Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Create or improve OpenChatCut custom skills. Use whenever the user asks to create a skill, capture a workflow as a skill, write a SKILL.md, add a reusable workflow, 创建技能, 把流程做成技能, 写一个技能, or improve an existing custom skill. Pushy: treat any workflow the user repeats as a skill candidate, even if they do not say "skill".
.claude/skills/0xsline-skill-creator/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-03 | ✗→✓ | ▲ Improved | 38% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 11% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 37% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 5% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 33% | 0% |
Create or improve custom skills for OpenChatCut. A skill is a SKILL.md file (plus optional references/ support docs) that teaches the agent a reusable workflow. Custom skills live at ~/.openchatcut/skills/<slug>/SKILL.md and are managed with the manage_skill tool.
workflow (tools used, steps, corrections), extract it from the history first. Confirm with the user before writing.
extraction, fixed steps, generation pipelines) or subjective output (writing style)? Suggest test prompts for the former.
manage_skill action=create. The tool reports theinstall path (~/.openchatcut/skills/<slug>/SKILL.md).
results with the user. Iterate with manage_skill action=update.
<slug>/
├── SKILL.md (required — frontmatter + instructions)
└── references/ (optional — docs loaded on demand with load_skill file=)digits, hyphens only). It becomes the load_skill name.
AND specific trigger contexts. Skills undertrigger easily, so be explicit: list concrete user phrases. "When to use" goes here, not in the body.
split detail into references/ files and point to them with clear "read this when" guidance. For references over 300 lines, add a TOC.
---\nname: …\ndescription: …\n---. Theparser accepts plain single-line, double-quoted, and | block scalars; plain single-line is preferred.
MUST-laden lists. Use examples with concrete Input/Output pairs.
(manage_skill, load_skill, edit_item, …). Do not invent tools.
anything that surprises the user. Do not create skills that facilitate unauthorized access or data exfiltration.
times. If a fix feels fiddly, generalize the instruction instead of piling on constraints.
load_skill when a task matches.load_skill file=references/<name>.bundled skill after activation.
[A-Za-z0-9_-]{1,120}. Anything else is rejected(path traversal defense — the file is written under ~/.openchatcut/skills/).
normalize before trusting anything parsed out of it.
manage_skill cannot edit them./ command menuonly when the user selects it; agents also discover it via the index.
npx tsx server/skills-files.verify.ts locally if the environment has the repo, or rely on the in-session test prompts.
Offer the user 2–3 realistic prompts (the kind of thing they would actually type) and ask: "Do these look right, or do you want to add more?" Then run them in the session with the skill activated, review the outputs together, and update the skill from the feedback.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 5,960 | 14,243 | +139% | 1 | 1 | 0% | 1,062 | 2,572 | +142% | 0 | 0 | — |
case-02 | fail→fail | 11,289 | 13,522 | +20% | 1 | 1 | 0% | 1,156 | 2,385 | +106% | 0 | 0 | — |
case-03 | fail→pass | 24,308 | 27,966 | +15% | 1 | 1 | 0% | 4,102 | 5,644 | +38% | 0 | 0 | — |
case-04 | fail→pass | 16,305 | 9,934 | -39% | 1 | 1 | 0% | 1,722 | 1,906 | +11% | 0 | 0 | — |
case-05 | pass→pass | 14,716 | 18,383 | +25% | 1 | 1 | 0% | 2,868 | 3,597 | +25% | 0 | 0 | — |
case-06 | pass→pass | 10,014 | 10,990 | +10% | 1 | 1 | 0% | 1,866 | 3,048 | +63% | 0 | 0 | — |
case-07 | fail→fail | 6,602 | 9,529 | +44% | 1 | 1 | 0% | 1,146 | 1,910 | +67% | 0 | 0 | — |
case-08 | fail→pass | 22,291 | 3,298 | -85% | 1 | 1 | 0% | 1,205 | 1,648 | +37% | 0 | 0 | — |
case-09 | fail→fail | 14,415 | 8,554 | -41% | 1 | 1 | 0% | 1,508 | 1,670 | +11% | 0 | 0 | — |
case-10 | fail→pass | 17,579 | 9,945 | -43% | 1 | 1 | 0% | 1,824 | 1,922 | +5% | 0 | 0 | — |
case-11 | fail→pass | 13,868 | 5,819 | -58% | 1 | 1 | 0% | 1,536 | 2,050 | +33% | 0 | 0 | — |
case-12 | fail→pass | 10,934 | 7,292 | -33% | 1 | 1 | 0% | 992 | 1,452 | +46% | 0 | 0 | — |
case-17 | fail→pass | 13,372 | 6,837 | -49% | 1 | 1 | 0% | 1,517 | 1,365 | -10% | 0 | 0 | — |
case-13 | fail→pass | 10,038 | 6,711 | -33% | 1 | 1 | 0% | 1,614 | 1,291 | -20% | 0 | 0 | — |
case-14 | fail→pass | 11,804 | 12,144 | +3% | 1 | 1 | 0% | 2,120 | 2,297 | +8% | 0 | 0 | — |
case-15 | fail→fail | 13,867 | 5,682 | -59% | 1 | 1 | 0% | 1,724 | 1,902 | +10% | 0 | 0 | — |
case-16 | fail→pass | 13,617 | 6,857 | -50% | 1 | 1 | 0% | 1,652 | 1,390 | -16% | 0 | 0 | — |
case-18 | fail→pass | 14,068 | 3,882 | -72% | 1 | 1 | 0% | 2,095 | 1,688 | -19% | 0 | 0 | — |
case-19 | fail→pass | 10,411 | 3,022 | -71% | 1 | 1 | 0% | 1,455 | 1,564 | +7% | 0 | 0 | — |
case-20 | fail→pass | 20,254 | 9,384 | -54% | 1 | 1 | 0% | 2,503 | 2,582 | +3% | 0 | 0 | — |
case-21 | pass→pass | 13,909 | 8,850 | -36% | 1 | 1 | 0% | 2,491 | 2,418 | -3% | 0 | 0 | — |
case-22 | pass→pass | 18,079 | 9,481 | -48% | 1 | 1 | 0% | 1,809 | 1,708 | -6% | 0 | 0 | — |
case-23 | pass→pass | 17,032 | 9,776 | -43% | 1 | 1 | 0% | 2,591 | 2,033 | -22% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +57 percentage points is the difference between those two pass rates over the 23 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.