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研究检索external-servicegithub未标认证来源可访问许可证需确认审计通过

update-knowledge-base更新知识库

Agent Skill

用于搭建或维护带检索增强的 RAG 工作流,适合让 Agent 处理知识库问答、向量检索、来源引用和事实核查。它可以辅助整理数据接入、Embedding、向量库、召回参数和回答生成流程。使用时需要确认数据来源、更新频率、召回阈值和引用展示方式,避免把未命中的资料或过期内容包装成确定事实。

总安装

309

周安装

13

GitHub Stars

374

下载量

108
CodexClaudeCursorGemini CLI

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:update-knowledge-base(更新知识库)
来源仓库:https://github.com/huytieu/cog-second-brain
仓库路径:skills/update-knowledge-base
安装命令:
npx skills add https://github.com/huytieu/cog-second-brain --skill update-knowledge-base
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/huytieu/cog-second-brain --skill update-knowledge-base

简介

用于搭建或维护带检索增强的 RAG 工作流。

  • 适合处理知识库问答、向量检索和来源引用核查。
  • 通过 GitHub 安装并使用 npx skills add 命令集成。
  • 需确认数据来源和召回阈值,避免将未命中内容包装成确定事实。
  • update-knowledge-base 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

COG Update Knowledge Base Skill

When to Invoke

  • After a release — update product knowledge with new features and changes
  • User says "update knowledge base", "update KB", "sync knowledge", "update product docs"
  • Periodic maintenance — ensure knowledge base reflects current product state
  • After major feature launches, architecture changes, or deprecations

Agent Mode Awareness

Check agent_mode in 00-inbox/MY-PROFILE.md frontmatter:

  • If agent_mode: team — use parallel agents to scan multiple sources and update multiple knowledge files simultaneously
  • If agent_mode: solo — process updates sequentially, one knowledge file at a time

Command: /update-knowledge-base

Pre-Flight Check

  1. Read 00-inbox/MY-INTEGRATIONS.md for active data sources and publishing targets
  2. Read 00-inbox/MY-PROFILE.md for active projects
  3. Get current timestamp: Run date '+%Y-%m-%d %H:%M' using Bash
  4. Scan existing knowledge base: Glob 05-knowledge/**/*.md to understand current state

Execution Strategy

Phase 1: Determine Update Scope

Ask the user (or infer from context):

What triggered this knowledge base update?

a) New release shipped (I'll pull changes from release notes and tracker)
b) Feature launched or updated (I'll document the feature)
c) Architecture or technical change (I'll update technical knowledge)
d) Periodic review (I'll scan for anything that's changed since last update)
e) Custom — describe what needs updating

Phase 2: Gather Update Data

Team Mode (parallel agents)

Launch data-gathering agents using the Task tool with run_in_background: true:

Agent: "release-scanner" (for release-triggered updates)

Scan recent releases and release notes for knowledge base updates.

1. Check vault for recent release notes:
   Glob: 04-projects/*/release-notes/*.md
   Read the most recent release notes for each active project

2. If GitHub is active:
   gh release list --repo [CUSTOMIZE: your-org/your-repo] --limit 5 --json tagName,name,body,publishedAt
   Get the latest release details

3. If Linear is active:
   Use ToolSearch to load Linear tools
   Check recently completed cycles and milestones for shipped features

Extract from each source:
- New features added (name, description, user impact)
- Features modified (what changed)
- Features deprecated or removed
- Technical changes (architecture, API, infrastructure)
- Bug fixes that affect documented behavior

Return: structured list of knowledge updates needed

Agent: "existing-kb-auditor"

Audit the current knowledge base for staleness and gaps.

1. Read all files in 05-knowledge/:
   Glob: 05-knowledge/**/*.md

2. For each file, extract:
   - Last updated date (from frontmatter or file modification)
   - Topics covered
   - Products/features documented
   - Any [TODO] or [OUTDATED] markers

3. Cross-reference with active projects from MY-PROFILE.md:
   - Are all active projects represented in the KB?
   - Are there KB entries for discontinued/inactive projects?

4. Check for:
   - Files not updated in >90 days (potentially stale)
   - Topics mentioned in recent release notes but missing from KB
   - Contradictions between KB entries and recent changes

Return: staleness report, gap analysis, and recommended updates

Agent: "tracker-feature-collector" (for feature-triggered updates)

Collect current feature/product state from project trackers.

If Linear is active:
1. Use ToolSearch to load Linear tools
2. List all projects and their current status: mcp__claude_ai_Linear_2__list_projects
3. List all initiatives: mcp__claude_ai_Linear_2__list_initiatives
4. For key initiatives, get details: mcp__claude_ai_Linear_2__get_initiative

If GitHub is active:
1. Check repo README and docs for current feature descriptions
2. Review recent PRs that touch documentation:
   gh pr list --repo [CUSTOMIZE: your-org/your-repo] --state merged --search "label:docs merged:>=[30_DAYS_AGO]" --json number,title,body --limit 20

Return: current product/feature state for knowledge base comparison

Solo Mode

Run the most relevant agent sequentially based on the update trigger.

Phase 3: Identify Knowledge Updates

Compare gathered data against existing knowledge base to determine:

  1. New entries needed — features, products, or topics not yet documented
  2. Updates needed — existing entries that need modification
  3. Deprecation/removal — entries for features that no longer exist
  4. Gap filling — missing context, examples, or cross-references

Present the update plan to the user:

Knowledge Base Update Plan:

NEW entries to create:
1. [Topic] — [reason: new feature in v[X]]
2. [Topic] — [reason: gap identified]

UPDATES to existing entries:
1. 05-knowledge/[file].md — [what needs changing]
2. 05-knowledge/[file].md — [what needs changing]

DEPRECATION candidates:
1. 05-knowledge/[file].md — [reason: feature removed in v[X]]

STALE entries (>90 days, may need review):
1. 05-knowledge/[file].md — last updated [date]

Proceed with these updates? (yes / modify plan / skip specific items)

Wait for user approval before making changes.

