/concept-synthesis

name: concept-synthesis
version: 0.1.0
description: Deduplicate and synthesize raw concept stubs into a tiered intellectual map (T1 Canon to T4 Riff), tracing idea evolution across sources over time. Transforms thousands of raw concept pages into a curated intellectual fingerprint.
triggers:
  - "concept synthesis"
  - "synthesize my concepts"
  - "find patterns across my notes"
  - "build my intellectual map"
  - "trace idea evolution"
  - "canon vs riff"
mutating: true
writes_pages: true
writes_to:
  - concepts/

concept-synthesis — From Raw Stubs to Intellectual Map

> **Convention:** see [conventions/quality.md](../conventions/quality.md) for > back-link enforcement and quote-fidelity requirements. > > **Convention:** see [_brain-filing-rules.md](../_brain-filing-rules.md) — > output files under `concepts/` per the primary-subject rule.

What this solves

Many ingestion pipelines (signal-detector, idea-ingest, voice-note-ingest) create a concept page for every idea mentioned. Over months this produces:

• Thousands of stub pages, many duplicates or near-duplicates
• Timeline entries that repeat the same source across multiple concept pages
• No synthesis — just "the user mentioned X on this date"
• No tier assignments — everything flat
• No clustering — related ideas aren't linked

This skill transforms that raw material into a curated intellectual map.

Architecture

Phase 1: Dedup + merge (deterministic)
  N stubs → ~N/4 canonical concepts
    ├── Jaccard dedup (word-overlap on titles + first-paragraph)
    ├── Substring dedup ("founder mode" vs "founder mode vs manager mode")
    ├── Semantic dedup (LLM: "are these the same idea?")
    └── Merge timelines + aliases from duplicates into the canonical page

Phase 2: Score + tier (deterministic + heuristic)
  Each canonical concept → scored and tiered
    ├── Frequency: distinct sources referencing this concept
    ├── Timespan: first mention → last mention in days
    ├── Breadth: distinct months it appears in
    ├── Engagement: avg engagement on concept-bearing sources (if available)
    └── Tier: T1 Canon | T2 Developing | T3 Speculative | T4 Riff

Phase 3: Synthesize (LLM, T1+T2 only)
  T1 + T2 concepts → rich synthesis
    ├── Evolution narrative: how the idea sharpened over time
    ├── Best articulation: highest-engagement or most precise quote
    ├── Related concepts: cross-links to other concepts
    ├── Context: what was happening when this idea emerged / evolved
    └── Counter-positions: what this idea argues against

Phase 4: Cluster + map (LLM)
  All tiered concepts → intellectual clusters
    ├── Group related concepts into domains (auto-named via LLM)
    ├── Generate cluster summary pages
    ├── Build a master concepts/README.md with the full map
    └── Identify idea genealogies (concept A → evolved into concept B)

Invocation

The skill is markdown agent instructions. The agent uses gbrain's existing operations + LLM passes:

# 1. List all concept pages
gbrain query "type:concept" --limit 10000 --json

# 2. Phase 1 dedup — agent applies Jaccard + substring locally,
#    then LLM passes to identify semantic duplicates.

# 3. Phase 2 tier — agent scores each canonical concept based on
#    frequency / timespan / breadth and writes tier into frontmatter.

# 4. Phase 3 synthesis — for each T1/T2, agent reads the timeline
#    + associated source pages and writes a synthesis section
#    onto the concept page via put_page.

# 5. Phase 4 clustering — agent reads the tiered concept list
#    and writes concepts/README.md with the full intellectual map.

Output: concept page format (post-synthesis)

T1 Canon — full synthesis

---
title: "concept name"
type: concept
tier: 1
tier_label: "Canon"
mention_count: 18
distinct_months: 8
first_mention: "YYYY-MM-DD"
last_mention: "YYYY-MM-DD"
composite_score: 78.4
aliases: ["alternate phrasing 1", "alternate phrasing 2"]
related: ["sibling-concept-1", "sibling-concept-2"]
---

# concept name

**Tier 1 — Canon** | 18 mentions across 8 months

## Synthesis

[2-4 paragraph narrative tracing how the idea evolved, what it means in
the user's worldview, why it matters. Third-person analytical voice.]

## Best Articulation

> "Verbatim quote from a source — the most precise or highest-engagement
> expression of this idea." — [Date](source-url)

## Evolution

| Period | Expression | Signal |
|--------|-----------|--------|
| YYYY-MM | "First articulation" | First use — aspiration frame |
| YYYY-MM | "Sharpening" | Anti-pattern emerges |
| YYYY-MM | "Peak form" | Cleanest expression |

## Related Concepts
- [sibling concept](sibling-concept.md) — relationship description
- [sibling concept](sibling-concept.md) — relationship description

## Timeline
[Full timeline with deduped entries, quotes, source links]

T3 / T4 — stub only (no LLM synthesis)

---
title: "concept name"
type: concept
tier: 4
tier_label: "Riff"
mention_count: 1
---

# concept name

**Tier 4 — Riff** | 1 mention

> "Quote from the source" — [Date](URL)

Output: cluster map at concepts/README.md

# Intellectual Universe

## Canon (T1) — N concepts
The permanent intellectual fingerprint. Ideas that recur across years.

### [Cluster Name]
- [concept-slug](concept-slug.md) — one-line characterization
- ...

### [Other Cluster]
- ...

## Developing (T2) — N concepts
Sharpening. Might become canon.

## Speculative (T3) — N concepts
Testing in public.

## Stats
- Total concepts: N
- T1 Canon: N
- T2 Developing: N
- T3 Speculative: N
- T4 Riff: N
- Earliest source: YYYY-MM-DD
- Latest source: YYYY-MM-DD

Quality gates

Dedup quality

• No two concept pages should be "the same idea in different words."
• Aliases preserved in frontmatter for search.
• Run `gbrain query "type:concept"` and spot-check the count reduction.

Tier quality

• T1 should feel like "yes, that IS one of my recurring frameworks" —

recognizable, rec