evaluate-learnings

name	evaluate-learnings
description	Analyze memory files for generally applicable learnings and extract them to AGENTS.md, skills, or other permanent locations. Use when reviewing accumulated corrections and decisions for broader applicability.

Evaluate Learnings

Use this skill to review accumulated memory entries (corrections and decisions) and determine which are project-specific and which represent general patterns that should be promoted to permanent, structural locations.

When to use

• After a project has accumulated several memory entries.
• Before backporting improvements to a boilerplate or starter template.
• Periodically, to keep memory files lean and ensure valuable patterns aren't buried.

Steps

1. Read all memory files

Read every file in .agents/memory/. For each entry, note whether it describes:

• Project-specific knowledge — tied to a particular library, API, or architectural choice unique to this project.
• Generic agent behavior — a pattern any agent in any project would benefit from.
• Generic tooling/convention — a pattern tied to the tech stack (e.g., Bun, Vitest, React) but not to this specific project.

2. Classify each entry

For each entry, assign one of these dispositions:

Disposition	Meaning	Action
Stay	Project-specific; belongs in memory	No action
Promote to AGENTS.md	Generic behavioral rule	Add/refine a rule in AGENTS.md
Promote to skill	Generic pattern best expressed as skill guidance	Update the relevant skill's SKILL.md
Promote to context.md	Architectural insight that applies project-wide	Add to .agents/context.md

3. Present the classification

Show the user a table with:

#	Entry title	Source file	Disposition	Target location	Rationale

Wait for the user to confirm or adjust before making changes.

4. Apply approved promotions

For each approved promotion:

1. Add the learning to its target location. Follow existing formatting and conventions in that file.
2. Delete the original memory entry. Active memory files should only contain entries not yet captured elsewhere. After promoting a learning, remove it from the source file (corrections.md or decisions.md). This keeps active memory lean and avoids stale duplicates competing with the promoted version. Git history preserves the full timeline if needed.

5. Verify coherence

After all promotions:

• Re-read AGENTS.md to confirm new rules don't contradict existing ones.
• Re-read any updated skills to confirm additions fit the skill's scope.
• Check that no learning was promoted to multiple overlapping locations.

Classification guidelines

Goes in AGENTS.md when…

• It's a behavioral rule that constrains how agents should act (e.g., "ask before guessing", "guard CLI entry points").
• It refines or strengthens an existing rule that proved too vague.
• It clarifies the boundary between agent files (e.g., what goes in context.md vs AGENTS.md).

Goes in a skill when…

• It's a procedural best practice within a specific workflow (e.g., "read the file tail before appending" fits the remember skill).
• It's a technique or recipe for a specific tool (e.g., "check DOM errors, not just console" fits review or playwright-cli).

Stays in memory when…

• It's about a specific library, API, or framework behavior unique to this project.
• It's a workaround for a project-specific integration issue.
• It would be meaningless without the project's specific context.

Anti-patterns

• Don't over-promote. Not every learning is a rule. If a correction happened once and is unlikely to recur in other projects, leave it in memory.
• Don't duplicate. If AGENTS.md already says "prefer immutability", don't add a memory-derived rule that says the same thing differently.
• Don't generalize prematurely. A pattern observed once in one project is an anecdote, not a rule. Promote with confidence only when the learning clearly applies beyond this project's specific context.