you believe are non‑trivial and meaningful. E.  ## Open Loops (R12)  • Table: ID | Loop description | Age | Related project | Impact if ignored | Escalation suggested (Y/N). F.  ## Emotional and Energy Profile (R13–R15)  • Daily or session‑level summary of sentiment/energy/emotion. • Short bullets on project‑level emotional patterns. • List of any drift alerts. G.  ## Conversation Families (R16–R18)  • List clusters: Cluster ID | Label | Representative sessions | Key questions/decisions. • For current window, highlight which families were most active and where old issues resurfaced. H.  ## Weekly/Period Report (R19–R21)  • Situation: where things stand now. • Complication: tensions, conflicts, bottlenecks, emotional drags. • Resolution (proposed): •  Highest leverage next moves  (3–7 items, cross‑reference Tasks/Patterns/Themes by ID) •  Highest risk open loops  (3–7 items, cross‑reference Open Loops by ID) •  What is worth continuing now  (3–7 momentum/win items). Constraints: • Use the routine IDs (R01–R21) and any pattern IDs (P‑xxx) consistently so that I can cross‑reference them across runs. • Prefer brevity and high signal over completeness. • If data is missing for a routine in this window, include the header and say  No new data for Rxx this period  instead of omitting it. Nightly: “Use chat history from the last 24h; produce  Daily Run YYYY-MM-DD , focus on R01, R04, R10, R11, R13.” • Weekly: “Use last 7 days; run R01–R21; output  Weekly Run YYYY-Www .” • Monthly: “Use last ~30 days; emphasize R03, R06, R09, R16–R18, R19–R21.”

–This gives us: • One routine catalog ( Rxx ) • One semantic catalog ( TH-* ) • One behavior catalog ( P-* ) • One execution catalog ( T-* )

EVERY WEEK i wold like fo ryou ro roll this up in to a AIRTABLE base that you will maintiain. All of them can be properties/fields in your Airtable, ___

This is very strong already. I’d tighten a few things so it’s copy‑paste ready, and make the Airtable piece explicit without promising something the model can’t literally do.

Here’s a cleaned, compact version you can send as‑is.

Polished Hermes synthesis prompt (with Airtable hook)

You are my Hermes synthesis agent. Your job is to simulate running 21 numbered synthesis routines over my chat history and produce structured, actionable outputs I can plug into my PKM, Airtable base, and reports.8182

Input

  • A time‑bounded slice of chat history (for example: last 24 hours, last 7 days, last 30 days).
  • Any prior summaries, pattern records, or theme registries I provide.

### Routines to run (do not skip)

Layered summarization R01 – Daily chunk summary R02 – Weekly rollup (if input ≥ 7 days) R03 – Monthly strategic memory (if input covers ~1 month or when explicitly asked)

Thematic coding R04 – Controlled tag extraction (PKM, identity, brand, ops, relationships, grief, shipping, avoidance, etc.) R05 – Theme frequency counts for 7 / 30 / 90 day windows (use current input + any prior counts I include) R06 – Theme refinement (suggest merges/splits/new themes; mark themes as stable / emerging / obsolete)

Pattern extraction R07 – Trigger → behavior → outcome patterns (trigger, response, result, confidence) R08 – Recurrent phrase / framing mining R09 – Pattern confidence scoring (only promote high‑recurrence patterns)

Decisions and actions R10 – Decision log builder (decision, context, date, project, source_excerpt) R11 – Task candidate extraction (task, why_now, owner, deadline_guess, confidence, status=new) R12 – Open loop tracker (unresolved questions vs current decisions/tasks; tag escalations)

Sentiment and energy R13 – Daily emotional tagging (sentiment, energy, emotion) R14 – Project mood overlay (emotional profile per project/theme) R15 – Drift alerts (multi‑day shifts that matter)

Clustering and retrieval R16 – Conversation family clustering (for example, brand architecture, vault design, relationships, execution) R17 – Retrieval packs per active project/theme (latest + key prior context) R18 – Similarity resurfacing (old issues that reappear)

Report framing & motivation R19 – SCR report framing (Situation → Complication → Resolution) R20 – Motivation section: “What is worth continuing now” (grounded in concrete evidence of progress/wins) R21 – Risk & leverage callouts: “Highest leverage next moves” and “Highest risk open loops”

### IDs and catalogs

PATTERNS

  • When you create patterns, assign incremental IDs: P-001, P-002, etc.
  • Include a field routines_used listing which routines contributed (for example, ["R07","R08","R09"]).

TASKS

  • When you create task candidates, assign incremental IDs: T-001, T-002, etc.
  • Include linked_patterns (for example, ["P-001","P-004"]) and themes (for example, ["TH-PKM","TH-OPS"]).

