Enrich Skill

Enrich person and company pages from external sources. Scale effort to importance.

Contract

This skill guarantees:

• Every enriched page has compiled truth (State section) with inline citations
• Every enriched page has a timeline with dated entries
• Back-links are created bidirectionally
• Tiered enrichment: Tier 1 (full), Tier 2 (medium), Tier 3 (minimal) based on notability
• No stubs: every new page has meaningful content from web search or existing brain context

Filing rule: Read skills/_brain-filing-rules.md before creating any new page.

Convention: See skills/conventions/quality.md for Iron Law back-linking.

Every mention of a person or company with a brain page MUST create a back-link FROM that entity's page TO the page mentioning them. An unlinked mention is a broken brain. See skills/_brain-filing-rules.md for format.

Philosophy

A brain page should read like an intelligence dossier, not a LinkedIn scrape. Facts are table stakes. Texture is the value -- what do they believe, what are they building, what makes them tick, where are they headed.

Citation Requirements (MANDATORY)

Convention: see skills/conventions/quality.md for citation formats and source precedence.

When sources conflict, note the contradiction with both citations.

When To Enrich

Primary triggers

• User mentions an entity in conversation
• Entity appears in a meeting transcript or email
• New contact appears with significant context
• Entity makes news or has a major event
• Any ingest pipeline encounters a notable entity

Do NOT enrich

• Random mentions with no relationship signal
• Bot/spam accounts
• Entities with no substantive connection to the user's work
• Same page enriched within the past week (unless new signal warrants it)

Enrichment Tiers

Scale enrichment to importance. Don't waste API calls on low-value entities.

Tier	Who	Effort	Sources
1 (key)	Inner circle, close collaborators, key contacts	Full pipeline	All available APIs + deep web research
2 (notable)	Occasional interactions, industry figures	Moderate	Web research + social + brain cross-ref
3 (minor)	Worth tracking, not critical	Light	Brain cross-ref + social lookup if handle known

The Enrichment Protocol (7 Steps)

Step 1: Identify entities

Extract people, companies, concepts from the incoming signal.

Step 2: Check brain state

For each entity:

• gbrain search "name" -- does a page already exist?
• If yes: UPDATE path (add new signal, update compiled truth if material)
• If no: CREATE path (check notability gate first, then create)

Step 3: Extract signal from source

Don't just capture facts. Capture texture:

Signal Type	What to Extract
Opinions, beliefs	What They Believe section
Current projects, features shipped	What They're Building section
Ambition, career arc, motivation	What Motivates Them section
Topics they return to obsessively	Hobby Horses section
Who they amplify, argue with, respect	Network / Relationships
Ascending, plateauing, pivoting?	Trajectory section
Role, company, funding, location	State section (hard facts)

Step 4: External data source lookups

Priority order -- stop when you have enough signal for the entity's tier.

4a. Brain cross-reference (always, all tiers)

• gbrain search "name" and gbrain query "what do we know about name"
• Check related pages: company pages for person enrichment and vice versa
• This is free and often the richest source

4b. Web research (Tier 1 and 2)

• Use Perplexity, Brave Search, Exa, or equivalent web research tool
• Key pattern: Send existing brain knowledge as context so the search returns DELTA (what's new vs what you already know), not a rehash
• Opus-class models for Tier 1 deep research, lighter models for Tier 2

4c. Social media lookup (all tiers when handle known)

• Pull recent posts/tweets for tone, interests, current focus
• Social media is the highest-texture signal for what someone actually thinks

4d. People enrichment APIs (Tier 1)

• LinkedIn data, career history, connections, education

4e. Company enrichment APIs (Tier 1)

• Company data, financials, headcount, key hires, recent news

Data Need	Example Sources	Tier
Web research	Perplexity, Brave, Exa	1-2
LinkedIn / career	Crustdata, Proxycurl, People Data Labs	1
Career history	Happenstance, LinkedIn	1
Funding / company data	Crunchbase, PitchBook, Clearbit	1
Social media	Platform APIs, web scraping	1-3
Meeting history	Calendar/meeting transcript tools	1-2

Step 5: Save raw data (preserves provenance)

Store raw API responses via put_raw_data in gbrain:

{
  "source": "crustdata",
  "fetched_at": "2026-04-11T...",
  "query": "jane doe",
  "data": { ... }
}

Raw data preserves provenance. If the compiled truth is ever questioned, the raw data shows exactly what the API returned.

Step 6: Write to brain

CREATE path

1. Check notability gate (see skills/_brain-filing-rules.md)
2. Resolve the entity's type, directory and subtype from the ACTIVE schema pack, never from the templates below: follow skills/brain-taxonomist/SKILL.md (gbrain schema show --json, or MCP get_active_schema_pack; schema_explain_type for one type). When the pack declares an entity type the evidence fits better than person or company (an investment firm, a protocol foundation), file it under that type's directory with its subtype. If no declared type fits, say so and ask; don't fall back to companies/.
3. Create the page from the template below, with the type and directory from step 2. Frontmatter carries only keys the pack declares for the type or that existing pages of the type already use, and only with known values: omit an unknown field entirely, never write null, an empty value or a placeholder.
4. Fill compiled truth with citations
5. Add first timeline entry
6. Leave empty sections as [No data yet] (don't fill with boilerplate)
7. In a batch, create the pages other pages will link to first. A link to a page that doesn't exist yet is recorded as a wanted page and becomes an edge once that page is written (gbrain wanted lists them).

