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Brain Ingest Gate

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garrytan
brain-ingest-gate

Pre-write quality gate for content entering the brain. No raw copies: a bare cp/mv into the brain repo is a bug. Before any new page lands, resolve named entities registry-first (a vector score is a floor for prose, never a gate for named things), then run the read-the-top-hit dedup decision tree (clear-dup / plausible-dup / clear). Owns dedup; delegates enrichment to the shipped ingestion skills. Routing convention, not an operation-boundary enforcement.

Overview

Publishergarrytan
Repositorygbrain
Skill namebrain-ingest-gate
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 Ingest Gate 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-variants/gbrain-coding/skills/brain-ingest-gate .claude/skills/brain-ingest-gate
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Brain Ingest Gate 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 Ingest Gate 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 Ingest Gate 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 Ingest Gate — Resolve and Dedup Before Anything Enters the Brain

Convention: see conventions/brain-first.md — the lookup chain (gbrain entitysearchqueryget) is the same chain this gate runs before every write.

Convention: see _brain-filing-rules.md — when the gate's verdict is "write", the primary subject picks the directory.

Convention: skills/conventions/quality.md owns the cross-cutting page rules (citations, Iron Law back-linking, notability) — every page the gate lets through follows them. Gate-specific delta: the gate only decides write/link/skip; the admitting skill applies the quality rules on write.

The Rule

No content enters the brain without passing this gate. A raw cp or mv into the brain repo is a bug.

One insight, one place. If it already exists, link to it — don't clone it. Before any new page is written (file migration, bulk import, manual gbrain put, subagent output), two checks run in order:

  1. Named-Entity Resolution Gate — is this about a named thing that already has a page under its chosen name?
  2. Dedup Gate — does the brain already state this insight somewhere?

Scope honesty: this gate is a routing convention — the harness resolves it into context when an ingest-shaped intent matches, and a well-behaved agent follows it. Nothing in the gbrain runtime mechanically blocks an unenriched or duplicate write if the skill never loads.

Why gbrain needs this gate

The native pipeline does NOT do semantic dedup for you:

  • gbrain import / gbrain sync skip only matching frontmatter IDs. Identical content under a different slug or ID indexes twice — every duplicate becomes a second search hit competing with the canonical page.
  • gbrain capture's dedup is a 24-hour exact content-hash — it catches re-captures of identical bytes, not the same insight reworded.
  • The remember verb dedupes facts, not pages.

Semantic dedup and named-entity resolution are this skill's job, in full.

When This Gate Fires

  1. File migration — moving files already in the workspace into the brain repo ("move this to brain").
  2. Bulk imports — batch moves of any kind into brain directories, BEFORE gbrain sync or gbrain import indexes them. For batches, also read conventions/test-before-bulk.md: gate 3-5 items and inspect the decisions before running the rest.
  3. Manual writesgbrain put or gbrain capture of rich content, or direct file writes into the brain repo.
  4. Subagent output — background agents writing notes or pages into the brain.

What This Gate Owns vs Delegates

This skill is a gate, not a pipeline. It owns the pre-write checks below. Everything downstream of a "write" verdict is delegated to shipped skills — do not restate their steps here or inline:

ConcernDelegate to
Routing new external content (meetings, articles, media)ingest
Entity detection + notability on inbound contentsignal-detector
Creating/updating person + company pages, tiered effort, backlinksenrich
Concept pages, tiering, cluster synthesisconcept-synthesis
Back-link enforcement (Iron Law)conventions/quality.md
Which directory the page lands in_brain-filing-rules.md

Named-Entity Resolution Gate (runs FIRST)

Fires whenever the content is about a NAMED project, place, company, person, or anything someone "wants to build / found / make."

Vector similarity alone cannot be trusted to catch named-entity dupes: a page stored under its chosen NAME will not embed close to the generic English phrase someone happens to describe it with. The classic failure: a search for a descriptive phrase scores the canonical named page below the prose floor, so a duplicate stub gets written on top of a years-old page. Stored by named meaning; retrieval attempted by literal generic phrase.

