apply-learnings

name	apply-learnings
description	Reviews pending corrections from the developer learnings queue and applies them as generalized rules to the relevant skill's Learnings & Corrections section. Uses the Reflect-Abstract-Generalize-Write pattern to convert raw user corrections into absolute directives that improve future skill execution. Use when the user asks to: - Apply learnings or corrections - Review the learnings queue - Improve developer skills from past feedback - "What corrections have accumulated?"
argument-hint
allowed-tools	Read, Write, Edit, Grep, Glob, Bash, AskUserQuestion

Apply Learnings

Process pending corrections from the developer learnings queue and apply them as generalized rules to the relevant skill files.

Workspace Discovery

Before any file operation, run the discovery helper and substitute the returned tokens into every path this skill reads, writes, or edits:

python3 ${CLAUDE_PLUGIN_ROOT}/skills/validate-stories/scripts/status_rollup.py --mode discover

The JSON output supplies {workspace_root}, {project_root}, {project_name}, {stories_dir}, and {learnings_queue}. The plugin is project-agnostic — never hardcode project or chapter names in edits.

Step 1: Read the Learnings Queue

cat "{learnings_queue}"   # substituted from discovery

If the file is empty or contains no "status": "pending" entries, report "No pending learnings to apply" and stop.

Step 2: Group by Skill

Parse each JSONL line and group entries by the skill field. Display a summary to the user:

Pending learnings:
- create-dag: 2 corrections
- create-ingestion: 1 correction

Step 3: Process Each Correction

For each pending correction, apply the Reflect-Abstract-Generalize-Write pattern:

3a. Reflect

What exactly was corrected? Quote the user's original correction.

3b. Abstract

Is this specific to one case, or does it apply generally? Consider:

• Does it apply to all artifacts or just this one?
• Is it a style preference or a correctness issue?
• Could it conflict with existing learnings?

3c. Generalize

Write the learning as an absolute directive following the meta-rules:

1. Start with "Always" or "Never"
2. Lead with the problem, then the fix
3. Include a concrete command or example
4. One rule per bullet

3d. Confirm with User

Use AskUserQuestion to show the proposed learning and ask if it should be applied:

Proposed learning for create-dag:
- **L-001** (2026-04-22): Always declare `schedule=None` explicitly in DAGs
  that have no automated trigger — do not omit the schedule parameter.

Apply this learning? [Yes / No / Edit]

Step 4: Write Approved Learnings

For each approved learning:

1. Read the target skill file: ${CLAUDE_PLUGIN_ROOT}/skills/{skill-name}/SKILL.md

2. Find the ### Active Learnings section (create it under a ## Learnings & Corrections heading if absent)

3. Determine the next learning ID (L-001, L-002, etc.) by counting existing entries

4. Append the new learning bullet after any existing entries (or replace the "No learnings recorded yet" placeholder if this is the first)

5. Use the Edit tool to make the change

Step 5: Update the Queue

After all learnings are processed, update {learnings_queue}:

• Change "status": "pending" to "status": "applied" for applied entries
• Change "status": "pending" to "status": "rejected" for rejected entries

Step 6: Report

Summarize what was done:

• Number of learnings applied per skill
• Number rejected
• Any conflicts with existing learnings that were resolved