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/learn)<a href="https://agentmods.dev/commands/affaan-m/ecc/learn"><img src="https://agentmods.dev/badge/commands/affaan-m/ecc/learn/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/learn"><img src="https://agentmods.dev/badge/commands/affaan-m/ecc/learn.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 | $0.00008 | $0.00324 |
| Opus 5.5 | $0.00003 | $0.00130 |
| Sonnet 5.5 | $0.00002 | $0.00065 |
| Haiku 4.5 | $0.00001 | $0.00032 |
Grade A, and why
learn 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:
- learn — 100% identical, 0 lines differ
- learn — 100% identical, 0 lines differ
- learn — 100% identical, 0 lines differ
- learn — 100% identical, 0 lines differ
- learn — 100% identical, 0 lines differ
- learn — 100% identical, 0 lines differ
- learn — 95% identical, 2 lines differ
- learn — 95% identical, 2 lines differ
What it actually says
Learn Command
Extract patterns, learnings, and reusable insights from the current session: $ARGUMENTS
Your Task
Analyze the conversation and code changes to extract:
- Patterns discovered - Recurring solutions or approaches
- Best practices applied - Techniques that worked well
- Mistakes to avoid - Issues encountered and solutions
- Reusable snippets - Code patterns worth saving
Output Format
Patterns Discovered
Pattern: [Name]
- Context: When to use this pattern
- Implementation: How to apply it
- Example: Code snippet
Best Practices Applied
- [Practice name]
- Why it works
- When to apply
Mistakes to Avoid
- [Mistake description]
- What went wrong
- How to prevent it
Suggested Skill Updates
If patterns are significant, suggest updates to:
skills/coding-standards/SKILL.mdskills/[domain]/SKILL.mdrules/[category].md
Instinct Format (for continuous-learning-v2)
{
"trigger": "[situation that triggers this learning]",
"action": "[what to do]",
"confidence": 0.7,
"source": "session-extraction",
"timestamp": "[ISO timestamp]"
}
TIP: Run /learn periodically during long sessions to capture insights before context compaction.
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 First seen · 62 lines · 8 tokens per session scan A 67b1f6181411
learn is a command published in the GitHub repository affaan-m/ECC (284,130 stars, last pushed today), licensed MIT. It adds 8 tokens to every session and 324 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
export-closedloop-learnings
Exports pending ClosedLoop learnings to global location with deduplication.
pull-learnings
Pulls shared organization patterns into local org-patterns.toon.
load-context
Load a large file or content into the RLM system for chunk-based analysis.
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.
revise-claude-md
Update CLAUDE.md with learnings from this session.
recall-set-key
Update your Recall API key across every location Claude Code reads it from. Run this whenever you generate a new key from https://recallmcp.com/dashboard/keys.