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Agent Skill · 研究检索

knowledge-capture(知识获取)

knowledge-capture 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。

knowledge-capture 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,权限为需要联网;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 65,351。

总安装 282周安装 12GitHub Stars 25下载 99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:knowledge-capture(知识获取)
来源仓库:https://github.com/noobygains/godmode
仓库路径:skills/knowledge-capture
安装命令:
npx skills add https://github.com/noobygains/godmode --skill knowledge-capture
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/noobygains/godmode --skill knowledge-capture

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。

knowledge-capture 用于查找、检索和筛选相关信息,适合基于关键词快速定位内容。适用于 Codex、Claude、Cursor 和 Gemini CLI,支持多种任务场景下的信息聚合。可通过来源仓库和原始 README 进一步验证功能细节。安装前应确认是否会触发联网、命令执行或文件读写操作。

建议在测试环境中验证后再用于关键知识管理任务。

来源仓库内路径:skills/knowledge-capture

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/noobygains/godmode --skill knowledge-capture;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

    它需要需要联网,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

knowledge-capture 怎么安装?

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

knowledge-capture 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。

knowledge-capture 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

knowledge-capture 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。

knowledge-capture 用的人多吗?

公开数据是累计安装 282 次,近一周安装 12 次,下载 99 次,GitHub 25 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。

knowledge-capture 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。

先想清楚的地方

  • 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

  • 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
knowledge-capture

知识获取 · 本页

28225需要联网未明确通过

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

Knowledge Capture

Overview

Every substantial interaction yields signal. A debugging session exposes a hidden codebase convention. A user correction reveals a preference. A failed plan uncovers a blind spot. Recording these observations is not overhead -- it is compound interest on capability.

Core principle: Solving a difficult problem without recording what you discovered means solving it from zero next time.

No exceptions. No workarounds. No shortcuts.

The Prime Directive

EXTRACT INSIGHT FROM EVERY MEANINGFUL INTERACTION

An interaction that ends without reflection is a wasted investment. You possessed the context, found the answer, observed what succeeded -- then discarded all of it.

No excuses:

  • Do not skip capture because the task felt "routine"
  • Do not skip capture because you are "about to switch contexts"
  • Do not skip capture because the lesson seems "self-evident"
  • What feels obvious with full context becomes invisible from a cold start

Reflect. Distill. Persist. Full stop.

When to Use

Mandatory after:

  • Delivering a complex feature (which patterns proved effective?)
  • Resolving a stubborn defect (what was the root-cause signature?)
  • Receiving review feedback that surfaced issues (what should differ next time?)
  • Being corrected by the user (new preference discovered)
  • Revising a plan significantly (what was misjudged?)
  • Discovering a codebase convention through trial and error (spare the next session that journey)

Exceptions (confirm with the human):

  • One-line trivial fixes
  • Interactions that produced no new information
  • When the user explicitly declines

Tempted to think "there is nothing to capture here"? Pause. That is rationalization.

The Entry Protocol

AFTER completing any meaningful task:

1. REFLECT: What succeeded? What failed? What was unexpected?
2. DISTILL: What generalizable pattern or insight emerges?
3. DEDUPLICATE: Is this already recorded? Does it contradict something stored?
4. PERSIST: Write it to the appropriate memory file with date and context
5. PRUNE: Remove any prior entries that newer evidence invalidates

