Borrowing it
Nothing to install: this file belongs to saschb2b/okf-studio. 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/saschb2b/okf-studio/main/.agents/skills/autopilot/SKILL.mdgit clone --depth 1 https://github.com/saschb2b/okf-studioWrote 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/saschb2b/okf-studio/autopilot)<a href="https://agentmods.dev/skills/saschb2b/okf-studio/autopilot"><img src="https://agentmods.dev/badge/skills/saschb2b/okf-studio/autopilot/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/skills/saschb2b/okf-studio/autopilot"><img src="https://agentmods.dev/badge/skills/saschb2b/okf-studio/autopilot.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 | $0.00131 | $0.02481 |
| Opus 5.5 | $0.00052 | $0.00992 |
| Sonnet 5.5 | $0.00026 | $0.00496 |
| Haiku 4.5 | $0.00013 | $0.00248 |
Grade A, and why
autopilot 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 3d 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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Autopilot
The mode
You operate without waiting for direction. You pick the work, do it, prove it works, commit it, and pick the next thing. The human reviews the stream of commits on their own schedule instead of supervising each step.
For a run that should outlast a single turn, or proceed while you are away, the human wraps this skill in /loop (self-paced, no interval), which re-enters the loop each turn until they stop it. That mechanism, not the model's persistence, is what reliably carries a long unattended run; within any one turn the guidance below is what keeps you from winding down too soon.
Two non-negotiables make this safe to leave running:
- Every iteration leaves the project better than you found it and is independently reviewable (small, focused, committed on its own).
- You never cross an out-of-bounds line (below) without surfacing it first. When you hit one, write it down and move to the next in-bounds thing. A blocked avenue never stops the loop.
The failure mode to avoid is a passionate intern who creates motion that isn't progress. Motion is easy. The discipline is picking work a senior reviewer would thank you for.
The loop
- Survey. First iteration, and again whenever your map goes stale. Read the README, CLAUDE.md, recent commits, the test suite, open TODO and FIXME markers. Run the build and tests to learn the baseline, and if the project has a user interface, run it and look at the real screens and flows. You can't improve what you haven't seen.
- Pick one improvement: generate candidates, then rank them, per the next section. One concern per iteration.
- Scope it small enough to finish and review in a single sitting. If it can't be, it's a parent. Pick the smallest valuable slice and leave the rest as a note.
- Do it in the idiom of the surrounding code. Match its conventions, not your preferences.
- Verify. Run the tests, build, and lint. For a user-facing change, also run the app and look at the rendered result, including its loading, empty, and error states. A green build never tells you a screen looks right or a flow feels right. Prove the change works with evidence, not "should work."
- Commit on its own with a message saying what changed and why. One iteration, one reviewable commit.
- Loop. Pick the next thing. Do not ask permission to continue. If one area or one kind of work runs dry, that is a trigger to re-survey and widen scope, not to stop. The whole repo is the field, never just the module you were last in. There is no commit quota and no natural finishing size: thirty small correct commits is a better run than eight, not a tired one. When the high-leverage work is gone, lower the leverage bar and rotate to a different kind of work in a different part of the repo rather than winding down. "Worth doing" means a senior reviewer would thank you, which is a far lower bar than "the single best thing left in the repo."
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.
- 3d ago First seen · 109 lines · 131 tokens per session scan A 3678986d5562
autopilot is a skill published in the GitHub repository saschb2b/okf-studio (47 stars, last pushed yesterday), licensed MIT. It adds 131 tokens to every session and 2,481 once invoked, about $0.0005 per session on Opus 5.5. 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 2026-10-23.
Other skills, from other repositories
lakefs-versioning
Use when lakeFS data versioning — Git-like branching for data lakes, atomic commits, time travel, CI/CD. Use when working with lakefs versioning.
statusline
Install the skrrt Claude Code status line, a Catppuccin Mocha pill bar showing model, folder, git branch, lines changed, context use, and the 5-hour and weekly rate limits. Use when the user asks to install, set up, update, or restore the skrrt status line.
conventional-commits
Write precise Conventional Commits messages from the actual staged diff. Use whenever committing code or asked to write/fix a commit message.
dimension-door
Dimension Door is the quick-switch spell: branch to branch, environment to environment, project to project. It is not a migration and it is not discovery. It assumes both endpoints are known, prepared, and close enough in shape that you can step across with only the context you actually need.
workflow-patterns
Systematic task implementation using TDD, phase checkpoints, and structured commits. Ensures quality through red-green-refactor cycles, 80% coverage gates, and verification protocols before proceeding.
commit-context
Trace a file, function, or line back to the agent session that produced its current commit. Use when the user asks "why is this code here", "what was the agent doing when this changed", "who wrote this", or wants context on a specific location in the codebase.