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Extract Feishu (Lark) Docs, Wiki pages, Wiki collections/hubs, spreadsheets, and Minutes (妙记) transcripts into clean high-fidelity local Markdown. The primary path is the lark-cli API — programmatic extraction with no LLM rewriting of the body — which recursively follows a collection's reference graph (mention-doc / sheet / cross-tenant links) and uses error codes to resolve permission boundaries precisely; a browser-DOM path is the fallback only when lark-cli cannot reach the content. Use this whenever the source is a Feishu/Lark URL and fidelity matters — including 导出飞书文档/合集/妙记转写, 把飞书 wiki/知识库转 markdown, scraping or archiving a Feishu collection, exporting a Feishu Minutes/妙记 transcript, or saving a Feishu page locally — even if the user only says clipping, archiving, converting, or "save this". Also covers the permission-denied path (owner-exported .docx → faithful Markdown with heading/highlight restoration).
Use this skill when the agent needs to interact with CLAWLOGIC prediction markets. This includes: registering as an agent on-chain, creating new prediction markets, analyzing market questions to form opinions, buying YES/NO positions, asserting market outcomes via UMA Optimistic Oracle, disputing incorrect assertions from other agents, settling resolved markets to claim winnings, and posting bet narratives ("what I bet and why") to the frontend feed. Triggers: - "create a market about..." - "what do you think about [market question]?" - "buy YES/NO on market..." - "assert the outcome of market..." - "dispute the assertion on market..." - "check my positions" - "settle market..." - Any discussion about prediction markets, trading, or information markets
Expert in using ktx, the executable context layer for data and analytics agents that enables accurate querying through MCP with skills, memory and a semantic layer
Turn papers, technical articles, or knowledge content into highly realistic AIGC slides. First create a narrative structure and page-by-page visual direction, then call an image generation model to produce a 16:9 slide image for each page, and finally synthesize into PPTX/PDF. Suitable for paper presentations, group meetings, open courses, technical sharing, and commercial research presentations; use this skill when users mention "paper PPT", "AI-generated PPT", "PPT that doesn't look like AI", "high-quality slides", or "page-by-page AI-generated PPT".
Create a complete SPEC from scratch through an exhaustive requirements interview before any planning or implementation. Use this skill whenever the user asks to create, define, clarify, scope, or write a spec/SPEC/PRD/requirements document from an idea, especially when they want to avoid assumptions, start at "step zero," or prepare input for later planning workflows. This skill must question goals, requirements, constraints, edge cases, business rules, and acceptance criteria before drafting the final spec.
Telnyx integration. Manage Accounts, PhoneNumbers, Medias, Conferences. Use when the user wants to interact with Telnyx data.
Comet Phase 3: Planning and Building. Invoke with /comet-build. Develop a plan and select an execution method (subagent or direct execution) for implementation.
Comet Preset Path: Non-bug Minor Tweaks. Skip brainstorming and full plan, directly proceed with open → lightweight build → light verify → archive. Suitable for partial optimizations of copy, configurations, documents, or prompts.
Comet Phase 1: Open. Invoke with /comet-open. Explore ideas and create change structure (proposal + design + tasks) via OpenSpec.
Use when planning, splitting, shrinking, or sequencing large features, refactors, migrations, or pull requests into small reviewable PRs or CLs. TRIGGER on "split this PR", "this diff is too large", "stacked PRs", "change sequence", "reviewable chunks", "migration plan", "how should I break this up?", or implementation scope too broad for one review. DO NOT TRIGGER for full code review, PR description writing, or tiny one-file edits that already form one self-contained change.
Score a LinkedIn post using real performance data. Pulls the user's own post history via Apify (or uses cached data) to identify what actually performs, then scores the draft against those patterns. Use this skill whenever the user says "score my post", "review my post", "rate this post", "give me feedback", "how good is this post", or pastes a LinkedIn post and asks for critique. Scores against real data, not generic advice. Designed for live scoring at events and everyday post review.
Create LinkedIn post graphics. Decides between an HTML/CSS structured graphic or an AI-generated infographic based on the post content. Use this skill whenever the user says "design a graphic", "create a visual", "make an image", "graphic for my post", "LinkedIn image", or wants any visual content to pair with a LinkedIn post. Also trigger when the user finishes writing a post and wants a matching graphic.