Omit any step = insight permanently lost

The Capture Lifecycle

digraph capture_lifecycle {
    rankdir=TB;
    node [shape=box style=filled];

    task [label="Meaningful Task\nCompleted" fillcolor=lightyellow shape=doublecircle];

    reflect [label="REFLECT\nWhat occurred?\nWhat surprised me?" fillcolor="#ccccff"];
    distill [label="DISTILL\nAbstract the insight\nfrom the specifics" fillcolor="#ccccff"];
    exists [label="Already\nrecorded?" shape=diamond fillcolor="#fff3e0"];
    conflicts [label="Contradicts\nan existing entry?" shape=diamond fillcolor="#fff3e0"];
    persist [label="PERSIST\nSave to memory\nwith date + context" fillcolor="#ccffcc"];
    prune [label="PRUNE\nRemove outdated\nentry" fillcolor="#ffcccc"];
    cluster [label="CLUSTER\n3+ related insights?\nForm a conviction" fillcolor="#ccffcc"];
    promote [label="PROMOTE\nHigh-confidence conviction\n-> CLAUDE.md rule" fillcolor="#e8f5e9"];
    skip [label="Skip\n(already known)" fillcolor="#eeeeee"];

    task -> reflect;
    reflect -> distill;
    distill -> exists;
    exists -> skip [label="yes, identical"];
    exists -> conflicts [label="no"];
    conflicts -> prune [label="yes"];
    prune -> persist;
    conflicts -> persist [label="no"];
    persist -> cluster;
    cluster -> promote [label="3+ confirmations"];
    cluster -> task [label="await further\nevidence" style=dashed];
}

Categories of Insight

Effective Patterns

Code approaches, debugging tactics, and architectural choices that led to clean results. These are positive signals to reinforce.

Sample entry:
  Date: 2025-04-10
  Context: Built retry logic for third-party webhook delivery
  Insight: Exponential backoff with random jitter eliminated thundering-herd spikes
  Confidence: medium (validated once)

Failures and Their Remedies

What went wrong, how it was resolved, how to prevent recurrence. Failures yield the highest-signal lessons.

Sample entry:
  Date: 2025-04-12
  Context: Logging middleware silently swallowed request bodies after refactor
  Insight: Any change to middleware ordering demands a full integration test pass -- blast radius is total
  Confidence: high (validated by production incident)

User Preferences

Stylistic choices, tool preferences, and conventions the user follows. Discovered through corrections and direct statements.

Sample entry:
  Date: 2025-04-13
  Context: User corrected my naming approach
  Insight: User requires camelCase for variables, PascalCase for types, and no abbreviations anywhere
  Confidence: high (direct correction)

Project-Specific Knowledge

Architecture details, hidden gotchas, critical files, tribal knowledge that lives nowhere in documentation.

Sample entry:
  Date: 2025-04-14
  Context: Spent 15 minutes searching for runtime config
  Insight: All runtime configuration lives in src/config/runtime.ts, not in .env files
  Confidence: high (verified in source)

Storage Mechanism

Leverage Claude Code's memory system: ~/.claude/projects/[project]/memory/

Memory File Taxonomy

FilePurposeExample Entry
effective-patterns.mdCode approaches and strategies that produced clean outcomes"Zod schemas at API boundaries catch malformed data before it propagates"
failure-analysis.mdRoot-cause patterns and diagnostic techniques"Tests passing locally but failing in CI usually indicate timezone or locale assumptions"
project-map.mdArchitecture, key files, and codebase conventions"Database migrations reside in db/migrations/ and execute via pnpm db:migrate"
human-preferences.mdStylistic choices, tooling preferences, and conventions the user enforces"User demands explicit error types; string-based errors are rejected"

Entry Structure

Every captured insight must include:

### [Concise title]
- **Date:** YYYY-MM-DD
- **Context:** What was happening when this was discovered
- **Insight:** The generalized takeaway
- **Confidence:** low / medium / high
- **Confirmations:** Number of times this has been validated

Deduplication Protocol

Before writing a new entry, scan the target memory file. If the insight already exists:

  • Same insight, same confidence level -> skip entirely
  • Same insight, elevated confidence -> update confidence and increment confirmation count
  • Contradicting insight -> replace the old entry, note the contradiction

The Maturation Cycle

Isolated insights become powerful when they converge into convictions.

