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 cloudnative-co/claude-code-starter-kit --skill eval-harnessgit clone --depth 1 https://github.com/cloudnative-co/claude-code-starter-kitWrote 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/cloudnative-co/claude-code-starter-kit/eval-harness)<a href="https://agentmods.dev/skills/cloudnative-co/claude-code-starter-kit/eval-harness"><img src="https://agentmods.dev/badge/skills/cloudnative-co/claude-code-starter-kit/eval-harness/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/cloudnative-co/claude-code-starter-kit/eval-harness"><img src="https://agentmods.dev/badge/skills/cloudnative-co/claude-code-starter-kit/eval-harness.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00020 | $0.01242 |
| Opus 5.5 | $0.00008 | $0.00497 |
| Sonnet 5.5 | $0.00004 | $0.00248 |
| Haiku 4.5 | $0.00002 | $0.00124 |
Grade A, and why
eval-harness 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 — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Eval Harness Skill
A formal evaluation framework for Claude Code sessions, implementing eval-driven development (EDD) principles.
Philosophy
Eval-Driven Development treats evals as the "unit tests of AI development":
- Define expected behavior BEFORE implementation
- Run evals continuously during development
- Track regressions with each change
Eval Types
Capability Evals
Test if Claude can do something it couldn't before:
[CAPABILITY EVAL: feature-name]
Task: Description of what Claude should accomplish
Success Criteria:
- [ ] Criterion 1
- [ ] Criterion 2
- [ ] Criterion 3
Expected Output: Description of expected result
Regression Evals
Ensure changes don't break existing functionality:
[REGRESSION EVAL: feature-name]
Baseline: git SHA (or a /checkpoint milestone name)
Tests:
- existing-test-1: PASS/FAIL
- existing-test-2: PASS/FAIL
- existing-test-3: PASS/FAIL
Result: X/Y passed (previously Y/Y)
Grader Types
1. Code-Based Grader
Deterministic checks using code:
# Check if file contains expected pattern
grep -q "export function handleAuth" src/auth.ts && echo "PASS" || echo "FAIL"
# Check if tests pass
npm test -- --testPathPattern="auth" && echo "PASS" || echo "FAIL"
# Check if build succeeds
npm run build && echo "PASS" || echo "FAIL"
2. Model-Based Grader
Have a separate instance (subagent) review open-ended outputs against a checklist:
[MODEL GRADER PROMPT]
Review the following code change and answer each question with YES/NO plus evidence:
1. Does it solve the stated problem?
2. Is it well-structured?
3. Are edge cases handled?
4. Is error handling appropriate?
Verdict: PASS/FAIL
Reasoning: [explanation]
3. Human Grader
Flag for manual review:
[HUMAN REVIEW REQUIRED]
Change: Description of what changed
Reason: Why human review is needed
Risk Level: LOW/MEDIUM/HIGH
Note: statistical metrics like pass@k require running the same task k times independently. If you truly need them, implement an automated script (e.g., a headless claude -p loop), not manual bookkeeping in an interactive session.
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 First seen · 206 lines · 20 tokens per session scan A 57a8491a73b2
eval-harness is a skill published in the GitHub repository cloudnative-co/claude-code-starter-kit (153 stars, last pushed 5d ago), licensed MIT. It adds 20 tokens to every session and 1,242 once invoked, about $0.0001 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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