Borrowing it
Nothing to install: this file belongs to opendatahub-io/agent-eval-harness. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/opendatahub-io/agent-eval-harness/main/CLAUDE.mdgit 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/instructions/opendatahub-io/agent-eval-harness/claude-md)<a href="https://agentmods.dev/instructions/opendatahub-io/agent-eval-harness/claude-md"><img src="https://agentmods.dev/badge/instructions/opendatahub-io/agent-eval-harness/claude-md/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/instructions/opendatahub-io/agent-eval-harness/claude-md"><img src="https://agentmods.dev/badge/instructions/opendatahub-io/agent-eval-harness/claude-md.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 | $0.05353 | $0.05353 |
| Opus 5.5 | $0.02141 | $0.02141 |
| Sonnet 5.5 | $0.01071 | $0.01071 |
| Haiku 4.5 | $0.00535 | $0.00535 |
Grade A, and why
agent-eval-harness CLAUDE.md 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 1mo 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 — 329 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Eval Harness
Generic evaluation framework for Claude Code skills and agent capabilities. Uses MLflow as the backbone for tracing, evaluation, datasets, and reporting.
Project Status
Phase 1 (core framework), Phase 2 (scoring integration), and Phase 3 (prompt-based evaluation) are implemented. See eval/plans/agent-eval-harness-design.md in the rfe-creator project for the full design doc.
Execution Model
The harness separates how many invocations (execution.mode) from what to execute (execution.skill or execution.prompt):
Execution Mode (case vs batch)
- case: One invocation per test case (default). The harness loops over cases.
- batch: One invocation for all cases via batch.yaml. The skill/agent loops internally.
What to Execute (skill vs prompt)
- Skill mode (
execution.skill): Test predefined skill implementations (/my-skill --args). Evaluates skill correctness, quality, and cost efficiency. - Prompt mode (
execution.prompt): Test agent capabilities directly by sending prompts without a skill wrapper.
Common Patterns
Skill evaluation (case mode):
execution:
mode: case
skill: rfe.create
arguments: '--priority {{ input.priority }} "{{ input.prompt }}"'
Skill evaluation (batch mode):
execution:
mode: batch
skill: rfe.speedrun
arguments: '--input batch.yaml --headless'
Agentic documentation testing (prompt mode) ✨:
execution:
mode: case
prompt: "{{ input.prompt }}"
Implemented flavor - Agentic Documentation Testing (see examples/openshift-agentic-docs.md):
- Documentation effectiveness: Can agents navigate and use your docs?
- Pattern understanding: Can agents identify and apply code patterns?
- Constraint compliance: Do agents respect documented rules?
- API usage: Can agents call APIs with the right fields and structure from documentation alone?
Includes builtin documentation generation prompts (navigation, anti-pattern, authoring, component-usage, architecture) for structured evaluation. See agent_eval/prompts/docs/.
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.
- 1mo ago Changed · +29 tokens per session 4e4f215eed18
- 1mo ago Changed · +158 tokens per session e32a9784cec6
- 1mo ago Changed · +34 tokens per session 323ad6e0591b
- 1mo ago Changed · -1 lines · +50 tokens per session 02e18996f570
- 2mo ago First seen · 330 lines · 5,082 tokens per session scan A 2884b3ee3738
agent-eval-harness CLAUDE.md is an instructions file published in the GitHub repository opendatahub-io/agent-eval-harness (45 stars, last pushed 17d ago), licensed Apache-2.0. It adds 5,353 tokens to every session, about $0.0214 per session on Opus 5.5. 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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