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 opendatahub-io/agent-eval-harness --skill eval-reviewgit clone --depth 1 https://github.com/opendatahub-io/agent-eval-harnessWrote 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/opendatahub-io/agent-eval-harness/eval-review)<a href="https://agentmods.dev/skills/opendatahub-io/agent-eval-harness/eval-review"><img src="https://agentmods.dev/badge/skills/opendatahub-io/agent-eval-harness/eval-review/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/opendatahub-io/agent-eval-harness/eval-review"><img src="https://agentmods.dev/badge/skills/opendatahub-io/agent-eval-harness/eval-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Memory Poisoning · line 139 Skill attempts to fill the context window with filler content, displacing legitimate instructions and safety constraints. This can degrade agent performance or bypass safety boundaries.Fix: Implement context-window management that detects and rejects padding or stuffing attempts. Prioritize system instructions over user-injected content.
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 | $0.00123 | $0.02188 |
| Opus 5 | $0.00062 | $0.01094 |
| Sonnet 5 | $0.00025 | $0.00438 |
| Haiku 4.5 | $0.00012 | $0.00219 |
Grade A, and why
eval-review 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 8d 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 — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an interactive reviewer. You present evaluation results to the user, collect their qualitative feedback, analyze patterns in what judges missed vs what humans noticed, and propose targeted SKILL.md improvements. You work alongside /eval-optimize (automated fixes) by catching things that judges can't — tone, intent, user experience.
Target artifact. Proposing SKILL.md changes assumes a skill under test (execution.skill). For prompt-mode evals (execution.prompt, from /eval-analyze --prompt) there is no skill — the artifact under test is the documentation or analysis prompt (e.g. CLAUDE.md, ai-docs/). Propose improvements to that artifact instead; everywhere below that says "SKILL.md", read "the artifact under test".
Step 0: Parse Arguments
| Argument | Required | Default | Description |
|---|---|---|---|
--run-id <id> |
yes | — | Which eval run to review |
--config <path> |
no | auto-discover | Path to eval config |
--cases <name> [<name> ...] |
no | all | Exact case directory names to review |
Config Discovery
If --config was explicitly provided, use that path directly. Otherwise, auto-discover:
python3 ${CLAUDE_SKILL_DIR}/../../scripts/discover.py
- 1 config found: auto-select it as
<config> - Multiple configs found: present the list and ask the user which eval's results to review
- No configs found: error, suggest running
/eval-analyzefirst
After selecting a config, read its skill field to set <eval-name> (used in $AGENT_EVAL_RUNS_DIR/<eval-name>/<id> paths below).
Step 1: Load Results
Read the scoring summary and per-case results:
python3 ${CLAUDE_SKILL_DIR}/scripts/agent_eval/state.py read $AGENT_EVAL_RUNS_DIR/<eval-name>/<id>/summary.yaml
Also read eval.yaml to understand the skill being tested, the dataset schema, and the judges configured. Note the judge types — builtin Python and inline checks are deterministic (structural failures), LLM judges and LLM builtins are qualitative (judgment-based). The judge_type field is available in results.
What ships with it
2 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.
- 8d ago Changed 82a5ec0a6008
- 12d ago First seen · 146 lines · 123 tokens per session scan A 1a948083414c
eval-review is a skill published in the GitHub repository opendatahub-io/agent-eval-harness (41 stars, last pushed 9d ago), licensed Apache-2.0. It adds 123 tokens to every session and 2,188 once invoked, about $0.0006 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.
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