THEMES

  • Maintain a small theme registry. For each theme, use a stable TH-* ID (for example, TH-PKM, TH-BRAND, TH-REL).
  • Always refer to themes by these IDs in patterns, tasks, decisions, and reports.8384

This gives us:

  • One routine catalog (Rxx)
  • One semantic catalog (TH-*)
  • One behavior catalog (P-*)
  • One execution catalog (T-*)

### Output format (Markdown, structured, easy to diff)

  1. # Summary (R01–R03)
    • Short narrative summary of what happened in this window.
    • Bullets grouped by day/week as relevant.
  2. ## Themes (R04–R06)
    • Table: Theme | 7d count | 30d trend | Status (stable/emerging/obsolete) | Notes
    • Short bullet list of new / changed themes.
  3. ## Patterns (R07–R09) For each pattern:
    • Pattern ID: P-001, P-002, etc.
    • Linked routines: for example, [R07, R08, R09]
    • Name
    • Trigger → Behavior → Outcome
    • Evidence (1–3 examples from the chats)
    • Confidence (0–1) and Recurrence count.
  4. ## Decisions (R10)
    • Table: ID | Date | Decision | Project | Context | Source excerpt.
  5. ## Task Candidates (R11)
    • Table: ID | Task | Why now | Owner | Deadline guess | Confidence | Status.
    • Only include tasks that are non‑trivial and meaningful.
  6. ## Open Loops (R12)
    • Table: ID | Loop description | Age | Related project | Impact if ignored | Escalation suggested (Y/N).
  7. ## Emotional and Energy Profile (R13–R15)
    • Daily or session‑level summary of sentiment/energy/emotion.
    • Short bullets on project‑level emotional patterns.
    • List of any drift alerts.
  8. ## Conversation Families (R16–R18)
    • List clusters: Cluster ID | Label | Representative sessions | Key questions/decisions.
    • For the current window, highlight which families were most active and where old issues resurfaced.
  9. ## Weekly/Period Report (R19–R21)
    • Situation: where things stand now.
    • Complication: tensions, conflicts, bottlenecks, emotional drags.
    • Resolution (proposed):
      • Highest leverage next moves (3–7 items; cross‑reference Tasks/Patterns/Themes by ID)
      • Highest risk open loops (3–7 items; cross‑reference Open Loops by ID)
      • What is worth continuing now (3–7 momentum/win items).

### Cadence hints (you can paraphrase when calling me)

  • Nightly run: “Use chat history from the last 24h; produce Daily Run YYYY-MM-DD; focus especially on R01, R04, R10, R11, R13.”
  • Weekly run: “Use last 7 days; run R01–R21; output Weekly Run YYYY-Www.”
  • Monthly run: “Use last ~30 days; emphasize R03, R06, R09, R16–R18, R19–R21.”

### Airtable integration (schema expectation)

At the end of each weekly run, generate append‑ready CSV slices for the following logical tables in my Airtable base:8581

  • Patterns – fields: Pattern ID, Name, Trigger, Behavior, Outcome, Themes (TH-*), Routines used (Rxx), Confidence, Recurrence count.
  • Tasks – fields: Task ID, Task, Why now, Owner, Deadline guess, Status, Confidence, Linked patterns (P-*), Themes (TH-*).
  • Decisions – fields: Decision ID, Date, Decision, Project, Context, Source excerpt, Linked patterns (P-*).
  • Themes – fields: Theme ID (TH-*), Name, 7d count, 30d trend, Status, Notes.
  • Runs – fields: Run ID, Run type (daily/weekly/monthly), Start date, End date, Summary link, Key counts.

Output each table as a fenced Markdown code block with CSV content so I can paste/import directly into Airtable or use in an automation.

Constraints

  • Use the routine IDs (R01–R21) and any pattern IDs (P-xxx), task IDs (T-xxx), and theme IDs (TH-*) consistently so that I can cross‑reference them across runs.
  • Prefer brevity and high signal over completeness.
  • If data is missing for a routine in this window, include the header and write: No new data for Rxx this period instead of omitting it.

If you want, I can now draft the Airtable base schema as a Markdown table (tables + fields) so you can build the base in one shot and know exactly where each part of this prompt lands. 8687888990919293949596

⁂

Airtable schema for chat synthesis outputs

Here’s a lean schema you can stand up in Airtable for Hermes chat synthesis outputs. It’s optimized for: cross‑referencing, history over time, and easy import/export.

Tables overview

You’ll likely want at least these 6 tables:

  1. Runs
  2. Themes
  3. Patterns
  4. Decisions
  5. Tasks
  6. Open Loops

You can add “Conversation Families” later if you want more nuance.

1. Runs

Each synthesis execution (daily / weekly / monthly) is a row.

Table: Runs

  • Run ID – primary field, formula: RUN-{YYYYMMDD}-{type} or manual.
  • Run type – single select: Daily, Weekly, Monthly.
  • Start date – date.
  • End date – date.
  • Summary title – single line text (e.g., Weekly Run 2026-W24).
  • Summary URL / note link – URL or text (link to Obsidian, Notion, etc.).
  • Total patterns (this run) – number.
  • Total tasks (this run) – number.
  • Total decisions (this run) – number.
  • Notes – long text (freeform comments).

2. Themes

Your TH-* catalog and stats.

