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Agent Skill · 研究检索

brain-ops(大脑操作)

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。

brain-ops 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为执行命令;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 39,839。

总安装 1,360周安装 55GitHub Stars 12,485下载 427核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

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。

来源仓库内路径:skills/brain-ops

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/garrytan/gbrain --skill brain-ops;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

brain-ops 怎么安装?

在终端执行 npx skills add https://github.com/garrytan/gbrain --skill brain-ops。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

brain-ops 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

brain-ops 需要什么权限?

权限级别是执行命令。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。

brain-ops 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

brain-ops 用的人多吗?

公开数据是累计安装 1,360 次,近一周安装 55 次,下载 427 次,GitHub 12,485 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

brain-ops 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 来源仓库有 12,485 星,维护活跃度相对可查。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
brain-ops

大脑操作 · 本页

1,36012,485执行命令未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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

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。目前只有一个公开来源,建议同时核对源仓库。

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