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/oguzsh/claudey/learn-evalgit clone --depth 1 https://github.com/oguzsh/claudeyWrote 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/oguzsh/claudey/learn-eval)<a href="https://agentmods.dev/commands/oguzsh/claudey/learn-eval"><img src="https://agentmods.dev/badge/commands/oguzsh/claudey/learn-eval.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 | $0.00025 | $0.00794 |
| Opus 5 | $0.00013 | $0.00397 |
| Sonnet 5 | $0.00005 | $0.00159 |
| Haiku 4.5 | $0.00003 | $0.00079 |
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 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- learn-eval — 100% identical, 1 lines differ
How it starts
The opening of the file, as written. The whole thing — 93 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 and save-location decision 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 (
~/.claude/skills/learned/): Generic patterns usable across 2+ projects (bash compatibility, LLM API behavior, debugging techniques, etc.) - Project (
.claude/skills/learned/in current project): 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]
-
Self-evaluate before saving using this rubric:
Dimension 1 3 5 Specificity Abstract principles only, no code examples Representative code example present Rich examples covering all usage patterns Actionability Unclear what to do Main steps are understandable Immediately actionable, edge cases covered Scope Fit Too broad or too narrow Mostly appropriate, some boundary ambiguity Name, trigger, and content perfectly aligned Non-redundancy Nearly identical to another skill Some overlap but unique perspective exists Completely unique value Coverage Covers only a fraction of the target task Main cases covered, common variants missing Main cases, edge cases, and pitfalls covered
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 · 93 lines · 25 tokens per session scan A 01d09ffbdcc4
learn-eval is a command published in the GitHub repository oguzsh/claudey (5 stars, last pushed 3mo ago), licensed MIT. It adds 25 tokens to every session and 794 once invoked, about $0.0001 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 2026-08-31.
Other commands, from other repositories
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checklist
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clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.