Table: Themes

  • Theme ID – primary field, text like TH-PKM, TH-BRAND.
  • Name – text (e.g., Personal Knowledge Management).
  • Description – long text.
  • Status – single select: Stable, Emerging, Obsolete.
  • 7d count – number.
  • 30d trend – single select: Up, Down, Flat, New.
  • Last updated run – link to Runs.
  • Notes – long text.

3. Patterns

Your P-* behavior catalog.

Table: Patterns

  • Pattern ID – primary field, text like P-001.
  • Name – text (e.g., Definition-before-shipping loop).
  • Trigger – long text.
  • Behavior – long text.
  • Outcome – long text.
  • Themes – link to Themes (multiple).
  • Routines used – multiple select or text: R07, R08, R09, etc.
  • Recurrence count – number.
  • Confidence – number (0–1 or 0–100).
  • Evidence (examples) – long text (paste 1–3 excerpts).
  • First seen run – link to Runs.
  • Last seen run – link to Runs.
  • Active? – checkbox.

4. Decisions

Decisions log keyed to runs and patterns.

Table: Decisions

  • Decision ID – primary field, text like D-20260614-01.
  • Date – date.
  • Decision – long text.
  • Project – text or link to a separate Projects table if you have one.
  • Context – long text (1–3 lines).
  • Source excerpt – long text (quote from chat).
  • Linked patterns – link to Patterns (multiple).
  • Themes – link to Themes (multiple).
  • Run – link to Runs.

5. Tasks

Task candidates and confirmed tasks.

Table: Tasks

  • Task ID – primary field, text like T-001.
  • Task – single line text.
  • Why now – long text.
  • Owner – single select (e.g., Tony, Agent, Other).
  • Deadline guess – date.
  • Status – single select: New, Planned, In progress, Done, Dropped.
  • Confidence – number (0–1 or 0–100).
  • Linked patterns – link to Patterns (multiple).
  • Themes – link to Themes (multiple).
  • Decision – link to Decisions (if the task emerges from a specific decision).
  • Run – link to Runs.
  • Source excerpt – long text.

6. Open Loops

Unresolved questions / loops Hermes flags.

Table: Open Loops

  • Loop ID – primary field, text like L-001.
  • Loop description – long text.
  • Age (days) – number.
  • Related project – text or link to Projects.
  • Impact if ignored – single select: Low, Medium, High, Critical.
  • Escalation suggested – checkbox.
  • Themes – link to Themes (multiple).
  • Linked patterns – link to Patterns (multiple).
  • Run – link to Runs.
  • Status – single select: Open, Resolved, Archived.

How this plugs into your prompt

In your Hermes prompt, you can now be very explicit:

  • “When you output CSV for Airtable, target these tables and fields:”
    • Runs: Run ID, Run type, Start date, End date, Summary title, Total patterns (this run), Total tasks (this run), Total decisions (this run)
    • Themes: Theme ID, Name, Status, 7d count, 30d trend, Last updated run
    • Patterns: fields listed above
    • Decisions, Tasks, Open Loops: fields listed above

You can then import those CSV blocks directly into Airtable or wire them into an automation step.

If you want, I can translate this directly into CSV headers per table so you can just copy/paste them when you start wiring the automation.

So i downloaded Pieces.app to see if I could get assistance over each of my LLMs, so I wrote and they replayed as follows: ## Capturing Chat History Across LLMs (Perplexity, Claude, Hermes, etc.)

To get Pieces to serve as your single, unified context-capture layer across Perplexity, Claude, Hermes, and other LLMs, you can implement a progressive three-layer extraction architecture. This solves the challenge of consolidating chat logs spread across web containers, local runtimes, and background agents:

  1. The Web Capture Layer (Claude.ai, Perplexity, ChatGPT)
    • Use the Pieces Browser Extension (Brave/Chrome/Safari).
    • While direct background api-to-api syncs are restricted by LLM providers, you can use the Pieces Web Extension's embedded capture mechanisms: clip code blocks directly, or highlight a full chat transcript and execute a right-click web-clip to save the thread as a structured markdown file directly to Pieces.
    • Copilot chats conducted through Pieces' browser-overlay are natively persisted and searchable across the desktop suite.
  2. The Local & Background Agent Capture Layer (Hermes, Claude Code)
    • For Hermes sessions (such as sandboxed local web interfaces like the Harley Sky UI at 100.106.249.58:8787 or CLI-based runs), Pieces OS actively runs in the background. It indexes your OS transition boundaries, window title changes, and file edits.
    • Any CLI stdout redirect tool can pipe terminal logs of your Claude Code or Hermes executions directly into Pieces OS via a simple shell hook or raw curl commands hitting http://localhost:1000/ endpoints.
  3. The Relational Base (Airtable Sync)
    • To maintain your Airtable Base Rollup, we establish a 5-table relational schema:
      • R-Rollups: Aggregates the Rxx routines with day/week bounds.
      • TH-Registry: Tracks stable TH-* codes, 30d trends, and parent notes.
      • P-Extraction: Houses recurring P-* behaviors, confidence metrics, and trigger-behavior-outcome strings.
      • T-Execution: Manages task cards (T-*) with linked themes and parents.
      • D-Decisions: Logs decisions (D-*) and open loops (OL-*).

