ECC is a toolkit that organizes and improves how coding agents work through skills, memory, security checks, research practices, and related extensions. It is for developers using agents such as Claude Code, Codex, OpenCode, and Cursor.
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 affaan-m/ECC --skill eval-harnessgit clone --depth 1 https://github.com/affaan-m/ECCWrote 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/affaan-m/ecc/eval-harness)<a href="https://agentmods.dev/skills/affaan-m/ecc/eval-harness"><img src="https://agentmods.dev/badge/skills/affaan-m/ecc/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/affaan-m/ecc/eval-harness"><img src="https://agentmods.dev/badge/skills/affaan-m/ecc/eval-harness.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00037 | $0.01423 |
| Opus 5.5 | $0.00015 | $0.00569 |
| Sonnet 5.5 | $0.00007 | $0.00285 |
| Haiku 4.5 | $0.00004 | $0.00142 |
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 29d 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- eval-harness — 91% identical, 6 lines differ
- eval-harness — 91% identical, 6 lines differ
- eval-harness — 91% identical, 6 lines differ
- eval-harness — 91% identical, 6 lines differ
- eval-harness — 91% identical, 6 lines differ
- eval-harness — 91% identical, 6 lines differ
- eval-harness — 91% identical, 6 lines differ
- eval-harness — 91% identical, 6 lines differ
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.
- 29d ago Changed · +1 lines 49a5a1097586
- 1mo ago First seen · 236 lines · 37 tokens per session scan A 1db3ddbf6ace
eval-harness is a skill published in the GitHub repository affaan-m/ECC (282,704 stars, last pushed 3d ago), licensed MIT. It adds 37 tokens to every session and 1,423 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.
Other skills, from other repositories
evaluating-llms-harness
Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
systematic-debugging
4-phase root cause debugging: understand bugs before fixing.
test-driven-development
TDD: enforce RED-GREEN-REFACTOR, tests before code.
dogfood
Exploratory QA of web apps: find bugs, evidence, reports.
verify-and-stop
Prove existing work meets acceptance conditions without expanding scope. Use for validation-only tasks, completion checks, focused gate runs, and last-mile proof.
darwinian-evolver
Evolve prompts/regex/SQL/code with Imbue's evolution loop.