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/dork-labs/dorkos/learngit clone --depth 1 https://github.com/dork-labs/dorkosWhat 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 | $0.00012 | $0.02249 |
| Opus 5 | $0.00006 | $0.01125 |
| Sonnet 5 | $0.00002 | $0.00450 |
| Haiku 4.5 | $0.00001 | $0.00225 |
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 2d 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.
How it starts
The opening of the file, as written. The whole thing — 307 lines — stays where its author put it; the contents beside it link to each section on GitHub.
System Learn Command
Learn new capabilities through trial and error experimentation, then codify successful behaviors into the system. This command acts as a collaborative learning partner.
Arguments
$ARGUMENTS- What to learn or codify. Examples:- Proactive: "learn how to interact with contacts using applescript"
- Retrospective: "we successfully created calendar events with smart defaults - codify this"
Mode Detection
Detect which mode based on the user's instructions:
Proactive mode (experimentation first):
- Keywords: "learn how to", "figure out how to", "try to", "experiment with", "discover how"
- Flow: Research → Experiment → Iterate → Codify
Retrospective mode (codify what worked):
- Keywords: "we just", "we successfully", "codify this", "what we did", "save this behavior", "remember how we"
- Flow: Analyze → Codify
Order of Operations
Execute these phases sequentially. This is an interactive, collaborative process.
Phase 1: Understanding
-
1.1 Parse
$ARGUMENTSto detect mode (proactive vs retrospective) -
1.2 For Proactive mode:
- Ask clarifying questions to understand the goal
- Define testable success criteria together with the user
- Present what we'll be experimenting with
- Get upfront consent: "This will involve experimentation. I'll try different approaches and ask for your feedback. Ready to proceed?"
-
1.3 For Retrospective mode:
- Confirm what was accomplished: "It sounds like we successfully [X]. Is that what you'd like to codify?"
- Clarify the scope: What specific behavior should be captured?
- Identify the key patterns that made it work
Phase 2: Research
-
2.1 Search for related existing behaviors in the system:
# Check existing skills Search .claude/skills/ for related patterns # Check existing commands Search .claude/commands/ for related functionality # Check hooks (configured in settings.json) Read .claude/settings.json for hook configurations # Check existing agents Search .claude/agents/ for related expertise # Check AGENTS.md for documented behaviors Read AGENTS.md and search for related keywords
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.
- 2d ago First seen · 307 lines · 12 tokens per session scan A 905e4e633649
learn is a command published in the GitHub repository dork-labs/dorkos (9 stars, last pushed 2d ago), licensed MIT. It adds 12 tokens to every session and 2,249 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
level-up
Assess your Claude Code level (0-10) and get a personalized roadmap to the next one.
correct-course
Navigate significant changes during sprint execution by analyzing impact, proposing solutions, and routing for implementation.
review-issue
Review and respond to a GitHub issue.
add-registry
Add a new notebook to registry.yaml.
review-pr-ci
Review a pull request and post the review to GitHub (CI/automated use).
review-pr
Review an open pull request and optionally post the review to GitHub.