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Brain Taxonomist

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garrytan
brain-taxonomist

Filing gate for ALL brain writes. Consulted before creating any new brain page to determine the correct path. Reads the ACTIVE schema pack via `gbrain schema show --json` — no hardcoded directory table. Also runs periodic taxonomy drift detection via `gbrain schema review-orphans`.

Overview

Publishergarrytan
Repositorygbrain
Skill namebrain-taxonomist
Stars
30.1K
Forks
4.5K
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by garrytan on GitHub. Read the source before you install it.

Installation

Install the Brain Taxonomist AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/garrytan/gbrain.git /tmp/gbrain
mkdir -p .claude/skills
cp -r /tmp/gbrain/plugin/skills/brain-taxonomist .claude/skills/brain-taxonomist
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Brain Taxonomist in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Brain Taxonomist on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Brain Taxonomist is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

brain-taxonomist

Purpose

Gate function: Before creating ANY new brain page, consult this skill to determine the correct filing path. This prevents misfiling at write time rather than cleaning up drift after the fact.

Drift function: Periodic scan for pages that have outgrown their current location.

Contract

This skill guarantees:

  • Every new page is filed at the path determined by the ACTIVE schema pack — never against a hardcoded directory table baked into this skill.
  • The decision is reproducible: invoking brain-taxonomist twice on the same content produces the same recommended path.
  • Ambiguous cases surface to the user via skills/ask-user/ rather than silently picking a default.
  • Per-source overrides via --source <id> are honored — multi-brain users (Persona B) get a different recommendation per source if their packs diverge.
  • When no matching page_types[] entry exists in the active pack, the skill signals to EIIRP Phase 3 (SCHEMA CHECK) rather than picking the closest-fitting fallback.

Critical: this skill reads the ACTIVE schema pack as data

brain-taxonomist has NO hardcoded directory table. Every decision is driven by gbrain schema show --json. This means:

  • A user who runs gbrain schema use gbrain-recommended gets the full recommended directory set (deal, meeting, concept, project, source, daily, personal, civic, original, place, trip, conversation, writing, plus all gbrain-base types).
  • A user who authored a custom pack via gbrain schema init + edit gets filing recommendations based on THEIR taxonomy, not gbrain's defaults.
  • Per-source overrides (tier 3 in the 7-tier resolution chain) are honored when --source <id> is passed to brain-taxonomist.

This is the single-source-of-truth principle (D9 from the v0.39 plan-eng-review).

When to Consult (MANDATORY)

Run the taxonomist check before writing to the brain in these cases:

  1. New brain page — any type (person, company, concept, book, meeting, etc.)
  2. Bulk import — before committing a batch of new pages
  3. Uncertain filing — when the primary subject is ambiguous

You do NOT need to consult for:

  • Updating an existing page in place (same path)
  • Appending to a Timeline section
  • Meeting entity propagation to existing pages

Decision Protocol

Step 1: Identify primary subject type

Walk these questions in order:

  1. Is the primary subject a NAMED PERSON? → person-typed directory
  2. Is the primary subject a NAMED ORGANIZATION? → company-typed directory
  3. Is it about a TIME-BOUNDED EVENT (meeting, deal, trip)? → temporal-typed directory
  4. Is it a REUSABLE MENTAL MODEL? → concept-typed directory
  5. Is it RAW MEDIA (article, video, book, PDF)? → media-typed directory
  6. Is it BULK SOURCE DATA? → source-typed directory
  7. None of the above → consult EIIRP Phase 3 for schema-pack candidate creation.

Step 2: Look up the directory for that type in the active pack

bash
gbrain schema show --json | jq '.page_types[] | select(.primitive == "entity")'

Each page_types[] entry has a path_prefixes: array. The first prefix is the canonical path. If multiple types match (e.g. both person and founder exist in the pack with expert_routing: true), prefer the more specific one (the one with the more specific path prefix).

Step 3: For books — determine sub-category

The gbrain-recommended pack treats books as media/books/<category>/<slug>.md where category is one of: psychology, philosophy, spirituality, business, media-and-society, family-and-divorce, heritage, science, fiction, biography, arts-and-design. If your active pack has a different scheme, walk it from gbrain schema show --json instead of hardcoding here.

