session-intelligence-harvester

仓库: panaversity/agentfactory
安装量: 48
排名: #15341

安装

npx skills add https://github.com/panaversity/agentfactory --skill session-intelligence-harvester
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 Type
Target Component
Location
What to Add
When It Triggers
Context-gathering gaps
CLAUDE.md
Section I
New step in context protocol
Before ANY platform work
Failure mode example
CLAUDE.md
Failure modes
Named example with correction
When similar situation detected
Pedagogical framework
Constitution
Section IIa
Teaching method update
During lesson design
Agent convergence pattern
Agent file
Convergence Patterns
Pattern + why + correction
During agent execution
Agent self-monitoring
Agent file
Self-Monitoring Checklist
New checklist item
Before agent finalizes output
Canonical source lookup
Multiple agents
Analysis Questions
Cross-reference check
During planning phase
Reusable workflow
New skill
.claude/skills/
New SKILL.md
When user invokes skill
Orchestration check
Command file
Phase 0 or relevant phase
New validation step
During workflow execution
Format specification
Canonical source chapter
Lesson content
Authoritative format
When 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: Read the target file to understand current structure and find exact placement Generate the update with surrounding context for precise placement 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: Read the target file (if not already read) Locate the exact insertion point Apply the edit using Edit tool 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 Create PHR documenting: What was learned Where it was encoded (list all files) Why this improves future work 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
:
CLAUDE.md: Updated agent architecture section
skill-creator: Added domain organization requirement
session-intelligence-harvester: Updated routing table with domain paths
Moved all skills to authoring/ or engineering/
Moved all agents to authoring/ or engineering/
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
:
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 : CLAUDE.md: Added "Content Fact-Checking (MANDATORY)" section CLAUDE.md: Added failure mode example to Failure Prevention list 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
← 返回排行榜