adewale
Indexé par la communauté
Skill Eval Harness
Agent Skill evaluation harness for paired variants, trace artifacts, and runner adapters
Vue d’ensemble
Agent Skill evaluation harness for paired variants, trace artifacts, and runner adapters
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Documentation source, pas des instructions pour ce site. Vérifiez les permissions avant d’exécuter des commandes.
Skill Eval Harness
Skill Eval Harness is a Python CLI that measures the causal lift of an Agent Skill: it runs the same case, model, and repetition with and without the skill, validates that exact experimental identity, then reports what changed, what passed, and whether the eval leaked its own answer. It reads evals/shared-benchmark.json, emits answer-key-safe task rows, grades files under eval-runs/ locally and deterministically — no model call in the grade path — and writes benchmark reports you can diff across variants.
General eval frameworks (openai/evals, vitest-evals, viteval) score one output against a rubric. This one measures the difference the skill makes, and spends its surface area on keeping that difference honest: paired with/without comparison, tune/holdout/holdback split discipline, leakage lint, materialized ablations with provenance gates, and per-model lift. None of those frameworks have them, and they are what make a reported number trustworthy rather than merely green.
Questions this helps answer
| Question | Command/report to use |
|---|---|
| Does this skill improve outputs compared with no skill at all? | prepare paired with_skill / without_skill rows, then benchmark paired lift and significance. |
| Which prompts improved, regressed, saturated, or showed no lift? | benchmark case_flags, render-viewer, and error-analysis. |
| Is the skill worth its extra tokens or dollars? | profile-skill, token-overhead, cost-summary, and lift-per-dollar summaries. |
| Did my latest skill edit introduce a regression? | Re-run the same manifest, inspect ablation_regressions, trend, and render-viewer --previous-workspace. |
| Which instruction, checklist, reference, scr |
Voir le texte original
# Skill Eval Harness [](https://github.com/adewale/skill-eval-harness/actions/workflows/ci.yml) [](LICENSE) Skill Eval Harness is a Python CLI that measures the **causal lift** of an Agent Skill: it runs the same case, model, and repetition with and without the skill, validates that exact experimental identity, then reports what changed, what passed, and whether the eval leaked its own answer. It reads `evals/shared-benchmark.json`, emits answer-key-safe task rows, grades files under `eval-runs/` locally and deterministically — no model call in the grade path — and writes benchmark reports you can diff across variants. General eval frameworks (openai/evals, vitest-evals, viteval) score one output against a rubric. This one measures the *difference the skill makes*, and spends its surface area on keeping that difference honest: paired with/without comparison, `tune`/`holdout`/`holdback` split discipline, leakage lint, materialized ablations with provenance gates, and per-model lift. None of those frameworks have them, and they are what make a reported number trustworthy rather than merely green. ## Questions this helps answer | Question | Command/report to use | |---|---| | Does this skill improve outputs compared with no skill at all? | `prepare` paired `with_skill` / `without_skill` rows, then `benchmark` paired lift and significance. | | Which prompts improved, regressed, saturated, or showed no lift? | `benchmark` `case_flags`, `render-viewer`, and `error-analysis`. | | Is the skill worth its extra tokens or dollars? | `profile-skill`, `token-overhead`, `cost-summary`, and lift-per-dollar summaries. | | Did my latest skill edit introduce a regression? | Re-run the same manifest, inspect `ablation_regressions`, `trend`, and `render-viewer --previous-workspace`. | | Which instruction, checklist, reference, scr
Examiner la source
Prix et coûts d’utilisation
- Obtenir le skill
- Prix non confirmé
- L’utiliser
- Prérequis non confirmés. Consultez les frais d’agent, d’API et de services à la source.
- Licence
- MIT
- Prix non confirmé
- Le prix n’est pas confirmé. Les liens existants vers les sources et l’installation restent disponibles.
Gratuit à obtenir ne signifie pas gratuit à utiliser. Le prix ne constitue pas une évaluation de sécurité. Soumettre un prix →
Structure non vérifiée
Un dépôt répertorié ne prouve pas qu’un skill est installable. Vérifiez les instructions avant de proposer une installation.
Réviser avant installation: Éviter l’installation automatique
Licence: MIT
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 63 GitHub stars
- Stars/forks activity: 63 stars, 5 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
Les outils sont des indications de métadonnées, pas une compatibilité testée. Les prompts sont des suggestions.
Commencer par une petite tâche
- 1Lisez la source et confirmez entrées, résultats, dépendances et permissions.
- 2Demandez un plan à l’agent. Approuvez la configuration et les coûts avant un test isolé.
- 3Vérifiez résultats et fichiers modifiés. Signalez uniquement ce qui a été exécuté et conservez la révision source.
Vérifiez les dépendances, clés API et frais externes dans la source. Un dépôt public ne rend pas tous les services gratuits.
Source et conseils d’utilisation
Répertorié
Métadonnées et examens sont indicatifs. Popularité, découverte et exécution réussie sont des faits distincts.
- Dépôt source
- adewale/skill-eval-harness
- Licence
- MIT
- Version
- 1.0.0
- Dernier push GitHub
- 1 août 2026
- Registre mis à jour
- 1 sept. 2026
- Chemin des instructions
- Structure non vérifiée
Version déclarée dans le registre ; vérifiez les versions de la source.
Qualité
71/100
Solide
Confiance
61/100
Sandbox uniquement
Audit
76/100
Revue nécessaire
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 63 GitHub stars
- Stars/forks activity: 63 stars, 5 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Verified installs
- —
- Résultats
- —
Copier ne signifie pas installer. Les compteurs nécessitent un rapport de réussite et ne garantissent pas la qualité globale.
Accès agent
L’API Registry fournit les signaux de décision, confiance, audit, cas d’usage et installation sans analyser l’interface.
Plus de détails
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"documentation": "Strong README/SKILL.md context",
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"Permission surface may require sandboxing",
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"Permission surface needs review: secrets or environment access, shell or command execution",
"GitHub adoption: 63 GitHub stars",
"Stars/forks activity: 63 stars, 5 forks; issue activity unavailable in current metadata",
"Dependency/runtime risk: command execution surface, credential or environment access",
"Permission surface: secrets or environment access, shell or command execution"
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"Safety: 36/100 Avoid automatic install",
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"manifest": "https://www.openagentskill.com/api/registry/manifest/adewale-skill-eval-harness"
}
}Pour le créateur
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