loopable
Skill2 starsChanged 3 months agoMITView on GitHub
Find recurring work worth automating, then take the top candidate through the full delivery flow — plan, /review-plan, implement, /review-deep, fix all findings, commit, and file a ticket in ~/Downloads/tickets/{project}/.
In this repository12 other files it gives its agents.
- battle-testSkill
- choicesSkill
- commitSkill
- frontend-verifySkill
- init-agentsSkill
- loopitSkill
- review-deepSkill
- reviewSkill
- security-auditSkill
- sessionsSkill
- thermo-nuclear-code-quality-reviewSkill
- vulgarizeSkill
The file
We split this skill into the rules it gives an agent
11 rules. About 0 are common, 7 are distinctive, and 4 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈7
Distinctive · estimate
This author's own choices. Worth a look.
4
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 14
Run these, skipping any that don't apply:
- Very specific: line 30
If $ARGUMENTS is scan, STOP here (report only).
- Distinctive: line 32
Hard gate: if the chosen candidate automates deploys, pushes, or production-DB changes — STOP and ask for explicit consent before Phase 3.
- Very specific: line 36
Write a short implementation plan (files to create/edit, trigger, success check, rollback).
- Very specific: line 44
Run the /review-deep skill on the changes.
- Distinctive: line 48
Commit the automation with a clear message (review already done in Phase 5).
- Very specific: line 49
Write a ticket for the change as markdown (summary, what changed, how to verify, rollback) and save it to …
- Distinctive: line 57
Do NOT automate deploys, pushes, or production-DB changes without explicit consent each time.
- Distinctive: line 58
Do NOT pad the candidate table.
- Distinctive: line 59
Do NOT skip or merely summarize review-deep findings — each one gets fixed or explicitly accepted.
- Distinctive: line 60
Do NOT push or deploy anything.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Loopable — find what deserves a loop, then build it
You are an automation scout and builder. Input: $ARGUMENTS (focus area, a candidate number like #2, or scan for diagnose-only).
Phase 1: Collect recurrence signals
Run these, skipping any that don't apply:
- Git history:
git log --oneline -200 --pretty='%ad %s' --date=short— cluster commit subjects; repeated verbs/patterns = recurring chores. Ignore one-day bursts; look for rhythm across days/weeks. - Manual scripts:
scripts/,*.sh,*.command, Makefile targets,package.json/composer.jsonscripts run by a human on a rhythm. - Claude usage:
~/.claude/skill-usage.logfiltered to this project + repeated user prompts in~/.claude/projects/<encoded-cwd>/*.jsonl. - Docs & memory: CLAUDE.md / roadmap.md / README chores ("every", "before each", "remember to"); global rules the project violates (e.g. missing roadmap.md).
- Already automated: husky hooks, launchd, cron, GitHub Actions,
.claude/settings.jsonhooks — exclude these; flag near-duplicate existing skills.
Phase 2: Score, rank, pick
Score candidates on frequency (3+ occurrences), determinism, verifiability (machine-checkable success), blast radius. Map each to ONE mechanism: /loop, /schedule, hook, launchd/cron + claude -p, workflow, or plain script (no LLM needed).
Print the ranked table:
| # | Recurring work | Evidence | Mechanism | Trigger + success check | Risk gate |
If $ARGUMENTS is scan, STOP here (report only). Otherwise continue with the top candidate, or the one named #N.
Hard gate: if the chosen candidate automates deploys, pushes, or production-DB changes — STOP and ask for explicit consent before Phase 3.
Phase 3: Plan + review the plan
Write a short implementation plan (files to create/edit, trigger, success check, rollback). Run the /review-plan skill on it and apply its feedback before touching anything.
Phase 4: Implement
Build the automation exactly as planned (hook in settings.json, script, skill, schedule, etc.). Keep it minimal — no features beyond the plan.
Phase 5: Deep review + address ALL findings
Run the /review-deep skill on the changes. Fix every issue it raises — do not just list them. Re-verify after fixing; repeat until clean or a finding is explicitly accepted with a one-line justification.
Phase 6: Commit + ticket
- Commit the automation with a clear message (review already done in Phase 5). Do NOT push — pushing is a separate, explicit step.
- Write a ticket for the change as markdown (summary, what changed, how to verify, rollback) and save it to
~/Downloads/tickets/{repo-folder-name}/YYYY-MM-DD-<slug>.md(create the folder if missing).
Phase 7: Report
End with: the ranked table, what was implemented (files + mechanism), review-deep findings and how each was addressed, the ticket path, and a short Not worth automating list so weak candidates aren't re-proposed next run.
Do NOT
- Do NOT automate deploys, pushes, or production-DB changes without explicit consent each time.
- Do NOT pad the candidate table; 3 strong candidates beat 10 weak ones. If nothing recurs enough, say so and stop after Phase 2.
- Do NOT skip or merely summarize review-deep findings — each one gets fixed or explicitly accepted.
- Do NOT push or deploy anything; the flow ends at local commit + ticket file.
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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.

