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/Fmarzochi/EGCWrote 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/fmarzochi/egc/learn-eval)<a href="https://agentmods.dev/commands/fmarzochi/egc/learn-eval"><img src="https://agentmods.dev/badge/commands/fmarzochi/egc/learn-eval/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/fmarzochi/egc/learn-eval"><img src="https://agentmods.dev/badge/commands/fmarzochi/egc/learn-eval.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 | $0.00025 | $0.01243 |
| Opus 5.5 | $0.00010 | $0.00497 |
| Sonnet 5.5 | $0.00005 | $0.00249 |
| Haiku 4.5 | $0.00003 | $0.00124 |
Grade A, and why
learn-eval 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 27d 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.
This is a copy
88% identical to learn-eval — 45 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/learn-eval - Extract, Evaluate, then Save
Extends /learn with a quality gate, save-location decision, and knowledge-placement awareness before writing any skill file.
What to Extract
Look for:
- Error Resolution Patterns: root cause + fix + reusability
- Debugging Techniques: non-obvious steps, tool combinations
- Workarounds: library quirks, API limitations, version-specific fixes
- Project-Specific Patterns: conventions, architecture decisions, integration patterns
Process
-
Review the session for extractable patterns
-
Identify the most valuable/reusable insight
-
Determine save location:
- Ask: "Would this pattern be useful in a different project?"
- Global (
<EGC dir>/skills/learned/, the EGC directory of the tool in use:~/.claudein Claude Code,~/.geminiin Antigravity,~/.egcwhen no tool is detected): Generic patterns usable across 2+ projects (bash compatibility, LLM API behavior, debugging techniques, etc.) - Project (the tool's own directory in the current project, such as
.claude/skills/learned/or.gemini/skills/learned/): Project-specific knowledge (quirks of a particular config file, project-specific architecture decisions, etc.) - When in doubt, choose Global (moving Global → Project is easier than the reverse)
-
Draft the skill file using this format:
---
name: pattern-name
description: "Under 130 characters"
user-invocable: false
origin: auto-extracted
---
# [Descriptive Pattern Name]
**Extracted:** [Date]
**Context:** [Brief description of when this applies]
## Problem
[What problem this solves - be specific]
## Solution
[The pattern/technique/workaround - with code examples]
## When to Use
[Trigger conditions]
-
Quality gate: Checklist + Holistic verdict
5a. Required checklist (verify by actually reading files)
Execute all of the following before evaluating the draft:
- Grep
~/.gemini/skills/and relevant project.gemini/skills/files by keyword to check for content overlap - Check MEMORY.md (both project and global) for overlap
- Consider whether appending to an existing skill would suffice
- Confirm this is a reusable pattern, not a one-off fix
- Grep
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.
- 27d ago Changed 8dce660717c7
- 1mo ago First seen · 117 lines · 25 tokens per session scan A 5331e8e835f1
learn-eval is a command published in the GitHub repository Fmarzochi/EGC (68 stars, last pushed 3d ago), licensed Apache-2.0. It adds 25 tokens to every session and 1,243 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 88% identical to learn-eval, differing in 45 lines, and is treated as a copy.
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