Evals

Skill adriannoes/awesome-agentic-ai/cursor-claude-codex/skills/igoruehara-spec-driven/skills/evals

Use to evaluate how faithfully the implementation matches the spec — runs the eval that checks whether each AC-N is covered by a task and referenced in a test, counts SPEC_DEVIATION and reports a score per feature. Trigger with /evals.From its SKILL.md

Install

npx -y skills add adriannoes/awesome-agentic-ai --skill evals

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

SKILL.md

1.6 KB, 348 tokens by cl100k_base, as published. Nobody here has run it

Translation note: Originally authored in Portuguese (pt-BR) by Igor Uehara (igoruehara/spec-driven, MIT). Translated to English by this hub to keep the repository language consistent. Original content unchanged in meaning; see the upstream repo for the pt-BR source.

Skill: Spec→code fidelity evals

Measures whether what was built reflects the spec — the quality metric of the agent's output. Two layers: deterministic (script) and judgment.

1. Deterministic layer

node scripts/eval-spec-fidelity.mjs .

Reports, per feature: total ACs, covered by a task, referenced in test/code, and open SPEC_DEVIATION. Fails (exit 1) if any AC has no task — broken traceability. Reference in test is a warning until the feature is implemented.

2. Judgment layer (the script does not catch this)

Does the test for each AC-N actually exercise the Given/When/Then — or does it just cite the ID in an empty test?
Does the implementation cover the edge cases and respect the spec's "Out of scope"?
Do the open SPEC_DEVIATION items have a resolution (fix the code or update the spec/ADR)?

Output

Score per feature + the gaps. It complements /validar (UAT for a single feature) with a portfolio fidelity view. The same eval runs in CI (esteira.yml) — here it is the judgment counterpart.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.