Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add opendatahub-io/agent-eval-harness/plugin install 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-check)<a href="https://agentmods.dev/skills/opendatahub-io/agent-eval-harness/eval-check"><img src="https://agentmods.dev/badge/skills/opendatahub-io/agent-eval-harness/eval-check/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-check"><img src="https://agentmods.dev/badge/skills/opendatahub-io/agent-eval-harness/eval-check.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00112 | $0.02200 |
| Opus 5 | $0.00056 | $0.01100 |
| Sonnet 5 | $0.00022 | $0.00440 |
| Haiku 4.5 | $0.00011 | $0.00220 |
Grade A, and why
eval-check 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 9d 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 — 191 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a harness health checker. You scan the full configuration (skills, commands, CLAUDE.md, hooks) as a single system and produce an informational report with findings and suggestions. You do not modify any files. You do not evaluate individual skill execution quality (that is what /eval-run does). Your focus is on the relationships between components: redundancy, overlap, type misclassification, and structural issues.
All findings are informational suggestions. The user decides what to act on.
Step 0: Parse Arguments
| Argument | Required | Default | Description |
|---|---|---|---|
--output <path> |
no | harness-report.md |
Where to write the report |
--include-global |
no | false | Also scan ~/.claude/CLAUDE.md (user-global config, may contain personal instructions) |
Step 1: Inventory
Run the inventory script to discover all configuration artifacts:
python3 ${CLAUDE_SKILL_DIR}/scripts/harness_inventory.py --root .
This reports:
- Number of skills, commands, hooks
- Number of eval configs (
eval.yamlfiles naming a skill; generated Harbor task packages are excluded) - Approximate word count per skill (note: this is a word count, not a precise token count)
- Structural warnings (missing CLAUDE.md, skills without frontmatter descriptions)
If only one skill is found, report the inventory and skip to Step 6. Note: "Single-skill configuration. Cross-component analysis is not applicable." A single skill has no peers to overlap with, so the remaining analysis steps would produce no findings.
Step 2: Read All Skills
For each skill found in Step 1, read its full SKILL.md content. Extract:
- The YAML frontmatter (between
---delimiters): name, description, allowed-tools - The body content: what rules and instructions it contains
- Any references to other skills (Skill tool calls,
/skill-namereferences)
Keep a structured summary of each skill's domain, trigger description, and key rules.
What ships with it
3 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.
- 9d ago First seen · 191 lines · 112 tokens per session scan A 7e9be5a1bf82
eval-check is a skill published in the GitHub repository opendatahub-io/agent-eval-harness (40 stars, last pushed 7d ago), licensed Apache-2.0. It adds 112 tokens to every session and 2,200 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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