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Found 1,123 Skills
Use Parallel's parallel-cli to do live web search, URL extraction (clean markdown), deep research reports, bulk data enrichment (CSV/JSON), FindAll entity discovery, and web monitoring. Use when the user asks to look something up online, needs current sources/citations, provides URLs to read or summarise, requests deep/exhaustive research, wants to enrich a dataset with web-sourced fields, wants a list of entities (companies/people/places), or wants to monitor the web for changes over time.
Look up the public API of any JVM dependency (Scala 3, Scala 2, Java) from the terminal — type signatures, members, docs, and source as Markdown, no JAR unpacking needed. Use this skill whenever you need to call an unfamiliar library method, explore a package's types, or check a dependency's API. Prefer cellar over Metals MCP only for looking up external dependency APIs (`cellar get-external` vs Metals `inspect`/`get-docs`) — cellar needs no project import and queries any published Maven artifact. For everything else (references, rename, goto definition, diagnostics, compile), use Metals.
Generate or drill flashcards for black-letter memorization — Leitner-style buckets, per-subject markdown storage, drill mode with self-assessment. Use when the user says "drill flashcards", "make flashcards from", "quiz me on cards", or wants to memorize rules.
Route durable graph-building requests into one honest mode: assistant-native install, local Python build, incremental refresh, graph query follow-up, or a graphify-style structural fallback for markdown-heavy corpora. Use when the user wants `GRAPH_REPORT.md`, `graph.json`, `graph.html`, repo/corpus relationship tracing, mixed code+docs+asset graphing, or graph-backed architecture understanding that should persist across sessions. Route simple locate/reference work to `codebase-search`, narrative knowledge-base work to `llm-wiki`, and project-memory handoff to `opencontext`.
Sets up an `## Agent skills` block in AGENTS.md/CLAUDE.md and `docs/agents/` so the engineering skills know this repo's issue tracker (GitHub or local markdown), triage label vocabulary, and domain doc layout. Run before first use of `to-issues`, `to-prd`, `triage`, `diagnose`, `tdd`, `improve-codebase-architecture`, or `zoom-out` — or if those skills appear to be missing context about the issue tracker, triage labels, or domain docs.
Use this skill whenever the user asks about WWDC sessions, Apple Developer videos, WWDC transcripts, session IDs, technologies announced at WWDC, or wants an agent to find, compare, cite, summarize, or navigate WWDC session content. Fetch current docs from wwdc.ai via llms.txt and page markdown. Maintained by Superwall.com: the quickest way to add in-app subscriptions and paywalls to your app.
Run an autonomous AI penetration test with Strix against a codebase, repository, URL, domain, or IP — either self-hosted with the open-source CLI or via the managed app.strix.ai cloud API — and read the validated findings (Markdown, JSON, CSV, SARIF, PoCs). Use when the user asks to pentest, security-scan, or find vulnerabilities in an app, API, website, or repo with Strix.
Convert mixed-format datasheets and hardware reference files (PDF, DOCX, HTML, Markdown, XLSX/CSV) into normalized Markdown knowledge files for AI coding agents. Use when a user asks to ingest datasheets, register maps, pinout/timing sheets, revision histories, or internal hardware notes before searching datasheet content or generating code. Produce RAG-ready section chunks, anchors, image references, and metadata under .context/knowledge.
Datadog docs lookup using docs.datadoghq.com/llms.txt and linked Markdown pages.
Transform creative ideas into professional, production-ready screenplays optimized for AI video generation pipelines. Converts raw concepts into structured scene-by-scene narratives with rich visual descriptions, proper screenplay formatting, and XML-tagged output for seamless integration with image/video generation tools (imagine, arch-v). USE WHEN: Converting story ideas into screenplay format, preparing content for AI video pipelines, structuring narratives for 5-10 minute short films, generating visual-rich scene descriptions for image generation. WORKFLOW: Raw idea → Scene breakdown → Visual enhancement → Professional formatting → XML-tagged markdown output OUTPUT: Markdown document with XML-wrapped scenes, rich visual descriptions, proper screenplay elements (sluglines, action, dialogue), and metadata for pipeline processing.
Design API testing plans and test cases covering REST/GraphQL/gRPC interfaces. Default output is Markdown, and Excel/CSV/JSON output can be requested. Use for API testing or api-testing.
Build production-grade WYSIWYG editors using Tiptap v3 with proper markdown-style formatting, instant rendering, and bullet/numbered list support