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-run)<a href="https://agentmods.dev/skills/opendatahub-io/agent-eval-harness/eval-run"><img src="https://agentmods.dev/badge/skills/opendatahub-io/agent-eval-harness/eval-run/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-run"><img src="https://agentmods.dev/badge/skills/opendatahub-io/agent-eval-harness/eval-run.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 | $0.00141 | $0.04629 |
| Opus 5 | $0.00071 | $0.02315 |
| Sonnet 5 | $0.00028 | $0.00926 |
| Haiku 4.5 | $0.00014 | $0.00463 |
Grade A, and why
eval-run 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 — 323 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an evaluation executor. You run a skill against test cases, score the outputs with judges, and report results. You orchestrate by calling scripts — never duplicate their work.
For the full data flow (dataset → workspace → execution → collection → scoring), see ${CLAUDE_SKILL_DIR}/references/data-pipeline.md. For tool interception mechanics, see ${CLAUDE_SKILL_DIR}/references/tool-interception.md.
Step 0: Parse Arguments and Load Config
Parse $ARGUMENTS:
| Argument | Required | Default | Description |
|---|---|---|---|
--config <path> |
no | auto-discover | Path to eval config |
--model <model> |
no | models.skill from config |
Skill model. Required if models.skill is unset in eval.yaml. |
--subagent-model <model> |
no | models.subagent → falls back to skill model |
Model for subagents (e.g., claude-sonnet-4-6 while main is claude-opus-4-7) |
--skill <name> |
no | from config | Override the skill to test |
--run-id <id> |
no | YYYY-MM-DD-<model> |
Identifier for this run |
--cases <id> [<id> ...] |
no | all cases | Exact case IDs to run |
--baseline <run-id> |
no | — | Previous run to compare against |
--no-llm-judges |
no | false | Skip LLM judges (prompt, prompt_file, LLM builtins, agent). Run deterministic judges (check, Python builtins, external code). |
--gold |
no | false | Save outputs as gold references after run |
--effort <level> |
no | runner.effort from config |
Agent reasoning effort (claude-code or codex) |
--runner <type> |
no | local | local (default Steps 1–8) or harbor (containerized — skips to Harbor runner section) |
--env <name> |
no | kubernetes |
Harbor execution environment: podman, kubernetes, openshift (only with --runner harbor) |
--mount <source:target[:ro or rw]> |
no | — | Repeatable Podman bind mount; defaults to read-only (only with --runner harbor) |
--cpus <n> / --memory-mb <MiB> |
no | Harbor defaults | Hard resource limits per Harbor environment |
If --runner harbor: after config discovery, skip to the Harbor runner section below. Steps 2–6 are replaced by one run.py call.
What ships with it
15 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.
- prompts/analyze-results.md 8.1 KB
- prompts/comparison-judge.md 1.4 KB
- references/data-pipeline.md 20 KB
- references/execution-modes.md 2.7 KB
- references/execution-monitoring.md 5.5 KB
- references/tool-interception.md 7.4 KB
- scripts/agent_eval 19 B
- scripts/collect.py 19 KB runs code
- scripts/execute.py 77 KB runs code
- scripts/preflight.py 6.4 KB runs code
- scripts/report.py 138 KB runs code
- scripts/score.py 133 KB runs code
- scripts/tools.py 10 KB runs code
- scripts/workspace_files.py 5.2 KB runs code
- scripts/workspace.py 32 KB runs code
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 · 323 lines · 141 tokens per session scan A e842d9b6190f
eval-run 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 141 tokens to every session and 4,629 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.