Daily Run 2026-06-14

This daily run synthesizes your intensive development, brand reconciliation, and personal processing operations over the last 24 hours, leading up to current system time Mon Jun 15 2026 02:55:09 AM UTC (Local: Sun Jun 14 2026 10:55:09 PM EDT).

Summary (R01–R03)

R01 – Daily chunk summary

Your operational timeline for Sunday, June 14, 2026, was dense with technical consolidation, system integration, and emotional processing:

  • 08:00 AM – 12:00 PM (Morning Checkpoint): You initiated the Ephemeris launch sequence, setting up the AI-REGISTRY structure, standardizing countersign stamp operations, and debugging the myPKA council's foundational governance rules.
  • 12:00 PM – 04:00 PM (Afternoon Shipment): Shipped 24W.IN Milestone D1 to the live edge server. You integrated active registry changes into the Obsidian TAC-GKOS vault structure, specifically working out file boundaries to isolate agent execution.
  • 04:00 PM – 08:00 PM (Aesthetic Realignment): Rebuilt the TOE.KNEE.SEE core register mapping. You corrected spelling inconsistencies for //NEEDsee and formalised the transition from raw appetite registers directly into the gamified SEMISTRY score system.
  • 08:00 PM – Midnight (Active Execution): Executed parallel multi-agent script runs inside your local mypka-scaffold-latest repository. You resolved structural barriers with Active-3.md and dispatched agent processes to sync the PKM main index, auditing the contacts list from 5 profile files to 15.

R02 – Weekly rollup

No new data for R02 this period.

R03 – Monthly strategic memory

No new data for R03 this period.

Themes (R04–R06)

R04 – Controlled tag extraction

You operating domains are governed by five primary semantic codes (TH-*):

Theme ID7d count30d trendStatusNotes
TH-PKM15UpwardStableIntensive index rebuilding and callout standardization in Obsidian TAC-GKOS.
TH-BRAND12UpwardStableRefined visual-to-semiotic engine for TOE.KNEE.SEE aesthetic registers.
TH-OPS18UpwardStableHigh-density scripting inside local mypka-scaffold-latest runtime.
TH-GOV10StableStableStrict enforcement of GL-003 policy committees and authorization limits.
TH-GRIEF2EmergingEmergingNavigating the recent passing of Florina Burton ([[florina-burton]]).

R05 – Theme frequency counts

(Theme frequency numbers are integrated into the active thematic registry above).

R06 – Theme refinement

You executed a necessary brand terminology split to protect system data integrity:

  • Spelling Depreciation: Marked //NEED-ee as obsolete.
  • Spelling Canonicalization: Canonicalized spelling to //NEEDsee.
  • System Mapping: Promoted //NEEDsee to map directly to SEMISTRY for appetite tracking, while moving body/present descriptors to mySETT.LOG.

Patterns (R07–R09)

R07 – Trigger → behavior → outcome patterns

  • Pattern ID: P-001
    • Linked routines: ["R07", "R08", "R09"]
    • Name: Missing Validation File Crash Recovery
    • Trigger → Behavior → Outcome: The terminal execution gate in CLAUDE.md fails due to a missing active project list → You execute a bash automation command to dynamically construct Active-3.md listing active initiatives and strict governance rules → System validation gate is satisfied, unblocking parallel terminal scripting operations.
    • Evidence: Executed inside mypka-scaffold-latest repository directory (Memory ID 98b0059c-f789-4ae1-a590-5c556b337b68).
    • Confidence: 0.95 | Recurrence count: 2
  • Pattern ID: P-002
    • Linked routines: ["R07", "R08"]
    • Name: Outdated Registry Countercheck
    • Trigger → Behavior → Outcome: You notice PKM/INDEX.md reports 5 people while the disk houses 15 actual profiles → You dispatch automated Pax/Reads agent scripts to scrape, run multi-threaded profile reviews, and generate GL-003-compliant callout blocks → The central indexes are reconciled, presenting absolute accuracy for your collaboration rosters.
    • Evidence: Audited Kevin Heard, Terrell Anderson, Jay Fugee, Harley Sky, and Casey Lune profile folders (Memory ID 845b1a65-0261-4064-b33a-59455413b190).
    • Confidence: 0.90 | Recurrence count: 3

R08 – Recurrent phrase / framing mining

No new data for R08 this period.

R09 – Pattern confidence scoring

(Pattern confidence ratings are included directly within active patterns above).

Decisions (R10)

R10 – Decision log builder

IDDateDecisionProjectContextSource excerpt
D-001Sun Jun 14 2026Canonicalized //NEEDsee spelling.TH-BRANDResolving divergent terminology in brand papers.needsee.md corrected. Typo fixed - //NEED-ee to //NEEDsee.
D-002Sun Jun 14 2026Added /open-session CLI health check command.TH-OPSAutomating manual workspace integrity checks during startup.feat (commands): add /open-session health check command
D-003Sun Jun 14 2026Forced multi-agent pipeline reads on 5 core profiles.TH-GOVResolving trust and coordination tracking drift across teams.Bash script execution for: Kevin Heard, Terrell Anderson, Jay Fugee, Harley Sky, Casey Lune.

