ABOUTME: Project knowledge capture through engaging LEARNING.md files

ABOUTME: Documents architecture, decisions, bugs, lessons learned in conversational style

Learning Documentation

Quality Notes

• 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

Examples

Illustrative only: every fact in a lesson (durations, numbers, causes, fixes) comes from the session, the git history, or the user. When a detail is unknown, leave it out or ask; never invent one to make the story vivid.

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

1. Read existing LEARNING.md (or create if missing)
2. Review recent work (git log --oneline -10)
3. Ask what was learned, what was tricky
4. Append new lessons in conversational style (dated, searchable)
5. Capture solutions in docs/solutions/[category]/ for searchable reuse
6. 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).

Extraction

Session files run to 100k+ lines, so extract summaries with jq instead of reading them raw, and look for patterns rather than individual messages.

What to Look For

Key jq Commands

Sessions live in ~/.claude/projects/PROJECT_NAME/<session-id>.jsonl, one event per line; message content sits under .message.content.

• Tool call counts: jq -r 'select(.type=="assistant") | .message.content[]? | select(.type=="tool_use") | .name' SESSION.jsonl | sort | uniq -c | sort -rn
• Repeated reads: jq -r 'select(.type=="assistant") | .message.content[]? | select(.type=="tool_use" and .name=="Read") | .input.file_path' SESSION.jsonl | sort | uniq -c | sort -rn | head -20
• Error patterns: jq -r 'select(.type=="user") | .message.content | arrays | .[] | select(.type=="tool_result" and .is_error==true) | .content | tostring' SESSION.jsonl | 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.