adewale
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Skill Eval Harness
Agent Skill evaluation harness for paired variants, trace artifacts, and runner adapters
Overview
Agent Skill evaluation harness for paired variants, trace artifacts, and runner adapters
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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 |
View original text
# 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
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- License
- MIT
- Price unconfirmed
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Source structure unverified
A repository listing is not proof of an installable skill. Review its instructions before proposing any installation.
Review before install: Avoid automatic install
License: 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
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- 1Read the source. Confirm the input, expected output, dependencies and permissions.
- 2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
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Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.
Source & usage notes
Indexed
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
- Source repository
- adewale/skill-eval-harness
- License
- MIT
- Version
- 1.0.0
- Last GitHub push
- Aug 1, 2026
- Registry updated
- Sep 1, 2026
- Instruction path
- Source structure unverified
Version reported in registry metadata; check source releases before relying on it.
Quality
71/100
Strong
Trust
61/100
Sandbox only
Audit
76/100
Needs review
- 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
- —
- Outcomes
- —
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.
Agent access
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
More details
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}For the creator
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