| name | learning-docs |
| description | Create and update LEARNING.md project retrospectives. Use when user says retrospective, lessons learned, what did we learn, document decisions, or session analysis. |
ABOUTME: Project knowledge capture through engaging LEARNING.md files
ABOUTME: Documents architecture, decisions, bugs, lessons learned in conversational style
Learning Documentation
Quality Notes
- Take your time reviewing recent work thoroughly before writing
- Quality of insights matters more than covering every change
- Re-read what you wrote: is it useful to a future reader, or just filler?
Purpose
Capture project knowledge in LEARNING.md - a living document that grows with the project. Not boring docs, but engaging technical storytelling.
When to Update
- After fixing non-trivial bugs
- After architectural decisions
- After integrating new tech
- After solving tricky problems
- Before context switches (end of day/week)
Structure
Sections: Project Overview, Architecture (mermaid diagrams), Tech Stack & Decisions (table: Technology | Why | Trade-offs), Lessons Learned (dated: Context → Problem → Solution → Takeaway), Pitfalls & Gotchas, Best Practices Discovered
Writing Style
| Do | Don't |
|---|
| Conversational tone | Dry technical prose |
| Analogies that clarify | Jargon without context |
| Concrete examples | Abstract descriptions |
| "We tried X, it broke because Y" | "X is not recommended" |
| Honest about mistakes | Sanitized corporate-speak |
Examples
Good:
We spent 2 hours debugging why webhooks weren't firing. Turns out Redis was silently dropping messages when memory hit 80%. Added maxmemory-policy volatile-lru and monitoring. Lesson: always monitor your message queues, silence is not golden.
Bad:
Webhook reliability was improved by adjusting Redis configuration parameters.
Workflow
- Read existing LEARNING.md (or create if missing)
- Review recent work (
git log --oneline -10)
- Ask what was learned, what was tricky
- Append new lessons in conversational style (dated, searchable)
- Capture solutions in
docs/solutions/[category]/ for searchable reuse
- Mirror lessons to vault (below): cross-project archive, blog discovery, knowledge-sync input
Vault copy (lessons): after appending to LEARNING.md, also append each new lesson to the vault (create the note first with obsidian create name="<project> - Learnings" silent if missing; command syntax in _OBSIDIAN.md):
obsidian append file="<project> - Learnings" content="### YYYY-MM-DD: <title>\n<Context → Problem → Solution → Takeaway, condensed to a few lines>"
The repo's LEARNING.md stays the source of truth. If the obsidian CLI is unavailable, skip (do not use the path fallback; the repo copy is authoritative).
Vault privacy (applies to EVERY vault copy in this skill: lessons, solutions, skill candidates): for work projects (HikmaAI, client repos), mirror only content safe outside the work perimeter. Exclude client names, internal hostnames and architecture details, unreleased product information, anything under NDA. When in doubt, keep it repo-only.
Solutions Directory
For solved problems worth referencing again, create files in docs/solutions/:
docs/solutions/
├── auth/ → Authentication, authorization, sessions
├── performance/ → Profiling, caching, optimization
├── infrastructure/ → CI/CD, Docker, deployment
├── database/ → Migrations, queries, indexing
├── testing/ → Patterns, fixtures, flaky test fixes
└── debugging/ → Hard bugs, investigation techniques
Format: docs/solutions/[category]/YYYY-MM-DD_short-description.md
Each solution file:
# Problem
[What broke / what we needed]
# Solution
[What fixed it, with code if relevant]
# Why It Works
[Root cause or design rationale, 1-3 sentences]
When to use LEARNING.md vs solutions/: LEARNING.md for narrative retrospectives, architectural decisions, broad lessons. Solutions/ for specific, searchable, reusable fixes; "how did we solve X?" answers.
Vault copy: After writing to docs/solutions/, also append to vault: obsidian append file="<project> - Solutions" content="### YYYY-MM-DD: [title]\n[Problem/Solution/Why]". Creates cross-project discoverability.
Session Analysis
Analyze past sessions to identify improvement opportunities. Session files live in ~/.claude/projects/ (project paths: slashes→dashes).
CRITICAL Rules
- NEVER read raw session files (100k+ lines, token killer)
- ALWAYS use jq to extract summaries
- Focus on patterns, not individual messages
What to Look For
| Pattern | Example | Fix |
|---|
| Token waste | Read same file 5+ times | Cache key info, update CLAUDE.md |
| Wrong paths | Built feature, then found existing code | Better initial search, architecture docs |
| Repeated mistakes | Same lint error 3 sessions | Pre-commit hook, CLAUDE.md note |
| Missing automation | Manual steps every session | Script it, add to workflow |
| Context loss | Re-learn after compaction | Save state to LEARNING.md before limit |
Key jq Commands
Sessions live in ~/.claude/projects/PROJECT_NAME/session_*.json.
- Tool call counts:
jq '[.messages[].content[]? | select(.type=="tool_use") | .name] | group_by(.) | map({tool: .[0], count: length}) | sort_by(-.count)'
- Repeated reads:
jq -r '... | select(.name=="Read") | .input.file_path' | sort | uniq -c | sort -rn | head -20
- Error patterns:
jq -r '... | select(.type=="tool_result" and (.content | tostring | test("error"))) | .content' | head -50
Propose Improvements As
CLAUDE.md updates, new skills, scripts, LEARNING.md entries, pre-commit hooks.
Vault Pattern Annotation
When a lesson learned maps to a skill domain, append to ## Skill Candidates in the relevant Second Brain note:
obsidian append file="Second Brain - Development" content="\n| <pattern> | <target-skill> | <project> | YYYY-MM-DD | weak |"
Signal starts as weak. The knowledge-sync skill promotes to strong when 3+ projects or 2+ independent sources confirm the pattern. Create the ## Skill Candidates section (with table header) if it doesn't exist yet.
