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 agentmods add skills/tpavanipradeep/everything-claude-code/eval-harnessnpx skills add tpavanipradeep/everything-claude-code --skill eval-harnessgit clone --depth 1 https://github.com/tpavanipradeep/everything-claude-codeWhat 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 | $0.00019 | $0.01405 |
| Opus 5 | $0.00010 | $0.00702 |
| Sonnet 5 | $0.00004 | $0.00281 |
| Haiku 4.5 | $0.00002 | $0.00140 |
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 3d 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.
This is a copy
91% identical to eval-harness — 5 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 237 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.
When to Activate
- Setting up eval-driven development (EDD) for AI-assisted workflows
- Defining pass/fail criteria for Claude Code task completion
- Measuring agent reliability with pass@k metrics
- Creating regression test suites for prompt or agent changes
- Benchmarking agent performance across model versions
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
- Use pass@k metrics for reliability measurement
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: SHA or checkpoint 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
Use Claude to evaluate open-ended outputs:
[MODEL GRADER PROMPT]
Evaluate the following code change:
1. Does it solve the stated problem?
2. Is it well-structured?
3. Are edge cases handled?
4. Is error handling appropriate?
Score: 1-5 (1=poor, 5=excellent)
Reasoning: [explanation]
What ships with it
1 file 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.
- 3d ago First seen · 237 lines · 19 tokens per session scan A 0bc29828b0b2
eval-harness is a skill published in the GitHub repository tpavanipradeep/everything-claude-code (104 stars, last pushed 5mo ago), licensed MIT. It adds 19 tokens to every session and 1,405 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to eval-harness, differing in 5 lines, and is treated as a copy.
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chat-perf
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