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Portable secure code review skill for AI coding agents — OWASP/CWE coverage, structured findings, and remediation guidance. Works with Cursor, Claude Code, Kiro, and Open Agent Skills.
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Generates a post-run feedback learning brief from benchmark artifacts. Use after scoreboard/scoring outputs to extract recurring patterns, mistakes, wins, and next-step improvement actions for the benchmark process.
SKILL.md
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Learn
Create a separate feedback-learning output from benchmark artifacts. This skill is intentionally independent from scoreboard generation.
Purpose
Produce a concise learning brief that helps improve future benchmark runs. Focus on patterns and actions, not rerunning scoring.
Inputs (default)
benchmark/artifacts/scoring/*.txt(required)benchmark/artifacts/findings/*.txt(optional but recommended)- generated scoreboard output (optional context)
If an input set is missing, continue with available artifacts and state limits.
Method
- Read all available scoring artifacts.
- Optionally correlate with findings artifacts for root-cause context.
- Extract repeated themes:
- where outcomes were strong,
- where outcomes were partial/incorrect/unknown,
- recurring reasoning or evidence-quality issues,
- recurring remediation-quality issues.
- Turn themes into concrete process improvements for the next run.
Output format
Use this structure:
# Learning brief
## Observed patterns
- <pattern 1>
- <pattern 2>
- <pattern 3>
## What improved outcomes
- <high-signal behaviors that correlated with correct judgments>
## What hurt outcomes
- <failure modes and recurring quality gaps>
## Next iteration actions
1. <specific action>
2. <specific action>
3. <specific action>
## Experiments for next run
- <small measurable process experiment>
- <small measurable process experiment>
Rules
- Keep recommendations actionable and benchmark-specific.
- Ground claims in artifact content; do not invent evidence.
- Prefer 3-7 bullets per section and 3-5 actions total.
- Keep this output separate from scoreboard output.