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 agentmods add commands/affaan-m/ecc/model-routegit 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/commands/affaan-m/ecc/model-route)<a href="https://agentmods.dev/commands/affaan-m/ecc/model-route"><img src="https://agentmods.dev/badge/commands/affaan-m/ecc/model-route.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 | $0.00000 | $0.00137 |
| Opus 5 | $0.00000 | $0.00068 |
| Sonnet 5 | $0.00000 | $0.00027 |
| Haiku 4.5 | $0.00000 | $0.00014 |
Grade A, and why
model-route 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 today.
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:
- model-route — 100% identical, 0 lines differ
- model-route — 100% identical, 0 lines differ
- model-route — 100% identical, 0 lines differ
- model-route — 100% identical, 0 lines differ
- model-route — 100% identical, 0 lines differ
- model-route — 100% identical, 0 lines differ
- model-route — 100% identical, 0 lines differ
- model-route — 100% identical, 0 lines differ
What it actually says
Model Route Command
Recommend the best model tier for the current task by complexity and budget.
Usage
/model-route [task-description] [--budget low|med|high]
Routing Heuristic
haiku: deterministic, low-risk mechanical changessonnet: default for implementation and refactorsopus: architecture, deep review, ambiguous requirements
Required Output
- recommended model
- confidence level
- why this model fits
- fallback model if first attempt fails
Arguments
$ARGUMENTS:
[task-description]optional free-text--budget low|med|highoptional
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.
- today First seen · 27 lines · 0 tokens per session scan A 9ad52439f152
model-route is a command published in the GitHub repository affaan-m/ECC (246,988 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 137 tokens. 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 commands, from other repositories
awesome-chatgpt
Search awesome-ChatGPT-repositories for open-source GitHub repositories related to ChatGPT and LLMs.
init
Scaffold a new MindBase project (v2 layout). Usage: /mb:init [template] [-- mission ...].
commit
智能生成 Git 提交信息并提交.
pr
Handle the full workflow from current branch state to an open, CI-monitored pull request.
doctor.es
Diagnostica problemas de inferencia LLM en Mac: asiai doctor verifica el estado de los motores, conflictos de puertos, carga de modelos y estado de la GPU.
requirement-review
需求文档多角色评审(requirement-review):需求文档 → 7-Agent 并行评审 → 重构高质量需求文档(Runtime 受控流程,0-7 阶段状态机).