mamertofabian/maid-runner/maid-outcome-enrich

Claude Code and agent skill by mamertofabian/maid-runner

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Generate optional advisory Outcome enrichment outside maid-runner by orchestrating maid enrich prompt, agent-available model generation, maid enrich validate, and maid enrich render with cloud privacy disclosure.

Install

npx vskill@latest install mamertofabian/maid-runner/maid-outcome-enrich

SKILL.md

MAID Outcome Enrich

Generate optional advisory Outcome enrichment while keeping maid-runner itself deterministic and AI-free. This skill is the only place in the enrichment workflow that may call a model or touch provider credentials.

Rules

  • Keep runner commands deterministic. Use maid enrich only for prompt corpus

creation, digest validation, and markdown rendering.

  • Use the model access already available to the hosting agent, or a specific model the user chooses.
  • CLOUD-PRIVACY: sending the lesson corpus to an external provider publishes Outcome lessons externally. Use external generation only after the user deliberately accepts that privacy boundary.
  • A non-zero maid enrich validate result is a hard stop. Do not hand-edit .maid/outcomes-digest.json to satisfy validation; regenerate the candidate digest from the bounded corpus instead.
  • Use only registered maid enrich and maid insights options. Do not invent

provider, model, base URL, timeout, or API-key options for maid-runner.

Workflow

  1. Refresh the learned Outcome index when needed:

``bash maid learn ``

  1. Build the bounded corpus the model may see:

``bash maid enrich prompt --output .maid/outcomes-enrichment-prompt.json ``

If the index is intentionally stale, use the existing explicit opt-in:

``bash maid enrich prompt --allow-stale-index --output .maid/outcomes-enrichment-prompt.json ``

  1. Send only .maid/outcomes-enrichment-prompt.json to a model.

Use the model access already available to the hosting agent, or a specific model the user chooses. If that sends the corpus to an external provider, disclose CLOUD-PRIVACY first: sending the lesson corpus externally publishes Outcome lessons outside the local repository boundary.

The model output must be a candidate .maid/outcomes-digest.json.

  1. Validate the candidate digest:

``bash maid enrich validate --digest .maid/outcomes-digest.json ``

Treat a non-zero maid enrich validate result as a hard stop. Do not hand-edit .maid/outcomes-digest.json to satisfy validation. Regenerate the candidate digest from the bounded corpus and validate again.

  1. Render advisory markdown from the validated digest:

``bash maid enrich render --digest .maid/outcomes-digest.json --md-output .maid/outcomes-digest.md ``

  1. Optionally view deterministic theme aggregation through insights:

``bash maid insights --theme-map .maid/outcomes-digest.json ``

Output Boundaries

.maid/outcomes-digest.json and .maid/outcomes-digest.md are advisory, generated, ignored artifacts. They are not authoritative Outcome records and must not replace manifest outcome: sections, validation commands, review evidence, or the MAID implementation lifecycle.

Security

  • Security scanPassed, 90 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
3
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