From Insight to Conviction

Insight 1: "This project validates forms with zod" (medium confidence)
Insight 2: "API route handlers also validate payloads with zod" (medium confidence)
Insight 3: "User corrected me for using manual validation instead of zod" (high confidence)

    Converge into conviction

Conviction: "This project ALWAYS validates at every boundary using zod"
Confidence: high (confirmed across 3 interactions)

From Conviction to Rule

When a conviction reaches high confidence (confirmed across 3+ interactions), it becomes eligible for promotion to a project CLAUDE.md rule:

Conviction: "This project always validates with zod"
  -> Confirmed 3+ times
  -> Propose to user: "I have observed that this project consistently
    uses zod for validation. Should I codify this as a project rule in CLAUDE.md?"
  -> User approves -> add to CLAUDE.md

Never auto-promote. Always obtain user consent before adding entries to CLAUDE.md. The human is the final authority on permanent rules.

Session-Start Review

When beginning a new session on a project:

  1. Read all memory files for the project
  2. Identify clusters of related insights that have not been synthesized
  3. Flag entries that may be stale (old date, low confidence, never reconfirmed)
  4. Surface convictions ready for promotion

Confidence Tiers

TierMeaningOriginAction
LowTentative signalSingle occurrence, unconfirmedRecord, watch for confirmation
MediumProbable patternConfirmed twice, or one strong indicatorRecord, apply when relevant
HighEstablished truthConfirmed 3+ times, or explicit user declarationRecord, apply consistently, consider promotion

Cognitive Traps

RationalizationTruth
"There is nothing to capture here"Every task produces signal. You are not examining closely enough.
"I will recall this naturally"You will not. The next session starts with a blank slate. Memory files are your continuity.
"Too minor to bother recording"Minor insights accumulate. Three small observations become one powerful conviction.
"It is self-evident"Self-evident to you now, with full context. Not self-evident when cold-starting next week.
"Recording takes too long"Thirty seconds to write an entry. Thirty minutes to rediscover the same insight.
"The user did not request this"They requested quality. Learning from experience IS how quality improves over time.
"Memory files are getting cluttered"That is a pruning problem, not a recording problem. Prune more; never stop capturing.
"This only applies to this one project"Project-specific knowledge is the MOST valuable kind. That is precisely why it is stored per-project.

Guardrails

Prohibited actions:

  • Skipping reflection after a difficult debugging session
  • Storing entries without date and context (undated insights decay)
  • Auto-adding rules to CLAUDE.md without user approval
  • Recording the same insight twice without deduplication
  • Retaining entries that newer evidence contradicts
  • Recording implementation minutiae instead of generalizable patterns

Required actions:

  • Reflect after completing any significant work
  • Check memory before persisting (deduplicate)
  • Attach confidence levels to every entry
  • Obtain user consent before promoting convictions to CLAUDE.md rules
  • Prune entries invalidated by newer evidence
  • Organize entries by topic, not chronologically

Integration

This skill feeds INTO other skills:

  • godmode:fault-diagnosis -- Historical failure analysis informs current troubleshooting
  • godmode:pattern-matching -- Captured conventions reinforce code conformity
  • godmode:test-first -- Past test failures shape future test strategy
  • godmode:task-planning -- Past plan failures prevent repeating misjudgments
  • godmode:quality-enforcement -- Captured quality patterns elevate the baseline

This skill is fed BY other skills:

  • godmode:review-response -- Review feedback becomes captured insights
  • godmode:completion-gate -- Verification failures become recorded lessons
  • godmode:comprehension-check -- Walkthrough findings become stored observations
  • godmode:task-runner -- Plan execution reveals what works and what does not

The virtuous cycle:

Work -> Capture -> Persist -> Apply -> Work better -> Capture more

平台分布

Codex

35.83%
按下载量换算35

Claude

28.41%
按下载量换算28

Cursor

18.76%
按下载量换算19

Gemini CLI

8.82%
按下载量换算9

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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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