The rules

  1. Resolve registry-first, not by the generic phrase. gbrain's native registry is the entity surface:

    bash
    gbrain entity "<name>"        # zero-LLM card: page, aka list, near-miss suggestions

    A card hit means the page exists — STOP, link, don't clone. On a miss (or for concept-shaped nouns), fall through to gbrain query "<name>" --limit 3. If the brain also keeps an explicit index of named initiatives (e.g. a page under concepts/), read it before concluding anything is new.

  2. Expand through aliases before searching. Named pages should carry an aliases: frontmatter list (generic label + chosen name + any nickname + signature phrase). Search EACH alias and the generic label, not just the phrase the user happened to say.

  3. A vector score is a floor for prose, NEVER a gate for named things. If there is ANY plausible named match, open and read the candidate page (gbrain get <slug>) before concluding it doesn't exist. A named page can be the right answer at a score that would be a clear miss for prose.

  4. When a NEW named thing appears, bake its aliases in the same write. Create the page with the full aliases: list so every future synonym resolves through gbrain entity. One frontmatter list covers all future phrasings — O(1), not a per-instance reminder.

Why a gate and not a memory note

A memory reminder ("query the real name, not the generic phrase") is a per-instance sticky note: it only works if it happens to be in hot context that turn, doesn't generalize to the next named entity, and rots. This skill loads when an ingest-shaped task routes here. Process rules belong in the triggered gate, not in hot memory.

Dedup Gate (runs SECOND)

Before writing ANY new page (for named things, the resolution gate above runs first and takes precedence):

  1. Extract the core claim — 1-2 sentences capturing what's novel about the new content.

  2. Search for it:

    bash
    gbrain search "<core claim>" --limit 5
  3. OPEN AND READ the top hit (gbrain get <slug>). Never band on the score alone. Donor systems publish cosine cutoffs for this step — do NOT port them: gbrain search returns fused hybrid rank scores, not cosine similarity, and no numeric threshold maps across. The band comes from reading, not from the number.

  4. Assign a band:

    BandMeaningAction
    clear-dupThe top hit already states the same insight about the same subjectSTOP. Link to the existing page (gbrain link / gbrain timeline-add) instead of writing.
    plausible-dupSame territory; possibly a new angleRead both fully. Same insight → link, don't write. Genuinely new angle → write WITH a cross-link to the existing page.
    clearNothing in the top results covers the claimWrite normally through the delegated enrichment skills.

Decision tree

New content to write
  ├─ Named thing? → Named-Entity Resolution Gate first
  │    (entity card → alias-expanded search → READ the candidate)
  ├─ Extract core claim (1-2 sentences)
  ├─ gbrain search "<core claim>" --limit 5
  └─ OPEN AND READ the top hit (gbrain get <slug>)
      ├─ clear-dup     → STOP. Link to existing. Report "duplicate".
      ├─ plausible-dup → Read both. Same insight?
      │    ├─ yes → STOP. Link to existing. Report "duplicate".
      │    └─ no  → Write with cross-link. Report "new angle".
      └─ clear         → Write via enrichment skills. Report "unique".

When to skip dedup

  • Operational/state files — time-series records, not knowledge.
  • Meeting transcripts — each meeting is unique by definition (entities INSIDE it still go through the named-entity gate via the delegated skills).
  • Timeline entries on existing pages — back-links are additive, not duplicative.
  • Media files — dedup by filename/hash, not semantic similarity.

Verification

After the batch, verify the gate's output holds:

bash
gbrain check-backlinks check            # mentioned entities link back (fix with: check-backlinks fix)
gbrain backlinks <new-slug>             # each new page has inbound links
gbrain search "<core claim>" --limit 3  # the insight has exactly ONE home

If check-backlinks check reports gaps on pages the gate just admitted, the enrichment delegation was skipped — route back through enrich before declaring the ingest done.

Contract

This skill guarantees:

  • No new page enters the brain through this skill's flows without the named-entity resolution check and the dedup check running first.
  • Every "duplicate" verdict names the matched slug and produces a link or timeline entry instead of a clone.
  • New named-entity pages carry an aliases: frontmatter list in the same write that creates them.
  • Dedup bands are assigned by READING the top hit, never by score alone; no numeric similarity thresholds are used against gbrain's fused scores.
  • Enrichment is delegated to shipped skills (ingest, enrich, signal-detector, concept-synthesis) — never restated or reimplemented inline.
  • Batches end with a gbrain check-backlinks check verification pass.
  • Routing matches the canonical triggers in the frontmatter.
  • Output written under the directories listed in writes_to:.
  • Privacy contract preserved: no real names, no fork-specific filesystem path literals, no upstream-fork references.

