Learning Capture

This skill provides patterns for capturing learnings at the right moments and routing them to the right places. Learnings compound over time - the more you capture, the more future work benefits.

Three Trigger Points

Learnings should be captured at three key moments, each with different depth and focus:

1. After Chat Session (Lightweight)

When: At the end of any significant agentic coding session

Focus: Quick capture of immediate insights

• What did we learn?
• What should be documented?
• Any quick process improvements?

Depth: Brief, 2-5 bullet points Time: 2-5 minutes

Example Output:

## Session Learning: 2026-01-25

- Discovered that the auth middleware requires explicit error handling
- Found undocumented API endpoint at `/internal/health`
- Process improvement: Always check existing tests before writing new ones

Session Handoff (if work continues later): Also capture state for the next session:

• What's done and what's next
• Decisions made with reasoning
• Docs that need updating (stale conclusions)
• Where to start next session

2. After Project Completion (Comprehensive)

When: At project milestones or completion

Focus: Thorough retrospective

• What worked well?
• What didn't work?
• What to do differently next time?

Depth: Full retrospective with multiple sections Time: 15-30 minutes

Sections to Cover:

• Project summary and outcomes
• What went well (successes, effective practices)
• What could be improved (challenges, pain points)
• Process improvements (suggested changes)
• Template refinements (updates needed)
• Action items (next steps)

3. After Overcoming Blockers (Targeted)

When: Immediately after solving a difficult problem

Focus: Capture while fresh

• What was painful?
• How was it solved?
• How to prevent next time?

Depth: Focused on the specific blocker Time: 5-10 minutes

Sections to Cover:

• Context: What were you trying to do?
• Initial hypothesis: What did you think was wrong?
• Actual root cause: What was actually wrong?
• Solution: How was it fixed?
• Prevention: How to avoid this in the future?

Two Output Targets

Learnings can improve two distinct areas:

Target 1: Codebase Documentation

What goes here: Project-specific knowledge

• Patterns and gotchas for this codebase
• Architecture decisions and rationale
• Debugging solutions for this project
• Troubleshooting guides
• API quirks and workarounds

Location: [current-codebase]/docs/ (appropriate subfolder)

Subfolder Guidelines:

Content Type	Location
Problem solutions	docs/solutions/
General learnings	docs/learnings/
Architecture decisions	docs/architecture/
Troubleshooting	docs/troubleshooting/

Target 2: Plugin/Workflow Improvements

What goes here: Process and tool improvements

• Workflow optimizations
• New patterns to add to skills
• Command/agent refinements
• Template improvements
• Cross-project patterns

Location: Plugin repository (commands, skills, or docs)

Improvement Types:

Type	Action
Command enhancement	Update command markdown
New skill pattern	Add to skill SKILL.md
Template fix	Update resources/templates/
New workflow	Create new command or skill

Deep Session Analysis Patterns

When performing a deep retrospective (analyzing SpecStory session files in .specstory/history/), use these patterns to extract insights the agent missed during the session.

Pattern: Repetition Detection

Signal: Same action performed multiple times without progress. How to detect: Scan for repeated file reads (same path 3+ times), repeated errors, repeated user corrections, and solution/revert cycles. What it means: The agent lacked context, had a faulty mental model, or didn't learn from previous attempts. Improvement: Add the missing context to CLAUDE.md or MEMORY.md so future sessions start with it.

Pattern: Frustration Signal Detection

Signal: User communication shifts from collaborative to directive. Severity scale:

1. Mild — User provides more specific instructions than before
2. Moderate — Short corrective phrases ("no", "not that", "just X")
3. Strong — User takes over the task themselves
4. Severe — User explicitly states frustration Improvement: Identify the specific mismatch and encode it as a behavioral rule.

Pattern: Wasted Effort Detection

Signal: Work that didn't contribute to the final outcome. How to detect: Compare final git diff to all changes during session, look for "let me try a different approach" patterns, count debugging cycles per issue (>2 = wasted). Improvement: Document the correct approach so future sessions don't repeat wrong paths.

Pattern: Scope Drift Detection

Signal: Work expanded significantly beyond original request. How to detect: Compare first user message to final summary. Count files touched vs expected. Improvement: If unintentional, add scope-check trigger to MEMORY.md.

Pattern: CLAUDE.md Growth

Signal: CLAUDE.md size exceeds 32,000 chars or triggers the performance warning (>40k). How to detect: Run wc -c CLAUDE.md. Check for RESOLVED issues still inline, niche guides that belong in docs/guides/, duplicate content (e.g., test commands listed in both Quick Reference and a Common Workflows section). Root cause: The learnings promotion workflow is additive-only — it promotes content UP to CLAUDE.md but never demotes stale content DOWN to docs/guides/ or docs/learnings/. Improvement: Before promoting new learnings to CLAUDE.md, check size and archive stale content. Resolved issues should be moved to docs/learnings/resolved-issues.md. Niche guides should be moved to docs/guides/ with a 1-line reference in CLAUDE.md.

