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/commands/affaan-m/ecc/plan)<a href="https://agentmods.dev/commands/affaan-m/ecc/plan"><img src="https://agentmods.dev/badge/commands/affaan-m/ecc/plan/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/commands/affaan-m/ecc/plan"><img src="https://agentmods.dev/badge/commands/affaan-m/ecc/plan.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00009 | $0.00369 |
| Opus 5.5 | $0.00004 | $0.00148 |
| Sonnet 5.5 | $0.00002 | $0.00074 |
| Haiku 4.5 | $0.00001 | $0.00037 |
Grade A, and why
plan 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- plan — 100% identical, 69 lines differ
- plan — 100% identical, 69 lines differ
- plan — 100% identical, 69 lines differ
- plan — 100% identical, 69 lines differ
- plan — 100% identical, 76 lines differ
- plan — 100% identical, 69 lines differ
- plan — 100% identical, 69 lines differ
- plan — 95% identical, 69 lines differ
What it actually says
Implementation Planner
Before writing any code for this feature/task, produce a structured plan.
Steps
- Clarify the goal — restate the requirement in one sentence; flag any ambiguities.
- Research first — identify existing utilities, libraries, or patterns in the codebase that can be reused. Do not reinvent what already exists.
- Identify dependencies — list external packages, APIs, environment variables, or database changes needed.
- Break into phases — structure work as ordered phases, each independently shippable:
- Phase 1: Core data model / schema changes
- Phase 2: Business logic + unit tests
- Phase 3: API / integration layer + integration tests
- Phase 4: UI / consumer layer + E2E tests
- Identify risks — note anything that could block progress or cause regressions.
- Define done — list the exact acceptance criteria (tests passing, coverage ≥ 80%, no lint errors, docs updated).
Output Format
## Goal
[One-sentence summary]
## Reuse Opportunities
- [Existing utility/pattern]
## Dependencies
- [Package / API / env var]
## Phases
### Phase 1 — [Name]
- [ ] Task A
- [ ] Task B
### Phase 2 — [Name]
...
## Risks
- [Risk and mitigation]
## Definition of Done
- [ ] All tests pass (≥80% coverage)
- [ ] No new lint errors
- [ ] Docs updated if public API changed
Apply ECC coding standards throughout: immutable patterns, small focused files, explicit error handling.
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 Changed · +3 lines · +3 tokens per session af82b05cc68e
- 1mo ago First seen · 50 lines · 6 tokens per session scan A 27d67051945d
plan is a command published in the GitHub repository affaan-m/ECC (283,276 stars, last pushed 4d ago), licensed MIT. It adds 9 tokens to every session and 369 once invoked, about $0.0000 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 2026-09-03.
Other commands, from other repositories
caveman-compress
Compress a markdown/text file into caveman format to save tokens.
megacave
Activate megacave mode (caveman in Classical Chinese).
ultracave
Activate ultracave mode (caveman at maximum compression).
prune-learnings
Manual command to invoke the pruning script for cleaning up old learnings.
pull-learnings
Pulls shared organization patterns into local org-patterns.toon.
recall-loop
Periodic memory digest — designed for use with Claude Code's /loop scheduler. Shows recent memories and pending to-dos in a compact, scannable format.