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 skills/outlinedriven/outline-driven-development/autolearnnpx skills add OutlineDriven/outline-driven-development --skill autolearngit clone --depth 1 https://github.com/OutlineDriven/outline-driven-developmentWrote 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/skills/outlinedriven/outline-driven-development/autolearn)<a href="https://agentmods.dev/skills/outlinedriven/outline-driven-development/autolearn"><img src="https://agentmods.dev/badge/skills/outlinedriven/outline-driven-development/autolearn.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 | $0.00055 | $0.04420 |
| Opus 5 | $0.00028 | $0.02210 |
| Sonnet 5 | $0.00011 | $0.00884 |
| Haiku 4.5 | $0.00006 | $0.00442 |
Grade A, and why
autolearn 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 — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Autolearn
Contract
| Field | Bound contract |
|---|---|
| Trigger | A non-trivial fix has been verified (observed working, not hoped working), or explicit autolearn or refresh invocation. |
| Authority | Reversible-local: write only the operating repo's docs/solutions/ and repo-root CONCEPTS.md. Rollback via git revert or file restore from history. No VCS push, credential, paid, published, deployed, or remote mutation. |
| Side effect | Writes or refreshes docs/solutions/ learning docs and CONCEPTS.md; stages only the surfaces this skill wrote or edited. |
| Done | A validated learning or concept entry exists, or an explicit determination that nothing qualifies. |
Inputs
- The verified fix or solved problem, from conversation history or codebase. Must be supplied or derivable from context.
- Optional:
mode:refresh [scope]to maintain existing docs;mode:headlessfor non-interactive operation. - Optional: an injected auto-memory block (supplementary context, not primary evidence).
Procedure
0. Route the mode
Strip mode: tokens from arguments before treating the remainder as context or scope.
- Capture (default): document one solved problem into docs/solutions/.
- Vocabulary capture: a durable, reusable project term surfaces; reconcile CONCEPTS.md.
- Memory handoff: a fact about the user, preferences, or cross-project context surfaces; do not write it into docs/solutions/ or CONCEPTS.md; surface it as a memory-handoff candidate for the memory system to capture.
- Refresh: maintain existing docs/solutions/ and CONCEPTS.md.
- Headless: overlays any mode; skip all questions, never pause, apply safe actions, mark uncertain as stale.
Fire automatically on a trigger phrase ("that worked", "it's fixed", "working now", "problem solved", "verified the fix", "tests pass now", "build succeeds", "that approach failed") or after a verified non-trivial fix. Auto-firing is permission to evaluate, not permission to fabricate.
One run can do all three repo-scoped actions: write a learning doc, reconcile a concept, and flag a memory-handoff candidate.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 241 lines · 55 tokens per session scan A 44a1f600846c
autolearn is a skill published in the GitHub repository OutlineDriven/outline-driven-development (52 stars, last pushed yesterday), licensed Apache-2.0. It adds 55 tokens to every session and 4,420 once invoked, about $0.0003 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 2026-09-03.
Other skills, from other repositories
askme
Explore intent with Verbalized Sampling before planning. Use when the task is ambiguous, you need maximum clarifying questions, or you want collaborative or adversarial mode via /askme.
slicing-code-context
Use when an exact symbol, path, entrypoint, or line range can bound a focused code question or patch proposal under a fixed source budget. Builds a deterministic slice packet and validates one constrained delegation. Not for source changes or broad repository exploration.
handoff-prompt
Use when the user asks for a handoff, delegation, or clipboard-ready prompt for another agent: a standalone path-free prompt copied to the clipboard, confirmed by title. Not for session-snapshot briefs — use handoff; never remote, credential, publish, deploy, or irreversible.
autopilot
Run a hands-off plan-to-ship pipeline by chaining existing skills. Use when the user says "autopilot", "take this from plan to shipped", "run the whole pipeline", "hands-off ship it", or "do the end-to-end build".
llm-self-loop
Use when a button click, dashboard check, or human verdict sits inside an iteration loop. Replaces it with an autonomous gate or moves non-automatable work outside the loop. Don't use for remote, credential, publish, deploy, or irreversible changes.
plan-review-tune
Use when plan-mode enforcement hooks intercept a plan review or the user runs /plan-review-tune to tune which plan-review questions fire. Persists the tuned question flow to a local question-registry and hook configuration. Not for auditing a plan against code — use plan-review.