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Get Started Free →Author a workflow — either an MCP workflow template (persisted, lifecycle) or a native .claude/workflows/*.js orchestration script (agent/parallel/pipeline fan-out)
.claude/skills/ruvnet-workflow-create/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-07 | ✗→✓ | ▲ Improved | 57% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 37% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 2% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 21% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 51% | 0% |
Author a workflow on whichever surface fits the job.
.claude/workflows/*.js — an imperative orchestration script that fans subagents out. Use for comprehensive fan-out (review, audit, migration, research) where you aggregate structured results in code.mcp__plugin_ruflo-core_ruflo__workflow_template to see available templatesmcp__plugin_ruflo-core_ruflo__workflow_create with steps, conditions, and execution ordermcp__plugin_ruflo-core_ruflo__workflow_list to see all defined workflowsmcp__plugin_ruflo-core_ruflo__workflow_status to monitor a workflowmcp__plugin_ruflo-core_ruflo__workflow_delete to remove unused workflowsFeatures: sequential/parallel steps, conditional branching, template inheritance, pause/resume approval gates.
.claude/workflows/*.jsWrite a .js file under .claude/workflows/. It MUST begin with a pure-literal export const meta block; the body runs inside an async wrapper (top-level await/return are legal) with these hooks injected:
| Hook | Purpose | |------|---------| | agent(prompt, opts) | Spawn one subagent; pass opts.schema (JSON Schema) to get validated structured output back | | parallel(thunks) | Run thunks concurrently with a barrier — .filter(Boolean) the results | | pipeline(items, ...stages) | Stream each item through stages independently — prefer this over a barrier | | phase(title) / log(msg) | Progress grouping / narration |
jsexport const meta = { name: 'my-workflow', // becomes the invocable name — must be a pure literal description: 'one line', phases: [{ title: 'Find' }, { title: 'Verify' }], } const SCHEMA = { type: 'object', required: ['ok'], properties: { ok: { type: 'boolean' } }, additionalProperties: false } phase('Find') const found = await agent('find the things', { schema: SCHEMA, agentType: 'tester' }) phase('Verify') const checked = await parallel((found.items || []).map((it) => () => agent(`verify ${it}`, { schema: SCHEMA }))) return { found, checked: checked.filter(Boolean) }
Rules: meta is a pure literal (no variables/calls/interpolation); default to pipeline over parallel; never use Date.now()/Math.random() (they throw — vary by index instead). Validate syntax (the body is ESM-in-async-wrapper, not a bare module):
bashnode -e 'const fs=require("fs");let s=fs.readFileSync(".claude/workflows/my-workflow.js","utf8").replace(/^export\s+const\s+meta/m,"const meta");fs.writeFileSync("/tmp/wf.mjs","let agent,parallel,pipeline,phase,log,args,budget,workflow;async function __wf(){\n"+s+"\n}")' \ && node --check /tmp/wf.mjs && echo OK
Run it with the workflow-run skill or Workflow({ name: 'my-workflow' }). Reference: .claude/workflows/plugin-contract-audit.js. See ADR-0002.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-07 | fail→pass | 6,571 | 5,856 | -11% | 1 | 1 | 0% | 1,358 | 2,134 | +57% | 0 | 0 | — |
case-01 | fail→fail | 26,663 | 28,776 | +8% | 1 | 1 | 0% | 6,203 | 2,243 | -64% | 0 | 0 | — |
case-02 | fail→fail | 18,751 | 4,458 | -76% | 1 | 1 | 0% | 4,301 | 1,255 | -71% | 0 | 0 | — |
case-03 | fail→pass | 14,426 | 16,500 | +14% | 1 | 1 | 0% | 3,172 | 4,353 | +37% | 0 | 0 | — |
case-04 | fail→pass | 14,551 | 7,676 | -47% | 1 | 1 | 0% | 2,554 | 2,611 | +2% | 0 | 0 | — |
case-05 | fail→pass | 32,201 | 11,093 | -66% | 1 | 1 | 0% | 2,997 | 3,630 | +21% | 0 | 0 | — |
case-06 | fail→pass | 5,844 | 5,099 | -13% | 1 | 1 | 0% | 1,275 | 1,930 | +51% | 0 | 0 | — |
case-08 | fail→pass | 9,750 | 9,999 | +3% | 1 | 1 | 0% | 2,193 | 3,212 | +46% | 0 | 0 | — |
case-09 | fail→pass | 10,881 | 3,151 | -71% | 1 | 1 | 0% | 2,177 | 1,539 | -29% | 0 | 0 | — |
case-10 | fail→pass | 7,013 | 1,576 | -78% | 1 | 1 | 0% | 1,251 | 1,144 | -9% | 0 | 0 | — |
case-11 | fail→pass | 7,644 | 2,831 | -63% | 1 | 1 | 0% | 1,444 | 1,434 | -1% | 0 | 0 | — |
case-12 | fail→fail | 6,087 | 1,696 | -72% | 1 | 1 | 0% | 1,114 | 1,197 | +7% | 0 | 0 | — |
case-13 | fail→pass | 9,851 | 4,338 | -56% | 1 | 1 | 0% | 1,770 | 1,738 | -2% | 0 | 0 | — |
case-14 | fail→pass | 11,807 | 5,879 | -50% | 1 | 1 | 0% | 2,243 | 2,022 | -10% | 0 | 0 | — |
case-15 | fail→pass | 6,759 | 10,003 | +48% | 1 | 1 | 0% | 1,325 | 3,025 | +128% | 0 | 0 | — |
case-16 | fail→pass | 11,466 | 5,860 | -49% | 1 | 1 | 0% | 2,408 | 1,977 | -18% | 0 | 0 | — |
case-17 | fail→pass | 10,577 | 4,129 | -61% | 1 | 1 | 0% | 1,865 | 1,801 | -3% | 0 | 0 | — |
case-18 | pass→pass | 12,438 | 3,967 | -68% | 1 | 1 | 0% | 2,670 | 1,749 | -34% | 0 | 0 | — |
case-19 | fail→pass | 12,137 | 1,202 | -90% | 1 | 1 | 0% | 2,161 | 1,106 | -49% | 0 | 0 | — |
case-20 | pass→pass | 8,768 | 2,705 | -69% | 1 | 1 | 0% | 1,779 | 1,524 | -14% | 0 | 0 | — |
case-21 | pass→pass | 4,100 | 7,613 | +86% | 1 | 1 | 0% | 925 | 2,058 | +122% | 0 | 0 | — |
case-22 | pass→fail | 11,290 | 6,764 | -40% | 1 | 1 | 0% | 2,075 | 1,069 | -48% | 0 | 0 | — |
case-23 | pass→pass | 12,412 | 10,120 | -18% | 1 | 1 | 0% | 2,614 | 2,608 | -0% | 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, and 20 counted toward the lift figure. The other 3 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +61 percentage points is the difference between those two pass rates over the 20 comparable cases. 2 cases got worse with the skill loaded, and they are included in that figure.
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.