---

YAML Frontmatter Schema

All learnings should include searchable frontmatter:

---
title: "Brief descriptive title"
date: YYYY-MM-DD
trigger: chat-session | project-completion | blocker-overcome
target: codebase | plugin | both
category: performance | database | integration | workflow | debugging | testing | security | design | generation | infrastructure
tags: [relevant, searchable, keywords]
severity: critical | high | medium | low
module: "affected_module_name"
---

Field Guidelines

trigger: Which trigger point captured this learning

• chat-session: Quick session insight
• project-completion: Full retrospective
• blocker-overcome: Problem solution

target: Where the improvement applies

• codebase: This specific project
• plugin: The playbook plugin itself
• both: Applies to both

category: Primary classification

• performance: Speed, efficiency issues
• database: Data layer issues
• integration: External service issues
• workflow: Process issues
• debugging: Investigation patterns
• testing: Test-related learnings
• security: Security-related learnings
• design: Visual design, UX, branding learnings
• generation: AI content/image generation learnings
• infrastructure: DevOps, CI/CD, deployment learnings

severity: Impact level

• critical: Must know, high impact
• high: Important, significant impact
• medium: Good to know
• low: Nice to have

tags: Searchable keywords (3-7 tags)

module: Specific code module affected (if applicable)

Decision Matrix: Which Target?

Use this matrix to decide where learnings should go:

Learning Type	Codebase?	Plugin?
Bug fix for specific code	Yes	No
General debugging pattern	Maybe	Yes
Architecture decision	Yes	No
Workflow improvement	No	Yes
Template enhancement	No	Yes
Cross-project pattern	Maybe	Yes
Project-specific gotcha	Yes	No
Tool usage tip	No	Yes

Capture Workflow

Step 1: Identify Trigger

Ask: "What kind of learning moment is this?"

• End of session → Lightweight capture
• Project done → Comprehensive retrospective
• Solved hard problem → Targeted capture

Step 2: Identify Target

Ask: "Who benefits from this learning?"

• Just this project → Codebase docs
• Future projects/workflows → Plugin improvements
• Both → Document in both places

Step 3: Use Appropriate Template

Use template from resources/templates/learnings.md and fill sections based on trigger type.

Step 4: Add Frontmatter

Add YAML frontmatter for searchability.

Step 5: Save to Correct Location

• Codebase: docs/learnings/YYYY-MM-DD-topic.md
• Plugin: Create PR to plugin repo

Step 5.5: Deep Analysis (Optional, Project Completion Only)

If session history files are available (.specstory/history/*.md):

• Spawn a research agent to scan for repetition patterns, frustration signals, wasted effort, scope drift
• Quantify findings with examples and estimated time impact
• Compare deep findings against the standard retrospective to identify blind spots
• Feed improvement actions into Step 6 alongside standard learnings

Step 6: CLAUDE.md Health Check & Promote Key Learnings

Before promoting, check CLAUDE.md health:

1. Run wc -c CLAUDE.md to check current size
2. If over 32,000 chars (80% of 40k limit), trigger a trimming pass before adding content:
   • Archive RESOLVED known issues to docs/learnings/resolved-issues.md
   • Move niche/domain-specific guides to docs/guides/
   • Remove content that duplicates the Quick Reference section
   • Condense verbose sections that can be replaced with a 1-line reference + link
3. If over 40,000 chars, trimming is mandatory before any promotion

Promotion hierarchy (highest = most actionable):

• Code/scripts (automatic) > CLAUDE.md (always visible) > Templates (on-demand) > Docs (reference)
• Ask: "Which 2-3 learnings should be promoted? Where should they live?"
• Implement the promotions (edit CLAUDE.md, create template, update script)

Demotion — content should also move DOWN the hierarchy over time:

• RESOLVED known issues → docs/learnings/resolved-issues.md
• Niche workflow guides (used by <1 session/month) → docs/guides/
• Content duplicated elsewhere in CLAUDE.md → delete
• Stale milestone-specific content → delete or archive

Integration Points

This skill supports:

• /playbook:learnings command (primary interface)
• /playbook:debug (captures blocker-overcome learnings)
• /playbook:work (suggests session learnings)

Best Practices

1. Capture while fresh - Don't wait, details fade quickly
2. Be specific - Vague learnings don't help
3. Include context - Future you needs to understand why
4. Add tags generously - Makes finding easier
5. Link to code - Reference specific files/lines when relevant
6. Update existing docs - Sometimes better than creating new ones
7. Review periodically - Old learnings may need updates

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Every learning captured makes future work easier. Compound your knowledge.