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-datasetgit 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-dataset)<a href="https://agentmods.dev/skills/opendatahub-io/agent-eval-harness/eval-dataset"><img src="https://agentmods.dev/badge/skills/opendatahub-io/agent-eval-harness/eval-dataset/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-dataset"><img src="https://agentmods.dev/badge/skills/opendatahub-io/agent-eval-harness/eval-dataset.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 Data Exfiltration · line 255 Data is uploaded to cloud storage (S3 / GCS / Azure Blob). This may be a legitimate backup or exfiltration to an external bucket. Manual review is recommended.Fix: Verify the destination bucket is trusted and owned by you. Never upload credentials, secrets, or workspace contents to external or unverified cloud storage.
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 | $0.00145 | $0.04205 |
| Opus 5 | $0.00072 | $0.02103 |
| Sonnet 5 | $0.00029 | $0.00841 |
| Haiku 4.5 | $0.00015 | $0.00421 |
Grade A, and why
eval-dataset 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 10d 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 — 269 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You generate evaluation test cases for an eval.yaml. Case provenance comes from generation.strategy (see Step 1.5): the agent authors cases from the skill analysis (skill, the default), a script synthesizes them from generation prompts (synthetic), or they are extracted from MLflow production traces (from-traces). In every case the goal is giving /eval-run something meaningful to test against, matching the dataset schema.
Step 0: Parse Arguments
| Argument | Required | Default | Description |
|---|---|---|---|
--config <path> |
no | auto-discover | Path to eval config |
--count <N> |
no | 5 | Number of cases to generate |
--run-id <id> |
no | — | Prior eval run to learn from when augmenting existing cases |
--harbor |
no | — | Also generate Harbor task packages (Step 8) |
--image <image> |
with --harbor |
— | Container image for Harbor task packages |
Provenance is in the config, not a flag. generation.strategy selects where cases come from:
skill (default — agent authors from skill analysis), synthetic (LLM generates from
generation.seeds), or from-traces (extracted from MLflow production traces). There is no
--strategy flag: whether to create a fresh set or augment an existing one is derived from the
current dataset state (Step 3), and --run-id informs the augment case.
--count applies to the skill and from-traces paths. synthetic is fully declarative — case
counts come from each seed's count in generation.seeds, so --count is ignored there; resize a
synthetic dataset by editing seed counts in eval.yaml.
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 dataset to populate
- No configs found: suggest running
/eval-analyzefirst
What ships with it
7 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.
- 10d ago First seen · 269 lines · 145 tokens per session scan A 946dd98363a6
eval-dataset is a skill published in the GitHub repository opendatahub-io/agent-eval-harness (41 stars, last pushed 7d ago), licensed Apache-2.0. It adds 145 tokens to every session and 4,205 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.
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