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Get Started Free →sangrokjung/loop-forge
Turn a one-line description of a repetitive task into a reusable, self-guarding slash command. loop-forge diagnoses the task into one of 5 loop shapes (Batch / Pipeline / Refine / Watch / Explore), interviews for the blanks, and auto-injects two safety devices the user didn't know they needed — an independent verifier (maker ≠ checker) and a hardstop (a budget/count/cooldown ceiling) — then previews the result and stamps it as a `/command` they can run forever.
Passed 19 of 25 test cases with the skill, and 9 of 25 without it.
and 15 more in the full report
12 fixed7 passed either way4 still failing2 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 12 test cases the skill fixed. Same model, same prompt, with and without the skill.
I requested a global scope command named '/quick-lint'. In what file path relative to the home directory must the command be saved?
| Checked | Without skill | With skill |
|---|---|---|
| Saves the command file to ~/.claude/commands/quick-lint.md. | Missed | Passed |
The instructions your model reads when the skill loads.
/loop-forge)Picture an engraving shop. A customer who can't engrave walks in and just describes the seal they keep needing — "I want to do this over and over." The artisan ① recognizes which of 5 standard molds (loop shapes) it is, ② asks what letters to cut (the blanks), ③ cuts them into a proven mold, ④ automatically fits a "misprint detector" (the verifier) and an "out-of-ink stop" (the hardstop), ⑤ pulls one test impression to show the customer, and ⑥ once they approve, hands back a reusable
/commandthey can stamp again any time — the same in Claude Code and in any other agent via a paste-able prompt.
Someone who doesn't code can't author a reusable /command that makes an AI repeat
a task reliably. loop-forge closes that gap. It takes a vague one-liner and stamps
it into a reusable slash command, while automatically attaching the two safety
devices a non-developer doesn't even know to ask for:
That auto-injection is the decisive difference from a plain prompt generator or a developer-facing skill builder.
Before → after (the elevator demo):
User: "I want to summarize 100 shop reviews into 3 lines each"
│
loop-forge:
1. Diagnoses → Batch loop
2. Interviews → where are the items? per-item task? where does output land? a cap?
3. Auto-injects → verifier (count in == count out + independent spot re-check)
+ hardstop (max_items budget + per-item fail cap + abort at 20% failure)
4. Previews → "here's how it will run" (+ optional 1-item sample)
5. Stamps → /review-summary (reusable forever; safety baked in)
test cases passed, vs 9 / 25 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.