Agent learn

Skill queen-of-code/AI-DLC/skills/agents/agent-learn

Captures learnings after a feature or incident completes. Updates PROJECT.md, writes or updates ADRs, adds retrospective notes to Tech Specs, and updates repo documentation so future agents start with current context.From its SKILL.md

Install

npx -y skills add queen-of-code/AI-DLC --skill agent-learn

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

9 stars9 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

4.0 KB, as published. Nobody here has run it

Learn Agent

The Learn agent runs as part of the AIDLC Validate+Learn phase after a feature passes validation, or as part of Close+Learn after an incident is resolved. Its job is to ensure that everything discovered during the cycle is captured in durable, agent-readable form so future work starts with full context.

When to Invoke

After a Feature passes Validate (development lifecycle)
After an Incident passes Close (operational lifecycle)
After any significant architectural decision that needs an ADR

Responsibilities

1. Update PROJECT.md (Required)

The project memory file (PROJECT.md at repo root) is the quick-reference context document that all agents read at the start of a session. After every feature completion, the Learn agent must update it:

Add the feature to the "Implemented Features" section with:
- Feature number and title (linked to the GitHub issue)
- Current status
- A 2–3 sentence description of what was built and why
- Sub-issues completed (linked to their GitHub issues)
- Key technical decisions made during implementation
Update "Last updated" date in the header
Update the Architecture Overview if the feature changed the deployment stack, added services, or modified the directory structure
Update Conventions if the feature established new patterns or changed existing ones

2. Write or Update ADRs (If Applicable)

If the feature involved significant architectural decisions — new services, technology choices, protocol decisions, schema changes — write an ADR capturing:

The context and problem
Options considered
The decision and rationale
Consequences and trade-offs

ADRs go in repo-root adr/, named NNNN-title-with-dashes.md. Copy from the bundled template spec-management/templates/adr-template.md and read adr-guidance.md for naming and when to write one.

3. Retrospective Notes on Tech Specs (If Applicable)

For each Tech Spec (Unit) in the feature, add a brief retrospective note at the bottom capturing:

What differed from the plan and why
Anything that was harder or easier than expected
Patterns discovered that should inform future specs

4. Update Repo Documentation (If Applicable)

If the feature changed how things work in ways that affect onboarding or day-to-day development:

Update README.md if top-level structure or quickstart changed
Update docs/ files if specific documentation areas are affected
Update AGENTS.md if agent/skill discoverability changed

5. Note Process Friction (If Applicable)

If the AIDLC process itself had friction during this cycle, note it in a brief section at the bottom of the feature's retrospective. This feeds back into AIDLC process improvement.

Output Format

The Learn agent produces a single commit (or PR) containing all documentation updates. The commit message follows the convention:

docs(learn): capture learnings from Feature #<N> — <title>

Quality Criteria

PROJECT.md accurately reflects the current state of the project after the feature
A new agent starting a session and reading only PROJECT.md would have enough context to understand what exists, where it runs, and what decisions were made
No stale information remains from before the feature was implemented
ADRs, if written, are self-contained and don't require reading the full PR history to understand