oneshot
Skill5 starsChanged 6 days agoMITView on GitHub
Low-attention workflow for experiments, internal tools, prototypes, and mechanical migrations. Define a machine-checkable finish line, then loop autonomously until it is met. Use when work is off the critical path and the model's judgment can stand in for the human's.
11 other projects use a version of this.
Adopted by
Most recently taken up by
- ADWilkinson/astacktheir own version, first seen Nov 8, 2026
- scalation-ai/oneshottheir own version, first seen Nov 1, 2026
- coroboros/agent-skillstheir own version, first seen Oct 29, 2026
- 2bTwist/dotagentstheir own version, first seen Oct 13, 2026
- Aurealibe/claude-configtheir own version, first seen Jul 10, 2026
- vicentedomus/claude-skillstheir own version, first seen Jun 26, 2026
Used with
Projects with it also use brainstorm.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
nicolasestremSkillbrainstorm
Structured idea generation and solution exploration with systematic analysis.
Also in 3 projects that use this
Other versions11 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 11
- Aurealibe/claude-config★ 68same name
- coroboros/agent-skills★ 4same name
- GlamgarOnDiscord/claude-saas-blueprint★ 2same name
- scalation-ai/oneshot★ 2same name
- corygabrielsen/skills★ 1same name
- 2bTwist/dotagents★ 0same name
- ADWilkinson/astack★ 0same name
- codebyalexx/claude-skills★ 0same name
- TheWatcher01/skills★ 0same name
- TheYMK/agent-skills★ 0same name
1 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
The file
We split this skill into the rules it gives an agent
3 rules. 0 are common, 2 are distinctive, and 1 is very specific to this project.
0
Common
Most of the 9 edited versions we read keep these.
2
Distinctive
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 13
Finish line first: Write down, before any code, a check a script can evaluate: a test suite passes, a CLI command prints the expected …
- Distinctive: line 16
If no such check exists, the first task is building it.
- Very specific: line 20
Don't ask the human questions.
“Common” is measured: kept by at least 6 in 10 of the 9 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Oneshot
Spend model intelligence instead of human attention. The human should be able to start this and forget it until the result is ready.
Steps
- Finish line first. Write down, before any code, a check a script can evaluate: a test suite passes, a CLI command prints the expected output, CI is green, a parity check between old and new implementation passes. If no such check exists, the first task is building it.
- Scaffold in a new branch or directory
oneshot/<slug>, isolated from critical-path code. - Loop: implement → run the finish-line check → read failures → fix.
Don't ask the human questions; make a reasonable choice and note it in
DECISIONS.mdin the working directory. - Large mechanical work (ports, migrations, codemods): build a small
harness instead of brute-forcing it:
- enumerate units of work in dependency order (e.g. walk the import graph)
- hand each unit to a subagent with a brief written with the
delegateskill - a check that fails if the new side falls behind the old (counts, parity tests)
- Review before reporting: low attention means the human won't catch what
you miss. Once the finish line passes, have the
playbook-reviewersubagent (or another fresh subagent) review the diff for correctness bugs, especially on real-world inputs the fixtures don't cover (other quote styles, redirects, encodings). Fix confirmed findings. - Stop conditions: finish line met and reviewed → report. Same failure 3 times, or the approach is clearly wrong → stop and report what you learned and what you'd try next. Never loop indefinitely.
Tools
- Claude Code:
/loop(self-paced) or a workflow script for fan-out. - bb: a child thread in a worktree; or
bb automation create --in 1m ...to run it off the main thread entirely.
Done
One message: what was built, where it is, the finish-line result, and the
contents of DECISIONS.md.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
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- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.