discover

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Proposes what to build next: ranked, cited feature candidates from market research and real usage. Also evaluates tools, platforms, and libraries you are considering switching to.

Code & Development 10 stars 0 forks Updated yesterday MIT

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Description 5%
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Skill Content

# discover — propose features, grounded in the market and real usage Turn "what should we build?" into a ranked, evidence-backed set of proposals — not a brainstorm of guesses. You read the product, research the world, factor in who actually uses it, and hand the chosen feature to `define`. Read [CONVENTIONS.md](../../CONVENTIONS.md) for the workspace (§1), the resume sweep (§5), git isolation (§11), intake schema (§3), memory (§4), the role dial (§6), integrations (§10), the close-out summary (§13), data-driven decisions (§19), and — critically here — **grounding & no-hallucination (§14)** and **freshness (§17)**. Everything you produce is a **verified fact with a source** or a **clearly-labeled suggestion**. Never invent a competitor, a statistic, a user count, or a source, and the ledger (§2), workspace integrity (§20). **Boundary with `assess`:** if the real question is *"are our existing features good enough?"*, that's `assess` (whole-app health) — run it, or read its latest `assessment.md`, and treat its "Recommended next moves" as this skill's input. `discover` leads when the question is *what net-new thing to build or adopt*. ## Step 1 — Understand the product as it is Scan the repo/app: what features exist, the domain, the stack, and what it already does well. If a live app, docs, or a connected tool (§10) is available, read it. Note the obvious gaps. State facts you can see; don't assume features you can't confirm. ## Step 2 — Scan the market (web, cited) Est...

Details

Author
saleh-alhaddad
Repository
saleh-alhaddad/itqan-engineering
Created
1 months ago
Last Updated
yesterday
Language
Shell
License
MIT

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Use when deciding what to build or whether to build it, when a product idea needs validating, when a metric moved and nobody knows why, when planning or running user interviews, when raw research needs synthesising, when designing an experiment or prototype test, when sizing or prioritising opportunities, when auditing a PRD or roadmap for unsupported claims, or when setting up a continuous discovery practice.

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