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Agent Skill · 研究检索

session-intelligence-harvester(会话情报收集器)

session-intelligence-harvester 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。

session-intelligence-harvester 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,权限为只读;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 41,772。

总安装 1,140周安装 48GitHub Stars 158下载 399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:session-intelligence-harvester(会话情报收集器)
来源仓库:https://github.com/panaversity/agentfactory
仓库路径:skills/session-intelligence-harvester
安装命令:
npx skills add https://github.com/panaversity/agentfactory --skill session-intelligence-harvester
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/panaversity/agentfactory --skill session-intelligence-harvester

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。

session-intelligence-harvester 用于查找、检索和筛选相关信息。适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词、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。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/session-intelligence-harvester

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/panaversity/agentfactory --skill session-intel…;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

session-intelligence-harvester 怎么安装?

在终端执行 npx skills add https://github.com/panaversity/agentfactory --skill session-intelligence-ha…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

session-intelligence-harvester 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。

session-intelligence-harvester 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

session-intelligence-harvester 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。

session-intelligence-harvester 用的人多吗?

公开数据是累计安装 1,140 次,近一周安装 48 次,下载 399 次,GitHub 158 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。

session-intelligence-harvester 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。
  • 标注为只读权限,试用阶段不容易误改本地文件。

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。
  • 只想先试试效果、不希望它改动本地文件的人。

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
session-intelligence-harvester

会话情报收集器 · 本页

1,140158只读未明确通过

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

Session Intelligence Harvester

Overview

Transform session learnings into permanent organizational intelligence by implementing updates across RII components. After productive sessions involving corrections, discoveries, or pattern identification, systematically extract what was learned, route it to the correct component, and apply the changes.

Why this matters: One-time fixes that aren't encoded into RII components will recur. The Chapter N skill format drift happened because no check existed to prevent it. After harvesting, that failure mode is encoded in 4 files—future sessions automatically benefit.

When to Use This Skill

Automatic Triggers (proactively suggest harvesting):

  • Session corrected format drift (wrong file structure, YAML, invocation)
  • Session added missing checks to orchestration files
  • Session identified failure mode worth preventing
  • Session touched 3+ files with similar pattern corrections
  • PHR was created documenting significant learning

Manual Triggers (user requests):

  • "Harvest learnings from this session"
  • "Capture session intelligence"
  • "What should we encode from this work?"
  • "Update RII with what we learned"

Workflow

Default to action: Implement all updates rather than only proposing them. Read target files, make edits, and commit changes. Only propose without implementing if explicitly asked.

Step 1: Session Analysis

Analyze the session by answering these questions. Write your analysis to track progress:

1. CORRECTIONS MADE
   - What errors/drift were corrected?
   - What was wrong vs what is now correct?
   - Why did the error occur? (missing check, format drift, etc.)

   WHY THIS MATTERS: Understanding root cause determines which RII
   component prevents recurrence. Format drift → agent convergence
   pattern. Missing context → CLAUDE.md protocol step.

2. PATTERNS IDENTIFIED
   - What recurring patterns emerged?
   - What canonical sources were referenced?
   - What validation would have caught this earlier?

   WHY THIS MATTERS: Patterns that recur across sessions deserve
   encoding. If you referenced a canonical source, other sessions
   will need that same reference.

3. LEARNING CLASSIFICATION
   - Context-gathering gap? (CLAUDE.md)
   - Pedagogical/teaching issue? (Constitution)
   - Agent convergence pattern? (Agents)
   - Reusable workflow? (Skills)
   - Missing orchestration check? (Commands)

   WHY THIS MATTERS: Wrong routing means learnings don't trigger at
   the right time. A convergence pattern in CLAUDE.md won't help
   chapter-planner catch it during planning.

