harvest-session-learnings
SolidScan the current working session and the active task's artifacts for reusable, generalizable lessons (what was tried, what failed and why, what surprised us, what the next task should do differently) and propose anchored entries to append to the task's LEARNINGS.md, the produce-side counterpart to the ADR-0017 consume path that task-init already reads. Append-only and read-only on existing entries; de-duplicates against what is already captured; keeps durable lessons and drops one-off task trivia. Use on demand mid-task after a hard-won fix or a surprising failure, or at closure to sweep a long session before the context is lost. Do not use to rewrite or prune existing learnings (never edit prior entries), to capture a single in-flight observation (use capture-observation), to close a slice or the task (use slice-closure or task-close), or when nothing durable was learned (return a NO_OP rather than manufacturing a lesson).
Install
Quality Score: 81/100
Skill Content
Details
- Author
- Mozurok
- Repository
- Mozurok/fhorja.dev
- Created
- 1 months ago
- Last Updated
- 5 days ago
- Language
- Python
- License
- MIT
Similar Skills
Semantically similar based on skill content — not just same category
learn
Run a session retrospective — extract lessons from what happened and persist them where they'll be found next time (CLAUDE.md, MEMORY.md, handoff.xml, skills). Invoke proactively after painful debug sessions, unexpected breakthroughs, end of significant work, or when a pattern recurs across sessions. Apply Kaizen — small improvements, compounded.
session-learnings
Captures key observations, anti-patterns, and rule improvement suggestions at the end of a session. Writes a sharded YAML to ~/worklogs/learnings/YYYY/MM/ and updates the learnings index. <example> Context: End of a debugging session on manifest generation user: "/session-learnings" assistant: "Captured 3 observations, 1 anti-pattern, 2 rule suggestions → ~/worklogs/learnings/2026/04/2026-04-14-manifest-debug.yaml" </example>
learn
Capture durable lessons after implementation, review, verification, or incidents. Use when recurring mistakes, systemic gaps, or explicit retrospective work should guide future contributors.