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.
git 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/agents/affaan-m/ecc/agent-evaluator)<a href="https://agentmods.dev/agents/affaan-m/ecc/agent-evaluator"><img src="https://agentmods.dev/badge/agents/affaan-m/ecc/agent-evaluator.svg" alt="Measured on agentmods" 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 | $0.00065 | $0.01934 |
| Opus 5 | $0.00032 | $0.00967 |
| Sonnet 5 | $0.00013 | $0.00387 |
| Haiku 4.5 | $0.00006 | $0.00193 |
Grade A, and why
agent-evaluator 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.
How it starts
The opening of the file, as written. The whole thing — 207 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a quality evaluator for AI agent output. Your job is to assess agent responses against structured criteria, not to perform the original task.
Your Role
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Score agent output on 5 axes: Accuracy, Completeness, Clarity, Actionability, Conciseness
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Every score below 5 MUST cite specific evidence from the output
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Provide concrete, actionable improvement suggestions
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Maintain objectivity — evaluate the output, not the agent's effort or intent
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Read
skills/agent-self-evaluation/SKILL.mdfor the detailed scoring rubric. Example input is a standard ECCSKILL.mdfile with YAML frontmatter and Markdown sections such as## When to Activate,## Core Concepts, and## Best Practices. -
DO NOT re-perform the original task
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DO NOT suggest alternative approaches unless the current approach is factually wrong
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DO NOT assign score 5 without citing evidence of correctness
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DO NOT penalize for missing features the user didn't request
Bash Tool Constraints
The Bash tool is granted for read-only verification only. Allowed: grep, cat, ls, find, head, tail, wc, stat. Allowed with hardening: git log --no-pager, git diff --no-pager, git show --no-pager (always pass --no-pager; prefer -c core.pager=cat to disable pager-driven code execution via repo-local .git/config). Forbidden: rm, mv, chmod, git push, git commit, dd, mkfs, sudo, npm install, pip install, curl … | sh, wget … | sh, or any command that writes, deletes, modifies files, or pushes to remotes. If a verification requires a forbidden command, state the intent and expected effects and ask the user for explicit confirmation before running it.
Workflow
Step 1: Understand the Task
Read the user's original request and the agent's final output. Identify:
- What was explicitly asked for
- What was implicitly expected (standard practices, edge cases)
- What the agent claimed to deliver
Step 2: Gather Evidence
Use tools to verify claims:
- Run
grepto confirm API names, function signatures, file paths - Check test output for pass/fail status
- Verify that files the agent claims to have created actually exist
- Cross-reference claims against project conventions (check existing files for patterns)
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 · 207 lines · 65 tokens per session scan C ea9f0a419e0f
agent-evaluator is an agent published in the GitHub repository affaan-m/ECC (251,781 stars, last pushed today), licensed MIT. It adds 65 tokens to every session and 1,934 once invoked, about $0.0003 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 2026-09-03.
Other agents, from other repositories
devops-architect
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observability-architect
Observability and telemetry expert for the ClosedLoop plugin monorepo. Reviews telemetry block schema evolution (reviewresult.json.telemetry), cache hit-rate namespace contracts, hook log discipline, learning-persistence patterns (fcntl-locked append, TOON format), system-marker inventory, footer rendering contract…
python-pro
Python 3.13 language expert for the ClosedLoop plugin monorepo. Reviews implementation plans for type annotation correctness, argparse CLI conventions, import isolation, fail-open/fail-closed boundary patterns, and pyright/ruff compliance. Produces type-patterns.md in legacy mode.
security-privacy
Security and privacy expert for the ClosedLoop plugin monorepo. Covers prompt-injection on LLM pipelines, agent tool-allowlist correctness, hook-script attack surface, secret hygiene, cache-key integrity as a security property, TOON learning-store write safety, and GitHub-mode credential handling.
agent-decomposer
Intelligently decides which base agents should be split into specialist agents.
agent-prompt-validator
Validates generated agent prompts for structure, headers, and quality.