Phase 4: Execute Updates

4.1 Create New Knowledge Entries

For each new entry, use this template:

---
type: knowledge
domain: [product/technical/process/architecture]
project: [project-name]
topic: [topic name]
created: [YYYY-MM-DD HH:MM]
last_updated: [YYYY-MM-DD]
source: [release-notes/feature-launch/manual/periodic-review]
version: "1.0"
tags: ["#knowledge", "#[project]", "#[topic-area]"]
related:
  - [path to related KB entry]
  - [path to related project file]
---

# [Topic Title]

## Overview
[Clear, concise description of the topic — what it is, why it matters]

## Current State
[How this feature/system/process works as of the last update]

### Key Details
- **[Detail 1]:** [Value/description]
- **[Detail 2]:** [Value/description]
- **[Detail 3]:** [Value/description]

## History
| Date | Version | Change | Source |
|------|---------|--------|--------|
| [Date] | [Version] | [What changed] | [Release notes / PR / manual] |

## Related
- [Link to related KB entries]
- [Link to PRDs if applicable]
- [Link to project files]

## Notes
[Additional context, caveats, or open questions]

---

*Last updated: [YYYY-MM-DD] | Source: [what triggered this update]*

Save to: 05-knowledge/[domain]/[topic-slug].md

mkdir -p "05-knowledge/[domain]"

4.2 Update Existing Entries

For each update:

  1. Read the existing file
  2. Update the relevant sections
  3. Add a row to the History table
  4. Update last_updated in frontmatter
  5. Update version (increment minor version)

4.3 Mark Deprecations

For deprecated entries:

  1. Add status: deprecated to frontmatter
  2. Add a deprecation notice at the top of the file: > **DEPRECATED:** This feature was removed/replaced in [version/date]. See [replacement link] for the current approach.
  3. Do NOT delete the file — historical context is valuable

Phase 5: Generate Update Summary

Create an update log:

---
type: kb-update-log
date: [YYYY-MM-DD]
created: [YYYY-MM-DD HH:MM]
trigger: [release/feature/periodic/manual]
tags: ["#knowledge", "#update-log"]
---

# Knowledge Base Update — [YYYY-MM-DD]

## Trigger
[What caused this update: release X.Y.Z / feature launch / periodic review]

## Changes Made

### New Entries
| File | Topic | Domain | Source |
|------|-------|--------|--------|
| [path] | [topic] | [domain] | [source] |

### Updated Entries
| File | Changes | Previous Version | New Version |
|------|---------|-----------------|-------------|
| [path] | [summary of changes] | [old ver] | [new ver] |

### Deprecated Entries
| File | Reason | Replacement |
|------|--------|-------------|
| [path] | [reason] | [link to replacement or N/A] |

## Knowledge Base Health
- **Total entries:** [N]
- **Updated this session:** [N]
- **Created this session:** [N]
- **Deprecated this session:** [N]
- **Stale entries remaining (>90 days):** [N]
- **Coverage:** [assessment of how well the KB covers active projects]

---

*Generated by COG Knowledge Base Updater*

Save to: 05-knowledge/_logs/kb-update-YYYY-MM-DD.md

mkdir -p "05-knowledge/_logs"

Phase 6: Optional Wiki Sync (requires approval)

If Confluence or Notion is active, offer to sync updated entries:

Knowledge base updated locally. Would you like to sync to your wiki?

Active wiki platforms:
- [Confluence / Notion — whichever is active]

Options:
a) Sync all changed entries to [platform]
b) Sync specific entries only
c) Skip wiki sync (vault-only)

NEVER auto-publish. Wait for explicit approval.

Confluence Sync

Use the /publish-to-confluence skill pattern for each entry being synced.

Notion Sync

1. Use ToolSearch to load Notion tools
2. Search for existing pages matching the KB entry: mcp__claude_ai_Notion__notion-search
3. If exists: update with mcp__claude_ai_Notion__notion-update-page
4. If new: create with mcp__claude_ai_Notion__notion-create-pages

Knowledge Base Organization

Recommended 05-knowledge/ structure:

05-knowledge/
  _logs/                    # Update logs
    kb-update-YYYY-MM-DD.md
  product/                  # Product features, capabilities, roadmap
    [feature-name].md
  technical/                # Architecture, APIs, infrastructure
    [system-name].md
  process/                  # Team processes, workflows, conventions
    [process-name].md
  domain/                   # Domain knowledge, industry context
    [topic-name].md
  integrations/             # Integration docs, API references
    [integration-name].md

Fallback Behavior

ScenarioBehavior
No trackers activeWork from vault data only (release notes, PRDs, project files)
No existing KB entriesCreate the initial knowledge base structure and first entries
Wiki sync failsAll changes are already saved locally; inform user to sync manually
Very large KB (>50 files)Process in batches, prioritize most recently changed entries
No recent changes foundReport that KB is up to date; suggest periodic review topics
Conflicting informationFlag conflicts for user resolution rather than auto-resolving

Error Handling

  • File conflicts: If an update would contradict a recent manual edit, present both versions to the user
  • Context overflow: Process knowledge files in batches of 10
  • Stale data sources: Warn if tracker data seems outdated (API issues)
  • Missing project context: Ask user to clarify which project the knowledge relates to

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。

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