Step 2: Route to RII Components

Use this routing table. Route learnings to the component where they will be discovered at the right time:

Learning TypeTarget ComponentLocationWhat to AddWhen It Triggers
Context-gathering gapsCLAUDE.mdSection INew step in context protocolBefore ANY platform work
Failure mode exampleCLAUDE.mdFailure modesNamed example with correctionWhen similar situation detected
Pedagogical frameworkConstitutionSection IIaTeaching method updateDuring lesson design
Agent convergence patternAgent fileConvergence PatternsPattern + why + correctionDuring agent execution
Agent self-monitoringAgent fileSelf-Monitoring ChecklistNew checklist itemBefore agent finalizes output
Canonical source lookupMultiple agentsAnalysis QuestionsCross-reference checkDuring planning phase
Reusable workflowNew skill.claude/skills/New SKILL.mdWhen user invokes skill
Orchestration checkCommand filePhase 0 or relevant phaseNew validation stepDuring workflow execution
Format specificationCanonical source chapterLesson contentAuthoritative formatWhen teaching that pattern

WHY ROUTING MATTERS: Learnings placed in the wrong component don't prevent recurrence. A check in content-implementer.md won't help if the error happens during chapter-planner execution.

Step 3: Read Target Files and Generate Updates

For each identified learning:

  1. Read the target file to understand current structure and find exact placement
  2. Generate the update with surrounding context for precise placement
  3. Track progress as you work through multiple files
## Learning: [Brief Title]

**Type**: [Context gap | Failure mode | Convergence pattern | etc.]

**Target**: [File path]

**Current State** (after reading file):
[What's missing or incorrect - quote existing content if helpful]

**Exact Placement**:
[Which section, after which content - be specific enough to Edit]

**Content to Add**:
[Exact content, matching the file's style and format]

**Rationale**:
[Why this prevents recurrence - what will trigger this check]

**Canonical Source** (if applicable):
[Which chapter/lesson defines the authoritative format]

Step 4: Implement Updates

Take action: Edit each target file. Use the Edit tool to make changes.

For each update:

  1. Read the target file (if not already read)
  2. Locate the exact insertion point
  3. Apply the edit using Edit tool
  4. Verify the edit was applied correctly

Track completion:

Updates Progress:
- [x] CLAUDE.md - Added failure mode section
- [x] chapter-planner.md - Added convergence pattern 6
- [x] sp.loopflow.v2.md - Removed (deprecated, replaced by native Plan Mode)
- [ ] content-implementer.md - Pending

Step 5: Validation

Before finalizing, verify each of these (check the box as you confirm):

- [ ] Read each target file before editing (no speculation about structure)
- [ ] Verified each learning routes to the component where it triggers at the right time
- [ ] Confirmed updates include exact placement context (not vague locations)
- [ ] Checked canonical sources exist for format-related learnings
- [ ] Searched target files to confirm no duplicate information exists
- [ ] Used imperative form for agent files, appropriate style for others
- [ ] Added cross-references where pattern appears in multiple files
- [ ] All edits applied successfully (no pending changes)

Step 6: Create PHR and Commit

  1. Create PHR documenting:

- What was learned - Where it was encoded (list all files) - Why this improves future work

  1. Commit changes with descriptive message: feat(intelligence): Harvest session learnings into RII Updates: - [File 1]: [What was added] - [File 2]: [What was added] Prevents: [What failure mode this prevents]

RII Component Reference

CLAUDE.md Structure

Section I: Context Gathering Protocol
  - Step 1-N: Sequential context steps
  - Each step has WHAT to do and WHY it matters
  - "Find canonical source" step for pattern teaching

Failure Modes (between Section I and II):
  - Named failure examples: "FAILURE MODE: [Name] Example"
  - "What I did wrong" list
  - "What I should have done" numbered steps
  - "Result" showing what was prevented

Agent File Structure

Analysis Questions (Section III):
  - Numbered questions with "Why this matters" explanation
  - Self-check prompt at end

Principles (Section IV):
  - Named principles with Framework + What this means + Application guidance
  - Self-check prompt

Convergence Patterns (Section VI):
  - "Generic pattern" description
  - "Why this is convergence" explanation
  - "Correction" with specific steps

Self-Monitoring Checklist (Section VIII):
  - Numbered checklist with checkmark emoji prefix
  - Each item is a verification question

Command File Structure

Phase 0: Foundation checks
  - Constitutional reading
  - Canonical source checks (for educational content)
  - Each step explains WHY

Convergence Patterns:
  - Symptom description
  - Detection method
  - Correction steps

Output Format

After completing harvest, provide summary:

