Skills Agentes

Brain Ingest Gate

Filtro de calidad previo a la escritura para todo lo que entra al brain: nada de cp/mv en crudo. Resuelve entidades con nombre por registro y aplica el árbol de decisión de dedup leyendo el primer resultado.

Reemplaza a: cp/mv directo de archivos al repo brain, Confiar solo en el score vectorial de gbrain search para detectar duplicados

Estrellas
28.9k

en todo el repo

Actividad
59

0–100, la ruta de este skill

Actualizado
hace 9 días

último commit aquí

Commits
1

últimos 90 días

Contexto
3.7k tok

115 tok en reposo

Paquete
2 archivos

16 KB

Instalar

Funciona con cualquier agente que lea SKILL.md

npx -y skills add garrytan/gbrain --skill brain-ingest-gate --agent claude-code

Se instala solo en este repositorio.

Qué hace

  • Runs a named-entity resolution check via gbrain entity/search before any new page write
  • Runs a dedup gate that reads the top search hit and assigns clear-dup/plausible-dup/clear bands
  • Blocks raw cp/mv writes into the brain, requiring link-instead-of-clone on duplicates
  • Requires new named-entity pages to carry a full aliases: frontmatter list on creation
  • Delegates enrichment (page creation, backlinks, tiering) to shipped skills after a write verdict

Úsalo cuando

  • Moving files already in the workspace into the brain repo
  • Bulk imports before gbrain sync or gbrain import indexes them
  • Manual gbrain put or gbrain capture of rich content
  • Subagent output writing notes or pages into the brain

No lo uses cuando

  • Operational/state files that are time-series records, not knowledge
  • Meeting transcripts (each meeting is unique by definition)
  • Media files, which dedupe by filename/hash instead

Qué lo activa

Di cualquiera de estas frases y el agente debería cargar este skill.

  • Mueve estos archivos al brain
  • Migra estas notas al repositorio brain
  • ¿Esto ya está en el brain?
  • Revisa duplicados antes de guardar esta página
  • Haz dedup antes de escribir en concepts/

SKILL.md

En inglés

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:

Concern Delegate to
Routing new external content (meetings, articles, media) ingest
Entity detection + notability on inbound content signal-detector
Creating/updating person + company pages, tiered effort, backlinks enrich
Concept pages, tiering, cluster synthesis concept-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:

    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:

    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:

    Band Meaning Action
    clear-dup The top hit already states the same insight about the same subject STOP. Link to the existing page (gbrain link / gbrain timeline-add) instead of writing.
    plausible-dup Same territory; possibly a new angle Read both fully. Same insight → link, don't write. Genuinely new angle → write WITH a cross-link to the existing page.
    clear Nothing in the top results covers the claim Write 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:

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.

Reproducido de garrytan/gbrain bajo licencia MIT. Leer esta página en markdown.

Archivos

2 archivos en el paquete. Solo se lee SKILL.md al activarse — las referencias se cargan si el skill decide que las necesita.

Antes de instalar

Requiere el CLI gbrain (entity, search, query, get, link, timeline-add, check-backlinks) y el brain repo con directorios people/, companies/, concepts/, projects/.

Detalles

Creador
garrytan
Categoría
Productividad
Licencia
MIT
Recursos incluidos
Incluye scripts o referencias
Repositorio
garrytan/gbrain
Código fuente
Ver SKILL.md

Etiquetas

Más de garrytan/gbrain

Este repo incluye 75 skills. Si instalas uno, normalmente ya tienes los demás.

Setup

28.9k

Configura GBrain con auto-aprovisionamiento de Supabase o PGLite, inyección en AGENTS.md y primera importación.

Costo de contexto al activarse
7.4k tok
Tamaño del paquete
1 archivo
Última actualización
hace 4 días
bases de datos

Chequeos de salud del brain: aplicación de back-links, auditoría de citas, validación de filing, detección de info obsoleta, páginas huérfanas y benchmarks.

Costo de contexto al activarse
5k tok
Tamaño del paquete
1 archivo
Última actualización
hace 4 días
productividad

Migra un brain de gbrain-base a la taxonomía de 14 tipos canónicos de gbrain-base-v2 usando gbrain onboard --check y el handler Minion unify-types.

Costo de contexto al activarse
3.2k tok
Tamaño del paquete
1 archivo
Última actualización
hace 5 días
bases de datos

Cuándo y qué recuperar: abre la página del brain de una entidad relevante antes de responder desde memoria.

Costo de contexto al activarse
740 tok
Tamaño del paquete
1 archivo
Última actualización
hace 1 hora
productividad

Operaciones del brain: búsqueda primero, ciclo leer-enriquecer-escribir, atribución de fuentes, enriquecimiento ambiental y back-linking. Leer antes de cualquier interacción con el brain.

Costo de contexto al activarse
2.6k tok
Tamaño del paquete
1 archivo
Última actualización
hace 3 días
productividad

Importa exports de ChatGPT, Claude y Perplexity y transcripciones de sesiones como páginas fechadas en conversations/, valida y extrae hechos, y mantiene el archivo sin huecos con detección y backfill.

Costo de contexto al activarse
5k tok
Tamaño del paquete
2 archivos
Última actualización
hace 4 días
productividad

Skills relacionados

Archivista universal para archivos personales (Dropbox/B2/Gmail-takeout/disco local). Filtra contenido de alto valor y lo muestra de forma interactiva; exige un allow-list scan_paths explícito en gbrain.yml.

Costo de contexto al activarse
2.7k tok
Tamaño del paquete
2 archivos
Última actualización
hace 3 meses
productividad

Transforma volcados de texto crudo de artículos en el brain en páginas estructuradas con resumen ejecutivo, citas textuales, insights clave, por qué importa y referencias cruzadas.

Costo de contexto al activarse
1.5k tok
Tamaño del paquete
2 archivos
Última actualización
hace 3 meses
productividad

Operaciones del brain: búsqueda primero, ciclo leer-enriquecer-escribir, atribución de fuentes, enriquecimiento ambiental y back-linking. Leer antes de cualquier interacción con el brain.

Costo de contexto al activarse
2.6k tok
Tamaño del paquete
1 archivo
Última actualización
hace 3 días
productividad