Borrowing it
Nothing to install: this file belongs to zhmxiaowo/opencode-simple. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/zhmxiaowo/opencode-simple/main/.opencode/commands/learn-eval.mdgit clone --depth 1 https://github.com/zhmxiaowo/opencode-simpleWrote 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/zhmxiaowo/opencode-simple/learn-eval)<a href="https://agentmods.dev/commands/zhmxiaowo/opencode-simple/learn-eval"><img src="https://agentmods.dev/badge/commands/zhmxiaowo/opencode-simple/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/zhmxiaowo/opencode-simple/learn-eval"><img src="https://agentmods.dev/badge/commands/zhmxiaowo/opencode-simple/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 | $0.01190 |
| Opus 5.5 | $0.00010 | $0.00476 |
| Sonnet 5.5 | $0.00005 | $0.00238 |
| Haiku 4.5 | $0.00003 | $0.00119 |
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 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.
This is a copy
94% identical to learn-eval — 7 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 (
~/.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]
-
Quality gate — Checklist + Holistic verdict
5a. Required checklist (verify by actually reading files)
Execute all of the following before evaluating the draft:
- Grep
~/.claude/skills/and relevant project.claude/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.
- 1mo ago First seen · 117 lines · 25 tokens per session scan A 6907c2ba7b1b
learn-eval is a command published in the GitHub repository zhmxiaowo/opencode-simple (2 stars, last pushed 7mo ago), licensed MIT. It adds 25 tokens to every session and 1,190 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to learn-eval, differing in 7 lines, and is treated as a copy.
Other commands, from other repositories
minutes-ideas
Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.
learn
Force claude-smart to extract learnings from this session now.
memory-store
Store an insight, decision, or pattern to memory.
analyze-context
USE WHEN you want to analyze project context before starting work on a task. Calls context + recall, then synthesizes goals, decisions, gotchas, and relevant memories into a pre-task brief.
cc-memory
Configure persistent memory that survives across sessions using a layered approach: split rule files for always-loaded context, auto-memory for organic learning, and optional MCP-backed long-term memory for large codebases.
lians-recall
Recall current (non-stale) facts from Lians memory, optionally as-of a past date.