## Session Intelligence Harvest Summary

**Session**: [Brief description]
**Date**: [ISO date]
**Status**: COMPLETE

### Learnings Extracted: [N]

| #   | Learning | Type   | Target | Status  |
| --- | -------- | ------ | ------ | ------- |
| 1   | [Title]  | [Type] | [File] | Applied |
| 2   | [Title]  | [Type] | [File] | Applied |

### Updates Applied

1. **[File]**: [What was added] (lines X-Y)
2. **[File]**: [What was added] (lines X-Y)

### PHR Created

- Path: [PHR path]
- Stage: [Stage]

### Canonical Sources Referenced

- [Pattern]: [Chapter X Lesson Y]

### Commit

- Hash: [commit hash]
- Message: [commit message summary]

Examples

Example 1: Format Drift Correction (Multi-File)

Session: Fixed skill format to use domain-based structure

Analysis:

CORRECTIONS MADE:
- Wrong: .claude/skills/section-writer.md (flat file, no domain)
- Correct: .claude/skills/authoring/section-writer/SKILL.md (domain + directory structure)
- Root cause: No domain organization for skills/agents

PATTERNS IDENTIFIED:
- Skills must be in authoring/ or engineering/ domain folders
- Agents must be in authoring/ or engineering/ domain folders
- Multiple files referenced old flat structure

CLASSIFICATION:
- Failure mode → CLAUDE.md
- Convergence pattern → chapter-planner.md, content-implementer.md
- Skill structure → skill-creator, session-intelligence-harvester

Updates Applied:

  1. CLAUDE.md: Updated agent architecture section
  2. skill-creator: Added domain organization requirement
  3. session-intelligence-harvester: Updated routing table with domain paths
  4. Moved all skills to authoring/ or engineering/
  5. Moved all agents to authoring/ or engineering/
  6. Generals Skills are at.claude/skills/

Example 2: Missing Validation (Single File)

Session: Discovered lessons weren't checking chapter-index.md for prerequisites

Analysis:

CORRECTIONS MADE:
- Wrong: Started chapter work without reading chapter-index.md
- Correct: MUST read chapter-index.md first to get Part, proficiency, prerequisites
- Root cause: No mandatory step in context protocol

CLASSIFICATION:
- Context-gathering gap → CLAUDE.md Section I

Updates Applied:

  1. CLAUDE.md: Added Step 1 to read chapter-index.md with specific extraction requirements

Example 3: Hallucinated Facts (Chapter 2 Incident)

Session: Wrote 6 lessons with unverified statistics, dates, and adoption numbers

Analysis:

CORRECTIONS MADE:
- Wrong: "50-75% time savings" for goose
- Correct: "75% of engineers save 8-10+ hours/week" (verified via Block announcement)

- Wrong: Conflated Agent Skills timeline (said single date)
- Correct: Oct 16, 2025 = Claude Code launch; Dec 18, 2025 = open standard release

- Wrong: Generic agent support lists
- Correct: Verified lists from official AAIF announcement

- Root cause: Trusted plausible-sounding data from memory instead of web verification

PATTERNS IDENTIFIED:
- Statistics, dates, and quotes MUST be web-verified before publication
- Existing factual-verifier agent was available but not used
- 50% of session time was spent fixing hallucinated facts

CLASSIFICATION:
- Critical failure mode → CLAUDE.md Failure Prevention + new section
- Process gap → Missing mandatory fact-check step

Updates Applied:

  1. CLAUDE.md: Added "Content Fact-Checking (MANDATORY)" section
  2. CLAUDE.md: Added failure mode example to Failure Prevention list
  3. Documented factual-verifier agent invocation pattern

Self-Monitoring

Before marking harvest complete, verify you have:

  • Analyzed session to identify all corrections and patterns
  • Classified each learning to determine correct routing
  • Read each target file before editing (no speculation)
  • Applied all edits using Edit tool (not just proposed)
  • Verified edits match target file's style and structure
  • Added cross-references where patterns appear in multiple files
  • Created PHR documenting the harvest
  • Committed changes with descriptive message
  • Generated summary showing all updates applied

平台分布

Codex

33.75%
按下载量换算135

Claude

31.96%
按下载量换算128

Cursor

17.7%
按下载量换算71

Gemini CLI

8.5%
按下载量换算34

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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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