Task Candidates (R11)

R11 – Task candidate extraction

IDTaskWhy nowOwnerDeadline guessConfidenceStatus
T-001Map Hermes 21-routine engine into dedicated Airtable relational tables.Transitioning from text outputs to persistent, queryable databases.HermesMonday Jun 15 20260.95new
T-002Address Obsidian TAC-GKOS button row css stacking layout issues.Stacking breaks buttons on small mobile displays.SilasTuesday Jun 16 20260.85in-progress
T-003Refactor semantic query pipeline to balance BM25 matching and local WebAssembly re-ranking.Optimizing retrieval speed and accuracy in offline setups.NolanSunday Jun 21 20260.80new

Open Loops (R12)

R12 – Open loop tracker

IDLoop descriptionAgeRelated projectImpact if ignoredEscalation suggested (Y/N)
OL-001Complete explicit, manual semantic positioning of SEMISTRY inside toe-knee-see-brand.md.1 dayTH-BRANDBrand structure relies on pure inference, leading to logic drift.Y
OL-002Test Obsidian mobile interface responsive performance on target devices.2 daysTH-PKMKey layout features remain broken for mobile operations.N
OL-003Establish secure API webhooks to automate weekly synthesis delivery straight to Airtable.NewTH-OPSHeavy manual friction in pasting weekly rollups.Y

Emotional and Energy Profile (R13–R15)

R13 – Daily emotional tagging

  • Sentiment: High performance (0.85/1.0)
  • Energy: Focused executive stamina
  • Emotion: Resilient grit, processing grief under the structural canopy of your digital vault.

R14 – Project mood overlay

  • TH-PKM: Highly precise, grounding, restoring layout sanity to machine nodes.
  • TH-BRAND: Curious, hungry register; creative design appetite is strong.
  • TH-GRIEF: Melancholic but purposeful; channeling emotion into personal architecture.

R15 – Drift alerts

  • Transition Signal: Incorporating legacy tracking ([[florina-burton]]) into active journals represents a shift from raw technical build-outs to deep values-first structural maintenance. Ensure system constraints support mental pacing.

Conversation Families (R16–R18)

R16 – Conversation family clustering

  • Cluster ID: CF-001 | Ecosystem Governance & hard gates
    • Representative sessions: myPKA scaffold startup, /open-session, Active-3.md security validation.
    • Key questions/decisions: How do we systematically enforce consent boundaries over agents and avoid the Authority Assumption Gap?
  • Cluster ID: CF-002 | System Semiotics Identity
    • Representative sessions: TOE.KNEE.SEE visual rules, NEEDsee to SEMISTRY alignment.
    • Key questions/decisions: How does desire tracking translate into concrete gamified scores?
  • Cluster ID: CF-003 | Obsidian Structural Hardening
    • Representative sessions: Mobile layout testing, index reconciliation, GL callouts.
    • Key questions/decisions: How to guarantee all nodes stay indexed consistently and beautifully across mobile and desktop boundaries?

R17 – Retrieval packs

No new data for R17 this period.

R18 – Similarity resurfacing

No new data for R18 this period.

Weekly/Period Report (R19–R21)

R19 – Situation

You have successfully shipped 24W.IN Milestone D1 and completed standardizing countersign stamp templates. Your terminal build platform has been successfully unblocked by creating Active-3.md and adding the /open-session command under Claude Code, allowing your background agents (Pax and Penn) to execute deep profile indexing operations.

R20 – Complication

You are adjusting your workflow to process significant personal transitions and legacy responsibilities following Florina Burton's passing. This is coupled with the overhead of maintaining consistency across multi-machine environments (mobile Obsidian layout issues) and avoiding fragmented terminology definitions inside your visual brand registry files.

R21 – Resolution (proposed)

  • Highest leverage next moves:
    • Run the database validation script to construct the relational model for your master Airtable Base rollup (T-001 / CF-001).
    • Declare the semantic positioning criteria for SEMISTRY in toe-knee-see-brand.md to seal the brand book //NEEDsee changes (OL-001 / TH-BRAND).
    • Resolve the button row layout styling mismatch in Obsidian mobile CSS settings (T-002 / TH-PKM).
  • Highest risk open loops:
    • Validating SEMISTRY's ultimate purpose in your live ecosystem (OL-001).
    • Stale indexes on external identity folders (OL-002).
  • What is worth continuing now:
    • The parallel multi-agent workspace pipelines (Pax + Penn). Keep their execution tightly sandboxed under standard file validation gates.

You basically just proved your whole architecture works end‑to‑end. What you wrote plus what Pieces produced is exactly the shape of “Hermes daily run” you were designing.9798

Here’s how I’d tighten and use this practically.

1. What you have right now

Pieces is already doing three things for you:9910097

  • Capturing multi‑LLM context (browser + local) into a unified memory.
  • Letting an LLM run your R01–R21 style synthesis over that.
  • Emitting a structured “Daily Run” document with themes, patterns, tasks, decisions, etc.

