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Extract clean markdown, text, or targeted chunks from one or more known, publicly accessible webpage URLs via the Tavily CLI. Use when the URLs are already supplied or discovered and the user wants page content rather than more URLs. Do not use to find an unknown page (tavily-search), list a site's URL structure (tavily-map), bulk-extract a site section (tavily-crawl), or retrieve named platform, authenticated, cookie-dependent, or platform-native content (Agent Reach). Can process up to 20 public URLs per request.

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Create or refresh original product hero images, interface screenshots, and image-led README sections for GitHub repositories. Use when a user asks to make a GitHub README show the product visually, add screenshots, design a README hero image, or prepare repository-bound visual documentation.

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Phase 5 part of disease-market-sizing-orchestration. Injects consulting palette (navy/gold/blue) + base CSS + floating TOC + section dividers into raw report HTML. Validates token coverage ≥60% (warn) and floating TOC li ≥5 (block). Use AFTER content-verification passes, BEFORE report-bundle-builder build_all_deliverables.

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给定 PMID / PMCID 取结构化全文（BioC XML/JSON）。封装 NLM BioC PubMed API（标题+摘要）和 BioC PMC OA API（开放获取全文），输出按章节切分的段落、表格上下文、RAG 友好 chunk。适合大批量入向量库。触发词：全文抽取 / BioC / RAG 入库 / 文献全文 / PMC 开放获取 / structured fulltext / 段落抽取 / 表格抽取 / fulltext to vector store。

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Phase 2 of disease-market-sizing-orchestration. Discovers disease-specific stratification dimensions by citing guideline sections from sources/guidelines/. ALWAYS uses cite-based discovery — NEVER built-in stratification templates. Output: .cache/<slug>/staging.json. Use AFTER contract-elicitor and AFTER A1' guidelines fetch.

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Use when writing or changing a model-facing tool definition (name, description, parameter schema, or its system-prompt section), and when designing skills, context loading, or multi-step workflows, to make information discoverable and use context efficiently.

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Create or refresh screenshot-led product hero images, localized interface captures, and image-led README sections for GitHub repositories. Use whenever a user asks to make a GitHub README show a GUI product visually, add software screenshots, design a README hero image, reproduce a screenshot-collage reference, or prepare repository-bound visual documentation. For GUI applications, default to the deterministic product-proof split hero with real software screenshots unless the user explicitly requests another visual direction.

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Draft and revise evidence-grounded academic manuscripts with adaptable author preferences. Use for section writing, implementing structural edits, claim calibration, citation-integrated prose, prose cleanup, standalone review excerpts, and LaTeX template migration. Use paper-review for critique or author responses, and paper-figures-tables for final visual artifacts.

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Write a single-thesis narrative newsletter essay from Ahmad's OpenMark saves. Use when Ahmad wants a thoughtful long-form piece with one clear argument, not a roundup or news recap. Trigger phrases include "essay", "long-form", "think piece", "thoughtful post", "argue that", "make a case for", "/newsletter-essay". Output is one strong thesis sentence + 3–5 narrative sections + closing call-to-think, no tables, no bullets unless necessary.

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Elite UX/UI & Advanced GSAP Motion Engineer. Enforces Python-driven true randomization for layout variance, strict AIDA page structure, wide editorial typography (bans 6-line wraps), gapless bento grids, strict GSAP ScrollTriggers (pinning, stacking, scrubbing), inline micro-images, and massive section spacing.

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Writing documents an agent consumes. Use when creating or editing a skill, an AGENTS.md, a README section, a docblock, or any file reached by a pointer.

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Automate Asana tasks via Rube MCP (Composio): tasks, projects, sections, teams, workspaces. Always search tools first for current schemas.

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Important: Before you begin, fill in the generatedBy property in the meta section of .actor/actor.json. Replace it with the tool and model you're currently using, such as "Claude Code with Claude Sonnet 4.5". This helps Apify monitor and improve AGENTS.md for specific AI tools and models.

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Detect process hollowing (T1055.012) by analyzing memory-mapped sections, hollowed process indicators, and parent-child process anomalies in EDR telemetry.