Step 4: Construct the slug

  • kebab-case, descriptive
  • no author name unless disambiguation is needed
  • match the canonical path prefix exactly (no leading slash)

Step 5: Validate before writing

  • Path follows the active pack's page_types[].path_prefixes
  • Slug is kebab-case, descriptive
  • Frontmatter includes type: matching one of the pack's page_types[].name
  • Cross-links to related pages are included

If the active pack doesn't have a type for what you're trying to file, DON'T pick the closest-fitting one. Instead, signal to EIIRP that a new type is needed and let the schema-pack cathedral handle the proposal flow.

Integration with Other Skills

  • eiirp — calls this skill as Phase 2 TAXONOMY for every output in its inventory.
  • ingest — article/media ingestion consults brain-taxonomist for filing.
  • repo-architecture — delegates the filing decision to this skill.
  • book-mirror — after generating a mirror, files it via brain-taxonomist.

Periodic Drift Detection

bash
# What pages have no type matching the active pack?
gbrain schema review-orphans --json

# What's the overall health?
gbrain doctor --json | jq '.checks[] | select(.name == "schema_pack_consistency")'

When schema_pack_consistency warns at >10% untyped, run the EIIRP Phase 3 SCHEMA CHECK flow to surface candidate types via schema detect.

Output Format

Advisory: a single recommendation block plus a one-line reasoning trail.

markdown
**File at:** `<directory>/<slug>.md`
**Reasoning:**
- Primary subject: <person|company|concept|...>
- Matched page_type: <name> (primitive: <entity|temporal|concept|media|annotation>)
- Active pack: <pack-name> v<version>
- Source: <source_id>

When ambiguous, surface 2 candidates via skills/ask-user/ rather than silently choosing.

When the active pack has NO matching type, signal to EIIRP Phase 3 (SCHEMA CHECK) and emit:

markdown
**No match in active pack `<name>`.**
**Suggested next step:** `gbrain schema detect --source <source_id>` then
`gbrain schema review-candidates`.

Anti-Patterns

  • Hardcoded directory table in this skill. Every decision goes through gbrain schema show --json. v0.39+ broke the old hardcoded table on purpose so users on gbrain-recommended or custom packs get the right routing automatically.
  • Picking the closest-fitting type when no type matches. Closest-fit silently degrades user filing. Surface to EIIRP Phase 3 instead.
  • Ignoring --source <id> on multi-brain setups. Per-source overrides are tier-3 in the 7-tier resolution chain; missing the flag silently uses the brain-wide active pack.
  • Auto-applying a gbrain schema review-candidates --apply decision. Even high-confidence suggestions need user approval — this skill is a GATE, not an automator.

Hard Rules

  • Never hardcode a directory table in this skill. Every decision goes through gbrain schema show --json. The active pack is canonical.
  • Per-source flag is first-class. Pass --source <id> to every CLI call when working with a non-default source.
  • Confidence-floor honor. EIIRP's Phase 3 produces suggestions with confidence < 0.6 that brain-taxonomist must surface to the user rather than auto-apply. Don't silently promote a low-confidence schema delta.

Changelog

v1.0.0 — gbrain v0.39.0.0

  • Initial port from upstream OpenClaw. Genericized — no references to private fork names per CLAUDE.md privacy rules.
  • Hardcoded directory table REMOVED. Every decision now reads the active schema pack via gbrain schema show --json. Single source of truth.
  • Book taxonomy moved from skill-text to the gbrain-recommended pack's media/books/ branch (see src/core/schema-pack/base/gbrain-recommended.yaml).
  • --source <id> propagation documented for multi-brain users (Persona B).

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Brain Taxonomist AI skill do?

Filing gate for ALL brain writes. Consulted before creating any new brain page to determine the correct path. Reads the ACTIVE schema pack via `gbrain schema show --json` — no hardcoded directory table. Also runs periodic taxonomy drift detection via `gbrain schema review-orphans`.

Why use Brain Taxonomist on TypingMind?

Because you install it once and use it with any model. Brain Taxonomist is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Brain Taxonomist in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/garrytan/gbrain/tree/master/plugin/skills/brain-taxonomist. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Brain Taxonomist?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Brain Taxonomist?

As many as you like. As long as a model supports skills, you can use Brain Taxonomist with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Brain Taxonomist AI skill free?

Yes. It is published on GitHub by garrytan under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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