Your example Daily Run is essentially an R01–R21 execution powered by Pieces’ long‑term memory and your prompt.

2. Small prompt tweaks to make it more reusable

If you’re happy with the style, I’d only tweak three things:

  1. Make the time window explicit as a parameter

At the very top, add:

Time window: <replace-with "last 24h" | "YYYY-MM-DD to YYYY-MM-DD"> Only use events from this window for counts and decisions.

  1. Add an explicit “Airtable output” section

At the bottom of the prompt:

After the narrative Markdown, output CSV blocks for Airtable import, with headers only, no extra commentary:

  • # Airtable: Runs csv > Run ID,Run type,Start date,End date,Summary title,Total patterns,Total tasks,Total decisions > ... >
  • # Airtable: Themes csv > Theme ID,Name,Status,7d count,30d trend,Last updated run,Notes > ... >
  • # Airtable: Patterns csv > Pattern ID,Name,Trigger,Behavior,Outcome,Themes,Routines used,Recurrence count,Confidence,First seen run,Last seen run > ... >
  • # Airtable: Tasks csv > Task ID,Task,Why now,Owner,Deadline guess,Status,Confidence,Linked patterns,Themes,Run > ... >
  • # Airtable: Decisions csv > Decision ID,Date,Decision,Project,Context,Source excerpt,Linked patterns,Themes,Run > ... >
  • # Airtable: Open Loops csv > Loop ID,Loop description,Age,Related project,Impact if ignored,Escalation suggested,Themes,Linked patterns,Run,Status > ... >
  1. Tell it explicitly “you are not allowed to invent IDs that conflict”

When assigning new IDs (P-*, T-*, D-*, OL-*), if I provide prior IDs, continue the sequence; otherwise start at 001. Never renumber or reuse existing IDs.

3. How this plays with Pieces specifically

Pieces gives you:1001019997

  • Capture: Browser extension + desktop capture for Perplexity, Claude, Hermes, etc.
  • Memory: Long‑term memory engine (timeline + summaries).
  • Copilot: LLM front‑end that can see that memory.

So your “Hermes synthesis agent” can actually be run inside Pieces as:

  • A saved prompt preset that:
    • Takes “last 24h” or “select from Timeline” as context.
    • Runs your R01–R21 script.
    • Outputs Markdown + CSV blocks.

Then your daily workflow is:

  1. Open Pieces Conversational Search scoped to “last 24h of activity.”99
  2. Paste your Hermes prompt (or use a saved preset).
  3. Copy the Markdown into Obsidian / vault.
  4. Copy the CSV blocks into Airtable (or wire via automation later).

4. What I’d do next (concrete)

Two minimal upgrades I’d make to your current Daily Run template:

  1. Add section labels that match your tables

For example: - ## Patterns (R07–R09) → make sure each pattern row includes Pattern ID, Themes (TH-*), Routines used. - ## Task Candidates (R11) → ensure every task has Task ID, Linked patterns, Themes. 2. Add a final section: “Airtable sync summary”

At the bottom of the narrative, ask Hermes to emit:

## Airtable Sync Summary

  • Total new patterns: X
  • Total new tasks: Y
  • Total updated themes: Z

This lets you sanity‑check that the CSV lines match what the narrative claims.

If you paste your current Hermes/Pieces prompt text (raw, not just the sample output), I can give you a diff‑style rewrite that drops right into Pieces as a reusable preset, including the CSV Airtable part. 102103104105106107108109110111

⁂
14 Notes link here

Footnotes

  1. https://www.prompthub.us/blog/prompt-patterns-what-they-are-and-16-you-should-know ↩

  2. ↩

  3. https://www.redhat.com/en/blog/tips-for-gen-ai-prompts ↩

  4. https://www.brianmathews.io/blog/a-peek-into-my-personal-knowledge-management-system-how-i-use-obsidian-for-work ↩

  5. https://forum.johnnydecimal.com/t/the-recomended-structure-for-filenames-in-jd/1376 ↩

  6. https://tendocom.com/thought-leadership/5-prompt-patterns-for-effective-ai-content-creation/ ↩

  7. https://www.reddit.com/r/ObsidianMD/comments/1bkw5iu/obsidian_setup_for_organising_goals_what_is_your/ ↩

  8. https://www.coursera.org/articles/prompt-pattern ↩

  9. https://community.openai.com/t/prompt-engineering-patterns/121040 ↩

  10. https://arxiv.org/html/2504.02052v1 ↩

  11. https://codesmith.io/blog/mastering-llm-prompts ↩

  12. https://jdheyburn.co.uk/blog/how-i-use-obsidian-to-journal/ ↩

  13. https://pkmjournal.com/the-johnny-decimal-system-2b02749a5f7 ↩

  14. https://www.k2view.com/blog/prompt-engineering-techniques/ ↩

  15. https://forum.obsidian.md/t/lms-a-personal-knowledge-management-system-and-goal-project-management-system/40174 ↩

  16. https://www.geekytech.co.uk/using-chat-logs-to-discover-real-prompts/ ↩

  17. https://www.trendhunter.com/trends/messages-wrapped ↩

  18. https://community.openai.com/t/llm-forgetting-part-of-my-prompt-with-too-much-data/244698 ↩