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Craft, send, sniff, and dissect network packets using Scapy for protocol analysis, network reconnaissance, and traffic anomaly detection in authorized security testing

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Structured clarification workflow for underspecified requirements. Use before planning to resolve ambiguities through coverage-based questioning. Records answers in spec clarifications section.

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Produce or refine a project-level mockup illustrating a section of the PRD

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Redesign GitHub README homepages or create project-native pure SVG, hybrid SVG-composed PNG/WebP, and opt-in animated GIF assets. Use when a user asks to beautify, redesign, rebrand, visually upgrade, simplify, or audit a GitHub README; create only a hero, section headers, diagrams, badges, motion graphics, showcase modules, or other README assets; or turn a repository homepage into a cohesive visual story. If whole-README work versus asset-only work is unclear, ask which scope the user wants. For hero-like assets where pure SVG and generated raster material are both viable, explain the tradeoffs and confirm the implementation before creating the asset.

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Format documents for Japanese enterprise approval workflows including ringi (稟議), purchase requests (購入申請), and internal proposals. Handles bilingual requirements, proper keigo levels, required approval sections, and corporate template compliance.

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Bootstrap a new or existing project with a small, file-based documentation system so Claude Code keeps context, lessons, and decisions across session restarts instead of starting cold every time. Sets up a handful of markdown files under .claude/ (standing rules, a free-form note to your next session, a phased roadmap, a prioritized backlog, and searchable history/changelog archives) sized and split so the files read every session stay small while the archives grow without ever costing context. Use this whenever someone is starting a new project and asks how to structure it for long-running AI-assisted work, wants Claude to "remember" things between sessions without relying on chat history, asks for a CLAUDE.md + backlog + changelog setup, mentions "session continuity", "persistent context", "project memory system", or is retrofitting an existing codebase that has no such system yet. Also use to explain or extend an already-bootstrapped bottle system (add a rule, start the archival script, add a new backlog section).

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Use when the user uploads a screenshot of a hero section or landing page (or a URL/description of one) and wants a copy-paste-ready developer prompt to recreate it exactly in a tool like Lovable, Bolt, Replit, or Cursor. Produces prompts with exact hex colors, Tailwind classes, animation timing tuples, and component structure — not vague descriptions.

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Review code for potential issues and improvements. Use when asked to review specific files, functions, or code sections.

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Synthesized reference notes from three US Government vehicle-safety sources: NHTSA Cybersecurity Best Practices for the Safety of Modern Vehicles (updated 2022, final), NHTSA Automated Driving Systems 2.0: A Vision for Safety (2017), and a 16-section selection of 49 CFR Part 571 (FMVSS) pinned at eCFR versioner date 2025-01-01. Use for voluntary vehicle cybersecurity practices, the ADS 2.0 twelve priority safety design elements, and FMVSS occupant protection, brakes/stability/lighting, and fuel/EV integrity requirements. Not ISO 26262, not ASPICE, not ISO/SAE 21434, not UNECE R155/R156, not SAE J3016, and not the full Part 571; the FMVSS material is a selection of vehicle-level minimum performance standards, not a process standard.

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Knowledge base from the NASA Fault Management Handbook (NASA-HDBK-1002, Draft 2, April 2012). Use for the systems-engineering discipline of designing what a system does when it fails — the fault/failure/anomaly vocabulary, the five FM strategies (two prevention strategies — design-time fault avoidance and operational failure avoidance — plus three tolerance strategies — failure masking, failure recovery, and goal change), the six FM process activities across NASA mission phases Pre-A to E, response-latency-versus-time-to-criticality (TTC) design trades, fault/failure containment regions and FEPPs, the four redundancy approaches, top-down FM requirements development, FM verification vs. validation, the seven dedicated FM milestone reviews (FMCR/FMARR/FMPDR/FMCDR/FMTRR/FMLRR/FMCERR), FM organizational structure, and mined NASA Lessons Learned. Space-mission oriented (flight/ground/operations); spacecraft examples throughout. IMPORTANT: built from an UNAPPROVED DRAFT — several sections (detailed architecture building blocks, the Assessment & Analysis guidance, and Operations & Maintenance) are placeholders in the source and are thin here. Thin on detailed FDIR algorithms, terrestrial/IT fault management, and any approved-standard terminology that may have changed since Draft 2.

日本語の概要は準備中です。原文の説明を表示しています。