Borrowing it
Nothing to install: this file belongs to AltimateAI/altimate-code. 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/AltimateAI/altimate-code/main/.opencode/command/learn.mdgit clone --depth 1 https://github.com/AltimateAI/altimate-codeWrote 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/altimateai/altimate-code/learn)<a href="https://agentmods.dev/commands/altimateai/altimate-code/learn"><img src="https://agentmods.dev/badge/commands/altimateai/altimate-code/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/altimateai/altimate-code/learn"><img src="https://agentmods.dev/badge/commands/altimateai/altimate-code/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 | $0.00018 | $0.00344 |
| Opus 5.5 | $0.00007 | $0.00138 |
| Sonnet 5.5 | $0.00004 | $0.00069 |
| Haiku 4.5 | $0.00002 | $0.00034 |
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.
This is a copy
100% identical to learn — 0 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.
What it actually says
Analyze this session and extract non-obvious learnings to add to AGENTS.md files.
AGENTS.md files can exist at any directory level, not just the project root. When an agent reads a file, any AGENTS.md in parent directories are automatically loaded into the context of the tool read. Place learnings as close to the relevant code as possible:
- Project-wide learnings → root AGENTS.md
- Package/module-specific → packages/foo/AGENTS.md
- Feature-specific → src/auth/AGENTS.md
What counts as a learning (non-obvious discoveries only):
- Hidden relationships between files or modules
- Execution paths that differ from how code appears
- Non-obvious configuration, env vars, or flags
- Debugging breakthroughs when error messages were misleading
- API/tool quirks and workarounds
- Build/test commands not in README
- Architectural decisions and constraints
- Files that must change together
What NOT to include:
- Obvious facts from documentation
- Standard language/framework behavior
- Things already in an AGENTS.md
- Verbose explanations
- Session-specific details
Process:
- Review session for discoveries, errors that took multiple attempts, unexpected connections
- Determine scope - what directory does each learning apply to?
- Read existing AGENTS.md files at relevant levels
- Create or update AGENTS.md at the appropriate level
- Keep entries to 1-3 lines per insight
After updating, summarize which AGENTS.md files were created/updated and how many learnings per file.
$ARGUMENTS
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 · 43 lines · 18 tokens per session scan A 891675a8519c
learn is a command published in the GitHub repository AltimateAI/altimate-code (813 stars, last pushed today), licensed MIT. It adds 18 tokens to every session and 344 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to learn, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
commit
A command that examines staged Git changes and proposes a commit message using the Conventional Commits format, such as feat, fix, or docs. Git is a tool for tracking changes to code.
create-plugin
Guided end-to-end plugin creation workflow with component design, implementation, and validation.
new-sdk-app
Create and setup a new FuXi Agent SDK application.
modernize-transform
Transform one legacy module to the target stack — idiomatic rewrite with behavior-equivalence tests.
feature-dev
Guided feature development with codebase understanding and architecture focus.
clean_gone
Cleans up all git branches marked as [gone] (branches that have been deleted on the remote but still exist locally), including removing associated worktrees.