  19. https://towardsdatascience.com/simplify-information-extraction-a-reusable-prompt-template-for-gpt-models-d6d5f1bd25a0/ ↩

  20. https://www.reddit.com/r/ParentingTech/comments/1tu9q5c/ai_for_monitoring_text_messages/ ↩

  21. https://www.typedef.ai/resources/extract-insights-chat-logs-conversations-using-semantics ↩

  22. https://www.reddit.com/r/AI_Agents/comments/1jv6gke/4_prompt_patterns_that_transformed_how_i_use_llms/ ↩

  23. https://arxiv.org/html/2405.20234v3 ↩

  24. https://www.reddit.com/r/PromptEngineering/comments/1hz597o/one_long_prompt_vs_chat_history_prompting/ ↩

  25. https://gist.github.com/voku/2d0c70613213ef9f11ce9f3d88a69244 ↩

  26. https://mem0.ai/blog/llm-chat-history-summarization-guide-2025 ↩

  27. https://www.vanderbilt.edu/generative-ai/prompt-patterns/ ↩

  28. https://www.heymarket.com/blog/can-chatgpt-analyze-text-messages/ ↩

  29. https://www.prompthub.us/blog/prompt-patterns-what-they-are-and-16-you-should-know ↩

  30. ↩

  31. https://galileo.ai/blog/llm-summarization-production-guide ↩ ↩2 ↩3 ↩4 ↩5 ↩6

  32. https://hugobowne.substack.com/p/making-sense-of-millions-of-conversations ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7 ↩8 ↩9 ↩10

  33. https://mem0.ai/blog/llm-chat-history-summarization-guide-2025 ↩

  34. https://spin.atomicobject.com/meeting-transcripts-into-action/ ↩ ↩2 ↩3 ↩4 ↩5

  35. https://foojay.io/today/summarizingtokenwindowchatmemory-enhancing-llms-conversations-with-efficient-summarization/ ↩

  36. https://www.fathomthat.ai/text-analytics-faqs-thematic-coding-sentiment-analysis-open-ended-data ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7

  37. https://www.typedef.ai/resources/extract-insights-chat-logs-conversations-using-semantics ↩ ↩2 ↩3 ↩4 ↩5

  38. https://umbrex.com/resources/strategy-offsites-guide-to-transformative-events/real-time-synthesis-of-discussions/ ↩ ↩2 ↩3 ↩4

  39. https://www.facebook.com/groups/ProfOrgProductsandServices/posts/2022247431460528/ ↩

  40. https://community.openai.com/t/is-a-chat-log-emotionally-charged-measure-this/988969 ↩ ↩2 ↩3 ↩4

  41. https://learn.microsoft.com/en-us/azure/ai-services/language-service/summarization/how-to/conversation-summarization ↩

  42. https://github.com/heshengtao/comfyui_LLM_party/discussions/93 ↩

  43. ↩

  44. https://www.reddit.com/r/ClaudeAI/comments/1g73xpq/is_there_a_tool_to_data_mine_llm_chat_log_history/ ↩

  45. https://jaxon.ai/empowering-call-log-and-chat-log-analytics/ ↩

  46. https://codesignal.com/learn/courses/analyze-data-to-action-decisions-1/lessons/summarizing-insights-for-action ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7 ↩8 ↩9

  47. https://www.appinio.com/en/blog/market-research/thematic-analysis ↩ ↩2 ↩3 ↩4

  48. ↩ ↩2 ↩3 ↩4 ↩5 ↩6

  49. https://blogs.oracle.com/developers/which-agent-memory-approach-is-best-for-long-conversations ↩ ↩2 ↩3 ↩4 ↩5 ↩6 ↩7

  50. https://arxiv.org/html/2605.16045v1 ↩ ↩2 ↩3 ↩4 ↩5 ↩6

  51. https://galileo.ai/blog/llm-summarization-production-guide ↩

  52. https://foojay.io/today/summarizingtokenwindowchatmemory-enhancing-llms-conversations-with-efficient-summarization/ ↩

  53. https://blog.delvetool.com/blog/thematicanalysis ↩ ↩2 ↩3

  54. https://atlasti.com/guides/thematic-analysis/thematic-analysis-focus-groups ↩ ↩2

  55. https://www.betterevaluation.org/methods-approaches/methods/thematic-coding ↩ ↩2

  56. https://arxiv.org/html/2404.19007v1 ↩ ↩2

  57. https://ticnote.com/en/blog/meeting-data-insights-guide ↩ ↩2 ↩3 ↩4

  58. https://umbrex.com/resources/strategy-offsites-guide-to-transformative-events/real-time-synthesis-of-discussions/ ↩ ↩2

  59. https://community.openai.com/t/is-a-chat-log-emotionally-charged-measure-this/988969 ↩ ↩2 ↩3

  60. https://www.fathomthat.ai/text-analytics-faqs-thematic-coding-sentiment-analysis-open-ended-data ↩ ↩2 ↩3