UPDATE path

1. Add new timeline entries (reverse-chronological, append-only)
2. Update compiled truth ONLY if the new signal materially changes the picture
3. Update State section with new facts
4. Flag contradictions between new signal and existing compiled truth
5. Don't overwrite user-written assessments with API boilerplate

Person page template

---
title: Full Name
type: person
created: YYYY-MM-DD
updated: YYYY-MM-DD
tags: []
company: Current Company
relationship: How the user knows them
# email, linkedin, twitter, location: add each only when you know it
---

# Full Name

> 1-paragraph executive summary: HOW do you know them, WHY do they matter,
> what's the current state of the relationship.

## State
Role, company, key context. Hard facts only.

## What They Believe
Ideology, first principles, worldview. What hills do they die on?

## What They're Building
Current projects, recent launches, what they're focused on.

## What Motivates Them
Ambition, career arc, what drives them.

## Communication Style
How they like to communicate, how they handle disagreement, what energizes
them in a conversation. Write only what you observed directly (meetings,
their emails or posts) or what they said about themselves; label each line
observed, self-described or inferred. One data point is a timeline entry,
not a pattern. Confidence follows interaction count: low after one, high
after five or more.

## Hobby Horses
Topics they return to obsessively. Recurring themes in their work/posts.

## Assessment
Your read on this person. Strengths, gaps, trajectory.

## Trajectory
Ascending, plateauing, pivoting, declining? Where are they headed?

## Relationship
History of interactions, shared context, relationship quality.

## Contact
Email, social handles, preferred communication channel.

## Network
Key connections, mutual contacts, organizational relationships.

## Open Threads
Active conversations, pending items, things to follow up on.

---

## Timeline
Reverse chronological. Every entry has a date and [Source: ...] citation.
- **YYYY-MM-DD** | Event description [Source: ...]

Company page template

---
title: Company Name
type: company
created: YYYY-MM-DD
updated: YYYY-MM-DD
tags: []
---

# Company Name

> 1-paragraph executive summary.

## State
What they do, stage, key people, key metrics, your connection.

## Open Threads
Active items, pending decisions, things to track.

---

## Timeline
- **YYYY-MM-DD** | Event description [Source: ...]

Step 7: Cross-reference

• Update company pages from person enrichment (and vice versa)
• Update related project/deal pages if relevant context surfaced
• Check index files if the brain uses them

Note (v0.10.1): Links between brain pages are auto-created on every put_page call (auto-link post-hook). Step 7 focuses on content cross-references (updating related pages' compiled truth with new signal from this enrichment), not on creating links. On a trusted local write the put_page response carries auto_links: { created, removed, errors }. MCP writes (stdio and HTTP) skip inline auto-link (auto_links.skipped: "remote") but queue plain mentions edges to pages that already exist (auto_links.mention_links: "queued"). Typed and frontmatter edges (works_at, invested_in, source, ...) wait for a maintenance pass: the stdio serve sweep, or gbrain sweep --once on the host for an HTTP brain. Use add_link for a typed edge you need immediately. Timeline entries still need explicit gbrain timeline-add calls.

Bulk Enrichment Rules

• Test on 3-5 entities first. Read actual output. Check quality.
• Only proceed to bulk after test shots pass your quality bar.
• 3+ entities from one source -> batch process or spawn sub-agent
• Throttle API calls. Respect rate limits.
• Commit every 5-10 entities during bulk runs.
• Save a report after bulk enrichment (see Report Storage below).

Validation Rules

• Connection count < 20 on LinkedIn = likely wrong person, skip
• Name mismatch between brain and API = skip, flag for review
• Joke profiles or obviously wrong data = save to raw, don't update page
• Don't overwrite user-written assessments with API boilerplate
• When in doubt: save raw data but don't update brain page

Report Storage

After enrichment sweeps, save a report:

• Number of entities processed
• New pages created vs existing updated
• Data sources called and results quality
• Notable discoveries or contradictions
• Validation flags or API failures

This creates an audit trail for brain enrichment over time.

When it fails

Follow the agent operator protocol for any gbrain error code, exit code, [AGENT] block or notice block. Specific to this skill:

• External enrichment APIs return rate_limited or an auth failure: back off for the stated delay; on auth failure stop and tell the user which key is missing. Never fill the gap with guessed facts.
• put_page returns revision_conflict: re-read the entity page, merge, and save with the new revision.
• auto_links.errors is non-empty on a write: the page saved but some links did not; list them in the report and add them with add_link after fixing the slugs.

Anti-Patterns

• Creating stub pages with no content
• Enriching without checking brain first
• Overwriting user's direct statements with API data
• Creating pages for non-notable entities

Output Format

An enriched person page contains:

• Frontmatter with type, tags, company, relationship, and contact fields
• Executive summary (1 paragraph: how you know them, why they matter, relationship state)
• State section with hard facts and inline [Source: ...] citations
• Texture sections (What They Believe, What They're Building, What Motivates Them, Hobby Horses)
• Assessment with trajectory read
• Relationship history and contact info
• Network connections and mutual contacts
• Timeline in reverse chronological order, every entry dated with source citation

An enriched company page contains:

• Frontmatter with type and tags
• Executive summary (1 paragraph)
• State section (what they do, stage, key people, metrics, your connection)
• Open Threads (active items, pending decisions)
• Timeline in reverse chronological order with dated, cited entries

Both page types have bidirectional back-links to every entity they mention.

Tools Used

• Read a page from gbrain (get_page)
• Store/update a page in gbrain (put_page)
• Add a timeline entry in gbrain (add_timeline_entry)
• List pages in gbrain by type (list_pages)
• Store raw API data in gbrain (put_raw_data)
• Retrieve raw data from gbrain (get_raw_data)
• Link entities in gbrain (add_link)
• Check backlinks in gbrain (get_backlinks)