The full behavior contract is documented in the body sections above; this section exists for the conformance test.

Output Format

One decision line per item checked, then the verification result:

Ingest gate — 3 item(s) checked

| item | entity resolution | band | action |
|---|---|---|---|
| notes-on-widget-co.md | resolved: companies/widget-co | clear-dup | linked (timeline entry on companies/widget-co) |
| pricing-thesis.md | n/a (prose) | plausible-dup | new angle — written to concepts/ with cross-link to concepts/pricing-power |
| charlie-example-intro.md | miss (near-miss: people/charlie-example) | — | read near-miss; same person → linked, no new page |

Verification: check-backlinks check → 0 gaps on admitted pages

Every "linked" or "duplicate" row MUST name the matched slug. If any row says "written", the enrichment delegation (which skill handled it) should be recoverable from the conversation.

Anti-Patterns

  • cp file.md <brain-repo>/concepts/ — raw copy, no gate, no enrichment.
  • ❌ Bulk mv of a folder into the brain repo, then gbrain sync — sync happily indexes every duplicate; matching-ID skip will not save you.
  • ❌ Trusting a low vector score as proof a named thing has no page — named pages don't embed near generic descriptions of them.
  • ❌ Banding on the search score without opening the top hit.
  • ❌ Porting numeric dedup thresholds from other systems onto gbrain's fused scores.
  • ❌ Writing a new named page without its aliases: list — the next synonym creates the next duplicate.
  • ❌ Reimplementing entity detection, backlinking, or concept linking inline instead of delegating to the shipped skills.
  • ❌ Skipping the gate because the write is "just one page" via gbrain put — single manual writes are where duplicate stubs come from.

Dedup (sharp boundaries)

  • capture — the quick-save front door; its dedup is a 24h exact content-hash on identical bytes. This gate is the SEMANTIC + named-entity layer for content entering the brain as real pages (migrations, bulk imports, inbox graduation). "capture this thought" → capture; "migrate these files into the brain" → this gate.
  • ingest — the router for NEW external content (meetings, articles, media) and its enrichment pipeline. ingest decides what to DO with content; this gate decides whether a page should EXIST at all. The gate fires before the write; ingest and its specialized skills handle everything after a "write" verdict.
  • enrich — page creation/update mechanics (tiers, citations, timelines, backlinks) AFTER this gate says "write" or "link".
  • concept-synthesis — retroactive, at-scale dedup of concept stubs that already slipped in. This gate is prevention at write time; concept-synthesis is the cleanup pass. "dedupe my existing concepts" → concept-synthesis.
  • frontmatter-guard (host-side) — the same standalone-gate pattern on an orthogonal axis: structural validity of what's written vs (here) semantic novelty of whether to write.
  • bulk-ingestion — the bulk sibling. Its pipeline dedup key (source + source_id) only makes RE-RUNS idempotent; it does not catch cross-source duplicates or resolve named entities. This gate is the semantic + named-entity layer bulk-ingestion runs on its Phase 3 trial items and bakes into the codified pipeline (its Phase 1d/6). "Build a large-corpus pipeline" → bulk-ingestion; "does this page already exist before I write it" → this gate.
  • data-loss-gate — the inverse gate: it stops data LEAVING the brain without confirmation; this gate stops data ENTERING without resolution + dedup.

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 Ingest Gate AI skill do?

Pre-write quality gate for content entering the brain. No raw copies: a bare cp/mv into the brain repo is a bug. Before any new page lands, resolve named entities registry-first (a vector score is a floor for prose, never a gate for named things), then run the read-the-top-hit dedup decision tree (clear-dup / plausible-dup / clear). Owns dedup; delegates enrichment to the shipped ingestion skills. Routing convention, not an operation-boundary enforcement.

Why use Brain Ingest Gate on TypingMind?

Because you install it once and use it with any model. Brain Ingest Gate 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 Ingest Gate in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/garrytan/gbrain/tree/master/plugin-variants/gbrain-coding/skills/brain-ingest-gate. 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 Ingest Gate?

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 Ingest Gate?

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

Is the Brain Ingest Gate 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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