Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add mhylle/claude-skills-collection --skill eval-harnessgit clone --depth 1 https://github.com/mhylle/claude-skills-collectionWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mhylle/claude-skills-collection/eval-harness)<a href="https://agentmods.dev/skills/mhylle/claude-skills-collection/eval-harness"><img src="https://agentmods.dev/badge/skills/mhylle/claude-skills-collection/eval-harness/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mhylle/claude-skills-collection/eval-harness"><img src="https://agentmods.dev/badge/skills/mhylle/claude-skills-collection/eval-harness.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00135 | $0.02502 |
| Opus 5 | $0.00068 | $0.01251 |
| Sonnet 5 | $0.00027 | $0.00500 |
| Haiku 4.5 | $0.00014 | $0.00250 |
Grade A, and why
eval-harness scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 324 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Eval Harness
A skill for building and running evaluation harnesses on AI features. Covers two eval types (capability, regression), three grader types (code, model, human), and the metrics that summarize them (pass@k, pass^k).
Evaluation-driven development (EDD)
Evals are defined before or alongside implementation, not after. Success criteria become explicit, measurable, and testable from day one.
Core principles:
- Define success first — before implementing a feature, define what "working correctly" means through explicit evaluations.
- Measure continuously — run evals throughout development, not just at the end.
- Automate where possible — prefer automated graders for speed and consistency.
- Human review for nuance — use human graders when quality judgments require context or subjectivity.
- Track regressions — every capability added becomes a regression test.
- Iterate on failures — failed evals provide specific, actionable feedback for improvement.
Benefits: clear success criteria (no ambiguity about what "done" means), early problem detection, confidence in changes, tests double as executable specifications, quantitative progress tracking.
The mindset shift:
Traditional: "Build it, then figure out if it works"
EDD: "Define what 'works' means, then build to pass those evals"
Eval types (summary)
| Type | Purpose | When to use |
|---|---|---|
| Capability | Verify new functionality works | Adding features, improving functionality, testing edge cases |
| Regression | Protect against unintended breakage | After code changes, before PR merge, after dependency updates |
Full structures and YAML examples → references/eval-types.md.
Grader types (summary)
| Type | Strength | When to use |
|---|---|---|
| Code (exact match, regex, function output) | Fast, deterministic, cheap | Any codifiable correctness check |
| Model (LLM judge with rubric) | Handles semantic variation, tone, quality | Natural-language outputs, multiple valid answers |
| Human (Likert, binary, ranking) | Catches what machines miss | Creative content, safety-critical judgments, calibrating auto-graders |
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 324 lines · 135 tokens per session scan A 07f9dc21d42a
eval-harness is a skill published in the GitHub repository mhylle/claude-skills-collection (18 stars, last pushed 8d ago), licensed MIT. It adds 135 tokens to every session and 2,502 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".
neuron-evaluation-engineer
Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…
jetson-validate-image
Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.
atmos-validation
Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.
skill-benchmark
Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.