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Found 690 Skills
Generate HeyGen presenter videos via the v3 Video Agent pipeline — handles Frame Check (aspect ratio correction), prompt engineering, avatar resolution, and voice selection. Required for any HeyGen video generation. Replaces deprecated endpoints with v3. Use when: (1) generating any HeyGen video (via API or otherwise), (2) sending a personalized video message (outreach, update, announcement, pitch, knowledge), (3) creating a HeyGen presenter-led explainer, tutorial, or product demo with a human face, (4) "make a video of me saying...", "send a video to my leads", "record an update for my team", "create a video pitch", "make a loom-style message", "I want to appear in this video", "generate a HeyGen video", "make a talking head video". Accepts avatar_id from heygen-avatar for identity-first HeyGen videos, or uses a stock presenter. Returns video share URL + HeyGen session URL for iteration. Chain signal: when the user wants to create/design an avatar AND make a video in the same request, run heygen-avatar first, then return here. Conjunctions to watch: "and then", "and immediately", "first...then", "X and make a video", "design [presenter] and record" = always CHAIN. If the user provides a photo AND wants a video, route to heygen-avatar first. NOT for: avatar creation or identity setup (use heygen-avatar first), cinematic footage or b-roll without a presenter, translating videos, TTS-only, or streaming avatars.
Detect and flag personally identifiable information (PII) in text, code, and configurations
Generates, validates, and persists a Figma personal access token to FIGMA_TOKEN. Use this skill whenever a Figma token is needed, missing, expired, or must be refreshed — before any task that calls the Figma API. Triggers on: "generate figma token", "create figma token", "set up figma token", "update figma token", "FIGMA_TOKEN missing", "FIGMA_TOKEN not set", "FIGMA_TOKEN expired", "figma token invalid", "figma authentication", "configure figma access", or any task that requires Figma API access and the token is absent or invalid. Works by checking for an existing valid token first, then auto-login with FIGMA_USERNAME/FIGMA_PASSWORD if available, otherwise falls back to manual login — no manual copy-paste required.
Meeting manager persona for Spark. Meeting preparation, transcript review, follow-up drafts, and scheduling.
The Jobs-to-be-Done framework as applied product methodology. Job statements, struggling moments, hire and fire criteria, the difference between feature-thinking and job-thinking. Honest about where JTBD adds clarity (discovery, prioritization, positioning) and where it becomes performative ritual (job-statement workshops that do not drive decisions, persona-theater disguised as JTBD). Triggers on jobs-to-be-done, JTBD, job statements, struggling moments, hire criteria, fire criteria, switch triggers, functional emotional social jobs, outcome-driven innovation. Also triggers when a team is over-relying on feature-request lists or persona archetypes that do not drive product decisions, when a positioning conversation needs the framing JTBD provides, or when discovery is producing outputs that do not connect to product strategy.
Convert a local AGENT.md into a Claude Code optimized agent. Audits one agent against Claude Code runtime behavior, creates a per-agent DAG rewrite plan with source-backed guardrails, and optionally rewrites the frontmatter and system-prompt body so the agent is thinner, more role-specific, and better aligned with Claude's agent runtime. Use when the user says "convert this agent to Claude", "normalize this AGENT.md", "thin this agent", or "rewrite this persona for Claude Code".
Community stock lists (Sharelist) via Longbridge Securities — browse popular and personal lists, view list details and constituents, create/delete/manage your own lists, and add/remove symbols. Like a public watchlist that other users can subscribe to. Read operations require no login; write operations (create/delete/add/remove/sort) require login. Triggers: "股票清单", "公开清单", "热门清单", "社区选股", "选股清单", "股票列表", "清单管理", "股票清單", "公開清單", "熱門清單", "社區選股", "選股清單", "訂閱清單", "sharelist", "stock list", "public watchlist", "popular list", "community picks", "stock collection", "create list", "manage list", "subscribe list".
Build and maintain a persistent markdown wiki that an LLM updates on the user's behalf, usually inside an Obsidian vault or git-tracked notes repo. Use when raw sources such as web articles, papers, meeting notes, transcripts, screenshots, or past analyses need to be turned into an interlinked knowledge base with immutable source files, LLM-written wiki pages, `index.md`, `log.md`, schema rules in `AGENTS.md` or `CLAUDE.md`, source summaries, query notes, and recurring lint passes. Triggers on: llm-wiki, personal wiki, obsidian wiki, research vault, knowledge base, source ingest, persistent notes, wiki maintenance, source summaries, query filing.
Guides cybersecurity deception operations using MITRE D3FEND—honeynets, decoy objects, decoy personas, and decoy credentials. Covers honeypot deployment, decoy file planting, credential baiting, and deception environment design. Use when deploying honeypots, planting decoy data, baiting credentials, or designing deception programs—not for detection (d3fend-detect), hardening (d3fend-harden), or isolation (d3fend-isolate).
Full-stack e-commerce marketing strategy builder. Analyzes your product, market, and competitors, then builds a complete omnichannel marketing plan covering paid ads, SEO, email/SMS, content marketing, social media, influencer partnerships, and referral programs. Includes target audience persona, competitive landscape, channel prioritization with budget allocation, content direction, and a 90-day action plan. Works for any e-commerce platform — Shopify, Amazon, Etsy, WooCommerce, TikTok Shop, and more. No API key required.
Create AI influencer or branded character personas.
Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot learning, creating system prompts with personas and guardrails, building JSON/function-calling schemas, or developing prompt evaluation frameworks to measure and improve model performance.