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Get Started Free →Route a fuzzy request to the right skill in this library. Use when the user is unsure which skill fits, asks 'which skill should I use for X', describes a task without naming a skill, or when a request could plausibly match several skills. Produces a best-fit recommendation with the inputs to gather, a runner-up with the tie-breaker, and a workflow recipe when the job spans multiple skills.
.claude/skills/mohitagw15856-which-skill/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 35% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 16% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 39% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 31% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 54% | 0% |
Given a fuzzy professional ask ("my boss wants an update on the Q3 launch"), pick the single best skill in this library to run — and say why — instead of making the user browse 400+ options.
Ask for (if not already provided):
SKILLS.md (the auto-generated listing grouped by domain), or search with npx pm-claude-skills list / the MCP search_skills tool. Match the user's phrasing against skill description trigger phrases.ab-test-readout for analysing a finished test) beats a broader neighbour (experiment-designer).WORKFLOWS.md, e.g. /ship-a-feature, /launch-a-product) when the ask needs 3+ chained skills — don't recommend the skills one by one.| You want… | Use | Not | |---|---|---| | A one-off deep teardown of a rival (SWOT, positioning map) | competitor-teardown | competitive-analysis | | A full landscape doc: feature matrix, win/loss, battlecard inputs | competitive-analysis | competitor-teardown | | A recurring "what changed in the market this week/month" briefing | competitive-intelligence-monitor | competitor-signal-tracker | | A read on one specific competitor announcement | competitor-signal-tracker | competitive-intelligence-monitor | | Release notes straight from a raw git log / commit list | changelog-generator | changelog-writer | | A Keep-a-Changelog entry from an already-curated change list | changelog-writer | changelog-generator | | Positioning, messaging pillars, use cases — the GTM content | go-to-market | go-to-market-planner | | A tiered launch plan with cross-functional coordination — the GTM operation | go-to-market-planner | go-to-market | | Themes from interview transcripts specifically | user-interview-synthesis | user-research-synthesis | | Synthesis across mixed sources (surveys, feedback, transcripts) | user-research-synthesis | user-interview-synthesis | | Pure RICE scoring of a backlog | rice-prioritisation | feature-prioritisation | | Choosing/applying a framework (RICE, MoSCoW, Kano, ICE) | feature-prioritisation | rice-prioritisation | | RICE blended with strategic-fit weighting | rice-impact-matrix | rice-prioritisation | | A summary of an existing document for executives | executive-summary | executive-update | | A standalone product briefing written for the C-suite | executive-update | executive-summary | | A BLUF-style project status update for stakeholders | stakeholder-update | executive-update | | Designing an experiment before it runs (sample size, guardrails) | ab-test-planner | ab-test-readout | | Analysing a finished test and making the ship/no-ship call | ab-test-readout | ab-test-planner |
Best fit: skill-name — one line: why this artifact matches the ask]
Before you run it, have ready:
Runner-up: other-skill — pick this instead if the tie-breaker condition].
Run it: /skill-name in Claude Code, or open it in the Playground.
(If a workflow fits better) This is a multi-skill job — run /recipe-name (chains a → b → c), because why the chain beats a single skill].
competitive-analysis); route on the deliverableSKILL_REQUEST.md| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 17,127 | 13,663 | -20% | 1 | 1 | 0% | 2,029 | 3,131 | +54% | 0 | 0 | — |
case-02 | fail→pass | 18,237 | 13,034 | -29% | 1 | 1 | 0% | 2,171 | 2,940 | +35% | 0 | 0 | — |
case-03 | fail→pass | 19,109 | 10,088 | -47% | 1 | 1 | 0% | 2,095 | 2,436 | +16% | 0 | 0 | — |
case-04 | fail→pass | 15,537 | 11,134 | -28% | 1 | 1 | 0% | 1,844 | 2,567 | +39% | 0 | 0 | — |
case-05 | fail→pass | 16,336 | 9,993 | -39% | 1 | 1 | 0% | 1,812 | 2,378 | +31% | 0 | 0 | — |
case-06 | fail→pass | 14,438 | 12,652 | -12% | 1 | 1 | 0% | 1,430 | 2,199 | +54% | 0 | 0 | — |
case-07 | fail→pass | 14,247 | 12,148 | -15% | 1 | 1 | 0% | 1,438 | 2,276 | +58% | 0 | 0 | — |
case-08 | fail→pass | 21,615 | 10,314 | -52% | 1 | 1 | 0% | 2,411 | 2,557 | +6% | 0 | 0 | — |
case-09 | fail→pass | 7,635 | 10,575 | +39% | 1 | 1 | 0% | 344 | 2,422 | +604% | 0 | 0 | — |
case-10 | fail→pass | 8,273 | 10,973 | +33% | 1 | 1 | 0% | 1,346 | 2,577 | +91% | 0 | 0 | — |
case-11 | fail→pass | 10,625 | 9,581 | -10% | 1 | 1 | 0% | 967 | 2,267 | +134% | 0 | 0 | — |
case-12 | fail→pass | 12,176 | 9,695 | -20% | 1 | 1 | 0% | 1,925 | 2,345 | +22% | 0 | 0 | — |
case-13 | fail→pass | 14,599 | 9,625 | -34% | 1 | 1 | 0% | 1,520 | 2,333 | +53% | 0 | 0 | — |
case-14 | fail→pass | 9,056 | 10,757 | +19% | 1 | 1 | 0% | 1,330 | 2,543 | +91% | 0 | 0 | — |
case-15 | fail→pass | 12,536 | 4,763 | -62% | 1 | 1 | 0% | 1,147 | 2,309 | +101% | 0 | 0 | — |
case-16 | fail→pass | 15,780 | 23,802 | +51% | 1 | 1 | 0% | 1,698 | 2,263 | +33% | 0 | 0 | — |
case-17 | fail→pass | 9,752 | 11,468 | +18% | 1 | 1 | 0% | 1,474 | 2,639 | +79% | 0 | 0 | — |
case-18 | fail→pass | 18,765 | 7,611 | -59% | 1 | 1 | 0% | 2,241 | 2,881 | +29% | 0 | 0 | — |
case-19 | fail→pass | 5,586 | 12,268 | +120% | 1 | 1 | 0% | 775 | 2,559 | +230% | 0 | 0 | — |
case-20 | pass→fail | 14,980 | 14,492 | -3% | 1 | 1 | 0% | 3,117 | 4,045 | +30% | 0 | 0 | — |
case-21 | pass→fail | 8,503 | 8,411 | -1% | 1 | 1 | 0% | 1,312 | 2,912 | +122% | 0 | 0 | — |
case-22 | pass→fail | 7,934 | 12,966 | +63% | 1 | 1 | 0% | 1,273 | 2,916 | +129% | 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. 22 cases were attempted. The headline lift of +68 percentage points is the difference between those two pass rates over the 22 comparable cases. 3 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.