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Found 5,539 Skills
Use when doing upstream market-research methodology — sizing a market as TAM/SAM/SOM computed BOTH top-down and bottoms-up (never a single unsourced number), planning a survey sample size with finite-population correction and per-segment minimums, or scoring candidate market segments against Kotler's measurable/substantial/accessible/differentiable/actionable criteria. Outputs always show the method and the assumptions. For market-research analysts and product-marketing at the sizing/survey/segmentation moment. Distinct from marketing-skill (campaign analytics, attribution, demand-gen) — this is the evidence-building methodology, not live-campaign optimization.
Find working Deepgram integration examples with third-party platforms and frameworks. Use whenever someone wants to integrate Deepgram with Twilio, LiveKit, LangChain, Vercel AI SDK, Discord, Vonage, Pipecat, Expo, FastAPI, Cloudflare Workers, Slack, Telegram, LlamaIndex, Zoom, Next.js, Nuxt, Django, SvelteKit, NestJS, Spring Boot, CrewAI, Riverside, SignalWire, and more. Examples are full runnable integration demos, not minimal feature snippets.
Use when validating that a real-world problem exists before defining JTBD or writing code. Triggers on "is there demand for this?", "how do I validate this idea?", "should we build this?", or before any MVP scope decision. Combines multiple signal sources to produce a Problem Statement with confidence level.
Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing academic-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.
Produce a comprehensive, evidence-grounded prioritized action plan from any PM input (notes, transcripts, drafts, executive asks, Slack threads, or a raw situation). Outputs one saveable document with an executive summary, input mirror, situation classification (Cynefin), the binding constraint (Theory of Constraints), prioritized questions and open decisions, a ranked action plan with the critical effort plus follow-ons, risks and pre-mortem, copy/paste prompts for downstream pm-skills, and an evidence map. Builds a source ledger and cites exact input quotes; refuses High-confidence plans for Complex or Chaotic situations. Use when you want the critical next effort and how to execute it.
Use when creating or revising model PR optimization history documents for SGLang, vLLM, or another serving framework that cite GitHub PRs. Requires manual, per-PR source-diff review and documentation of motivation, key implementation approach, most important code excerpts, reviewed files, and validation implications instead of generated or one-line summaries.
Analyze source code and produce an enterprise-quality, domain-organized Wiki under `.nium-wiki/`. Trigger on: "generate wiki", "create docs", "update wiki", "rebuild wiki", or any documentation generation request. Capabilities: - Semantic code analysis — understands logic, not just structure - Auto-generated Mermaid diagrams (architecture, data flow, class, dependency) - Bidirectional cross-linking across all documents - SHA256-based change detection for incremental rebuilds - Every section traces back to source via relative path links - Multi-language output (zh/en/ja/ko/fr/de and more)
DGX Cloud Lepton managed GPU compute platform with run/status/cancel interface. Use when submitting TAO jobs to DGX Cloud, dispatching training/eval/inference to Lepton GPU resources, or managing Lepton workspace deployments. Trigger phrases include "run on Lepton", "submit to DGX Cloud", "Lepton job", "managed GPU on DGX Cloud".
Ingest raw context the user pastes or points at — a ticket, a design doc, meeting notes, a spec, a URL, referenced files/paths — and have an agent READ and UNDERSTAND all of it, then synthesize a well-formed feature brief (goal, scope, constraints, and load-bearing unknowns) that feeds sdd-clarify and the sdd-feature-flow harness. Use at the very start of a feature when you have source material instead of a one-line goal, or whenever the user says "here's the context" / "read this" / dumps a ticket or doc.
Triage inbound journalist source queries and draft a response only when the user's expertise is a real fit. Runs each query through proven source-request lenses (4-gate fit triage, credential-standing test, deadline read, BLUF/inverted-pyramid drafting), kills weak fits, asks for missing proof, and never auto-sends.
Gate a pitch against one journalist at a time. Runs the pair through proven media-relations checks (last-10-bylines audit, 90-day topic sweep, beat-vs-angle-vs-one-off, the 5 forms of journalism, source-mirror, stated-preferences, database triangulation) and returns fit, soft-fit, no-fit, or unknown with a real recent anchor and specific edits.
Develop a Base44 app remotely inside Base44's cloud sandbox using your own agent — no local checkout and no deploy/push commands. The implementation is remote: writing a resource file into the sandbox is what ships it (backend functions, entities, and agents all auto-sync from the file you write), and OAuth connectors are set up against the remote app via MCP tools or the projectless `base44 connectors` CLI. This skill is the place for learning what you can author in the sandbox, how backend functions, entities, and agents are structured, and how to connect a connector without a local filesystem. Triggers on 'develop my Base44 app remotely', 'no local files', 'cloud sandbox', 'create an entity/agent remotely', 'connect a connector remotely', 'bring my own agent', or any work editing a Base44 app inside a sandbox.