Kilo Code is an open-source AI coding agent that works inside VS Code and JetBrains, from the command line, or through cloud services. Developers use it to build software with AI models, switch between providers, and run cloud agents or automated code reviews. The catalogue includes eleven skills, eight agents, and one instruction for Kilo Code.
Borrowing it
Nothing to install: this file belongs to Kilo-Org/kilocode. 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/Kilo-Org/kilocode/main/.opencode/command/learn.mdgit clone --depth 1 https://github.com/Kilo-Org/kilocodeWrote 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/kilo-org/kilocode/learn)<a href="https://agentmods.dev/commands/kilo-org/kilocode/learn"><img src="https://agentmods.dev/badge/commands/kilo-org/kilocode/learn.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 5.1 | $0.00018 | $0.00344 |
| Opus 5 | $0.00009 | $0.00172 |
| Sonnet 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 today.
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.
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.
- today First seen · 43 lines · 18 tokens per session scan A 891675a8519c
learn is a command published in the GitHub repository Kilo-Org/kilocode (27,190 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. 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 2026-09-06.
Other commands, from other repositories
code-review
Code review the current proposed code change.
merge-conflict
Resolve a merge conflict.
pr-review
Code review for pull request $1.
checklist
Generate a custom checklist for the current feature based on user requirements.
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.