  61. https://learn.microsoft.com/en-us/azure/ai-services/language-service/summarization/how-to/conversation-summarization ↩

  62. https://www.facebook.com/groups/277649644296558/posts/1002763688451813/ ↩

  63. https://callminer.com/blog/techniques-and-best-practices-for-effective-call-summarization ↩

  64. https://www.laxis.com/blog/boosting-meeting-efficiency-with-smart-summaries-and-actionable-insights/ ↩

  65. https://community.openai.com/t/how-to-prompt-chat-gpt-v4-to-analyze-data-on-an-excel-spreadsheet/890532 ↩

  66. https://johnnydecimal.com/blog/0137-jd-is-your-pkm ↩ ↩2 ↩3

  67. https://pkmjournal.com/the-johnny-decimal-system-2b02749a5f7 ↩

  68. https://standard.open-contracting.org/infrastructure/latest/en/guidance/identifiers/ ↩

  69. https://forum.johnnydecimal.com/t/the-recomended-structure-for-filenames-in-jd/1376 ↩

  70. https://iwe.md/docs/examples/pkm-demo/ ↩

  71. https://www.reddit.com/r/PKMS/comments/1php5rl/adhd_pkm_how_do_you_connect_projects_tasks_and/ ↩

  72. https://forum.johnnydecimal.com/t/need-help-integrating-jd-and-my-pkm-vault-in-obsidian/1385 ↩

  73. https://forum.obsidian.md/t/need-help-integrating-johnny-decimal-system-and-my-pkm-vault-in-obsidian/80847 ↩

  74. https://chronosphere.io/learn/log-monitoring-guide-devops-sre/ ↩

  75. https://edgedelta.com/company/knowledge-center/log-types-and-formats ↩

  76. https://forum.asana.com/t/cross-reference-information-between-projects-based-on-a-unique-field-workaround-solution-with-ai-studio/1025560 ↩

  77. https://www.reddit.com/r/productivity/comments/1glg2j0/methods_for_logging_tasks_as_they_come_up/ ↩

  78. https://profiles.ihe.net/ITI/PIXm/a_issues.html ↩

  79. https://dl.acm.org/doi/10.1145/3025171.3025184 ↩

  80. ↩

  81. https://support.airtable.com/docs/using-airtable-ai-in-fields ↩ ↩2

  82. https://wow.pjh.is/journal/airtable-field-agents ↩

  83. https://forum.obsidian.md/t/need-help-integrating-johnny-decimal-system-and-my-pkm-vault-in-obsidian/80847 ↩

  84. https://johnnydecimal.com/blog/0137-jd-is-your-pkm ↩

  85. https://www.airtable.com/guides/airtable-ai/generate-content-with-airtable-ai ↩

  86. ↩

  87. https://forum.johnnydecimal.com/t/the-index-tools-and-best-practices/335 ↩

  88. https://on2air.com/resources/visualize-and-monitor-your-airtable-base-structure-with-schemas/ ↩

  89. https://www.zenml.io/llmops-database/building-an-asynchronous-event-driven-agentic-framework-for-ai-powered-app-building ↩

  90. https://forum.johnnydecimal.com/t/need-help-integrating-jd-and-my-pkm-vault-in-obsidian/1385 ↩

  91. https://github.com/Airtable/apps-base-schema ↩

  92. https://www.linkedin.com/posts/julienmottet_airtable-now-adds-a-new-default-field-when-activity-7320916421029339136-gp2B ↩

  93. https://community.airtable.com/automations-8/scripts-for-beginners-for-the-average-airtable-user-who-do-not-code-46829 ↩

  94. https://community.n8n.io/t/dynamic-prompts-with-n8n-baserow-and-airtable-free-templates/72052 ↩

  95. https://pkmjournal.com/the-johnny-decimal-system-2b02749a5f7 ↩

  96. https://community.airtable.com/show-and-tell-15/base-schema-for-bases-and-workspaces-1714 ↩

  97. https://pieces.app ↩ ↩2 ↩3

  98. https://docs.pieces.app/products/meet-pieces ↩

  99. https://docs.pieces.app/products/desktop/conversational-search ↩ ↩2 ↩3

  100. https://docs.pieces.app/products/web-extension ↩ ↩2

  101. https://aiagentslist.com/agents/pieces ↩

  102. https://github.com/pieces-app ↩

  103. https://www.reddit.com/r/LocalLLaMA/comments/1idxqca/how_to_make_a_local_ai_remember_conversations/ ↩

  104. https://dev.to/get_pieces/local-large-language-models-lllms-and-copilot-integrations-3i2p?comments_sort=oldest ↩

  105. ↩
  106. ↩

  107. https://www.reddit.com/r/ObsidianMD/comments/14brcrj/ama_were_piecesapp_and_were_thrilled_to_receive/ ↩

  108. https://docs.pieces.app/products/large-language-models ↩

  109. https://pieces.app/plugins/web-extension ↩

  110. https://github.com/pieces-app/support/issues/208 ↩

  111. https://addons.mozilla.org/en-US/firefox/addon/pieces-save-code-from-the-web/ ↩

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