Route a fuzzy request to the right skill in this library. Us
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.
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills /tmp/route-a-fuzzy-request-to-the-right-skill-in-this-library.-us && cp -r /tmp/route-a-fuzzy-request-to-the-right-skill-in-this-library.-us/exports/continue/pm-essentials/which-skill/which- ~/.claude/skills/route-a-fuzzy-request-to-the-right-skill-in-this-library.-uswhich-skill.md
# Which Skill Router
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.
## What This Skill Produces
- The **best-fit skill** for the request, with a one-line justification
- The **inputs to gather** before running it (from that skill's Required Inputs)
- A **runner-up skill** and the tie-breaker that separates them
- A **workflow recipe** recommendation instead, when the job genuinely spans 3+ skills
## Required Inputs
Ask for (if not already provided):
- **The task in the user's own words** (even one sentence is enough)
- **Who the output is for** (audience changes the pick: a board deck is not a team update)
- **One-off or recurring?** (a monitor/briefing skill differs from a one-time analysis)
## Routing Method
1. **Name the artifact.** What lands on someone's desk when this is done — a PRD, a ranked list, a briefing, a plan? Route on the deliverable, not on topic keywords.
2. **Search the catalog — never route from memory.** Read `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.
3. **Prefer the specific skill over the general one.** A skill built for the exact artifact (e.g. `ab-test-readout` for analysing a finished test) beats a broader neighbour (`experiment-designer`).
4. **Check the disambiguation table below** for the known look-alike clusters before answering.
5. **Escalate to a workflow recipe** (see `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.
6. **Recommend, don't interrogate.** Ask at most one clarifying question, and only when the answer would change the pick.
## Disambiguation Table — look-alike clusters
| 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` |
## Output Format
### Skill Recommendation
**Best fit:** `skill-name` — [one line: why this artifact matches the ask]
**Before you run it, have ready:**
- [input 1 from that skill's Required Inputs]
- [input 2]
**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](https://mohitagw15856.github.io/pm-claude-skills/).
*(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].
## Quality Checks
- [ ] The pick was verified against the live catalog (SKILLS.md / search), not recalled from memory
- [ ] Every look-alike cluster the ask touches was checked against the disambiguation table
- [ ] The recommendation names the concrete artifact the user will get, not a topic
- [ ] The runner-up includes a real tie-breaker condition, not "also good"
- [ ] Multi-skill jobs point to one workflow recipe, not a list of 4 skills to run manually
## Anti-Patterns
- [ ] Do not recommend more than two skills — a router that returns a list has not routed
- [ ] Do not route on topic keywords ("competitor" ≠ always `competitive-analysis`); route on the deliverable
- [ ] Do not ask a chain of clarifying questions — one at most, and only if it changes the pick
- [ ] Do not invent skill names — if nothing in the catalog fits, say so and suggest `SKILL_REQUEST.md`
- [ ] Do not recommend a general skill when a specific one exists for the exact artifactConduct a structured ethical review of an AI or ML feature, model, or product. Use when preparing to deploy an AI system, assessing algorithmic risk, auditing a model for bias, or producing a responsible AI impact assessment. Produces a structured ethics review covering fairness, transparency, privacy, safety, accountability, and societal impact with a risk tier score, pre-deployment checklist, and prioritised mitigations.
Structure AI and ML product decisions with the rigour of any product decision. Use when building AI-powered features, evaluating LLM integrations, designing AI products, or assessing AI readiness. Produces a complete AI product canvas covering problem definition, model approach, data requirements, evaluation framework, UX design, responsible AI checklist, and launch monitoring plan.
Transform feature briefs into structured design briefs that give designers the context they need before opening Figma. Use when asked to write a design brief, create a design handoff, brief a designer on a new feature, or translate a PRD into design requirements. Produces a brief with user goal, emotional context, success criteria, constraints, edge cases, and out-of-scope boundaries.
Design statistically rigorous A/B tests and interpret experiment results. Use when asked to design an experiment, run an A/B test, calculate sample size, interpret test results, or assess whether an experiment was successful. Produces a complete experiment design with hypothesis, sample size, run time, success criteria, and risk flags — or a results interpretation with ship/iterate/kill recommendation.
Synthesises user signals from multiple research sources into a unified, weighted insight brief. Use when you have data from interviews, support tickets, NPS verbatims, app reviews, or sales calls and need to reconcile contradictions, surface the underlying need behind requests, or answer 'what are users really telling us'. Produces ranked insights with confidence ratings, source weighting rationale, divergent signal analysis by user segment, and a research gap identification section.
Structure a product data analysis, metric deep-dive, funnel analysis, or cohort study. Use when asked to analyse product metrics, investigate a drop in conversion, explain a data change to stakeholders, or find the root cause of a metric movement. Produces a structured analysis with question, root cause, confidence level, and recommended action.
Interpret product metrics against goals and surface actionable signals. Use when asked to analyse product health, review key metrics, investigate a performance issue, produce a health report, or assess product-market fit signals. Produces a structured health report with RAG status, trend analysis, root cause hypotheses, and prioritised actions.
Structure a retention analysis, churn investigation, or engagement deep-dive for any product team. Use when asked to analyse user retention, investigate churn, measure DAU/MAU, or build a retention improvement plan. Produces a retention snapshot with root cause hypotheses, aha-moment correlation, and prioritised interventions.