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brain-ops大脑操作

Agent Skill

brain-ops 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

1,360

周安装

55

GitHub Stars

12,485

下载量

427
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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:brain-ops(大脑操作)
来源仓库:https://github.com/garrytan/gbrain
仓库路径:skills/brain-ops
安装命令:
npx skills add https://github.com/garrytan/gbrain --skill brain-ops
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/garrytan/gbrain --skill brain-ops

简介

brain-ops 构建动态上下文膜层,强制所有外部调用前先查询内部脑状态以维持一致性。

  • 适用于需要强约束的知识密集型任务,防止模型偏离既定原则或重复提问。
  • 通过 READ→ENRICH→WRITE 循环保障信息流双向校验,提升输出可信度。
  • 依赖本地脑库完整性,空脑状态下可能限制部分高级推理功能的发挥。
  • brain-ops 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Brain Operations — The Ambient Context Layer

The brain is not an archive. It is a live context membrane that every interaction flows through in both directions.

Convention: See skills/conventions/brain-first.md for the 5-step lookup protocol. Convention: See skills/conventions/quality.md for citation and back-link rules.

Contract

This skill guarantees:

  • Brain is checked BEFORE any external API call (brain-first lookup)
  • Every inbound signal triggers the READ → ENRICH → WRITE loop
  • Every outbound response checks brain for relevant context
  • Source attribution on every fact written (inline [Source:...] citations)
  • User's direct statements are highest-authority data
  • Back-links maintained on every brain write (Iron Law)

Iron Law: Back-Linking (MANDATORY)

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/conventions/quality.md for format.

Phases

Phase 1: Brain-First Lookup (MANDATORY)

Before using ANY external API to research a person, company, or topic:

  1. gbrain search "name" — keyword search for existing pages
  2. gbrain query "natural question about name" — hybrid search for context
  3. gbrain get <slug> — if you know the slug, read the full page
  4. Check backlinks: who references this entity?
  5. Check timeline: recent events involving this entity

The brain almost always has something. External APIs fill gaps, not start from scratch.

Phase 2: On Every Inbound Signal (READ → ENRICH → WRITE)

Every message, meeting, email, or conversation that references a person or company:

  1. Detect entities — people, companies, deals mentioned
  2. Load brain pages — read existing pages for context before responding
  3. Identify new information — what does this signal tell us that the page doesn't know?
  4. Write it back — update the brain page with new info + timeline entry + source citation
  5. Create if missing — if notable and no page exists, create via enrich skill

User's direct statements are the highest-value data source. Write them to brain pages immediately with attribution [Source: User, YYYY-MM-DD].

Phase 2.5: Structured Graph Updates (automatic)

Every put_page call automatically extracts entity references and writes them to the graph (links table) with inferred relationship types. Stale links (refs no longer in the page text) are removed in the same call. This is "auto-link" reconciliation.

  • No manual add_link calls needed for ordinary page writes.
  • Inferred link types: attended (meeting -> person), works_at, invested_in, founded, advises, source (frontmatter), mentions (default).
  • The put_page MCP response includes auto_links: {created, removed, errors} so the agent can verify outcomes.
  • To disable: gbrain config set auto_link false. Default is on.
  • Timeline entries with specific dates still need explicit gbrain timeline-add (or batch via gbrain extract timeline --source db).

Phase 3: On Every Outbound Response (READ → PULL → RESPOND)

Before answering any question about a person, company, or topic:

  1. Check the brain — read relevant pages
  2. Pull context — use compiled truth + recent timeline
  3. Respond with context — the brain makes every answer better

Don't answer from general knowledge when a brain page exists.

Phase 4: Ambient Enrichment

This is not a special mode. This is the default. Everything the user says is an ingest event.

  • Person mentioned → check brain, create/enrich if needed (spawn background)
  • Company mentioned → same
  • Link shared → ingest it (delegate to idea-ingest)
  • Data shared → delegate to appropriate skill

Rules:

  • Never interrupt the conversation to do enrichment
  • Spawn sub-agents for anything that would slow down the response
  • Never announce "I'm enriching the brain" — just do it silently

Output Format

No separate output. Brain-ops is an always-on behavior layer, not a report generator. The output is updated brain pages and enriched responses.

Cross-source citation format (v0.18.0+)

When a brain has multiple sources (wiki, gstack, yc-media, etc.), every citation MUST include the source id: [source-id:slug]. Example:

You told me about the retry budget approach — see [wiki:topics/resilience] and [gstack:plans/retry-policy] for where this came from.

Rules:

  • The key is sources.id (immutable), never sources.name (mutable display).
  • Single-source brains still write [default:slug] OR may omit the prefix for backward compat.
  • Every page payload returned by search, query, get_page, list_pages carries source_id — always use it when citing, never guess.

If a search result has source_id: "gstack" and slug: "plans/foo", the citation is [gstack:plans/foo]. That's the whole rule.

Anti-Patterns

  • Answering questions about people/companies without checking the brain first
  • Using external APIs before checking the brain
  • Writing facts without inline [Source:...] citations
  • Blocking the response to do enrichment
  • Overwriting user's direct statements with lower-authority sources
  • Creating brain pages for non-notable entities

Tools Used

  • search — keyword search
  • query — hybrid vector+keyword search
  • get_page — read a brain page
  • put_page — create/update brain pages
  • add_link — cross-reference entities
  • add_timeline_entry — record events
  • get_backlinks — check who references an entity
  • sync_brain — sync changes to the index

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

36.35%
按下载量换算155

Claude

32.31%
按下载量换算138

Cursor

18.46%
按下载量换算79

Gemini CLI

9.73%
按下载量换算42

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/garrytan/gbrain --skill brain-ops 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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