Results for “agent-discovery”

Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.

Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.

This skill should be used when the user asks to "create a plugin", "scaffold a plugin", "understand plugin structure", "organize plugin components", "set up plugin.json", "use ${CLAUDE_PLUGIN_ROOT}", "add commands/agents/skills/hooks", "configure auto-discovery", or needs guidance on plugin directory layout, manifest configuration, component organization, file naming conventions, or Claude Code plugin architecture best practices.

Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.

Fast, accurate code search for AI agents using ~98% fewer tokens than grep+read. Indexes any local or remote repository in under a second (~250ms on CPU, no GPU or API key needed). Supports natural-language and symbol queries, semantic similar-code discovery, and MCP server integration for Claude Code, Codex, Cursor, and OpenCode. Python library available for programmatic use. Triggers on: semble, code search, semantic code search, semble search, token-efficient search, find code, code search mcp, agent code search, semble find-related, semble savings.

Build, audit, debug, and migrate websites for durable organic discovery and reliable use by search engines, LLMs, and browser agents. Use for SEO audits or implementation; new sites, routes, templates, CMSs, catalogs, or content programs; crawling, rendering, indexing, robots, canonicals, redirects, sitemaps, hreflang, structured data, metadata, links, faceted navigation, JavaScript SEO, Core Web Vitals, or migrations; keyword, intent, architecture, content, local, media, or authority work; traffic, ranking, CTR, or indexation losses; AEO, GEO, AI Overviews, AI Mode, ChatGPT search, crawler controls, agent-readable pages, Markdown representations, `Accept: text/markdown` content negotiation, `llms.txt`, WebMCP, or agent interaction design. Trigger whenever code or architecture can materially affect public discoverability or machine usability, even if the user does not say SEO. Do not use for paid-search management alone.

Upwork freelance coach and orchestrator for niche discovery, positioning, and specialist routing. Use when the user asks to talk to the Upwork coach, wants to find their freelance niche, build their Upwork presence, figure out positioning, or decide which Upwork specialist to run next. Triggers: 'help me with Upwork', 'find my niche', 'what should my Upwork profile say', 'Upwork coach', 'position myself on Upwork', 'I keep losing jobs on Upwork'.

Requires python3, and AISA_API_KEY. Uses the supplied AISA_API_KEY to send requests to https://api.aisa.one. Native-first ClawHub plugin for `youtube`. Ships the packaged AIsa skill with an `openclaw.plugin.json` manifest and a Claude-compatible bundle fallback. YouTube SERP Scout for agents. Search top-ranking videos, channels, and trends for content research and competitor tracking. Use when: the user needs YouTube search, trend discovery, channel research, or SERP analysis.

Requires python3, and AISA_API_KEY. Uses the supplied AISA_API_KEY to send requests to https://api.aisa.one. Native-first ClawHub plugin for `openclaw-search`. Ships the packaged AIsa skill with an `openclaw.plugin.json` manifest and a Claude-compatible bundle fallback. Intelligent search for agents. Multi-source retrieval with confidence scoring - web, academic, and Tavily in one unified API. Use when: the user needs web search, research, source discovery, or content extraction.

Autonomous discovery of concepts, methods, and vocabulary we have NOT heard of yet — sweeps broadly, diffs against a known-concepts ledger, and reports only what is genuinely new plus whether it names a gap in this stack. Built because 'graph engineering' had to arrive by word of mouth: a fixed topic list can only refresh what you already named, so it can never surface the thing you did not know to look for. Use for periodic/scheduled discovery, 'what's new in agent engineering', 'what are we missing', 'anything we haven't heard of'. Triggers: radar, discover, what's new, unknown unknowns, 新概念, 我们没听过的, 有什么没跟上的 — NOT for researching a topic you can already name (use /research), NOT for model releases (use /model-research), NOT for internal priorities (use /next).

Use when designing, building, or debugging a Model Context Protocol (MCP) server in Node/TypeScript. Triggers: stdio JSON-RPC handshake, tool descriptions as discovery surface, lazy startup vs eager catalog loading, telemetry placement, schema design for tool inputs (zod), tool naming conventions for discoverability, error handling that does not leak stack traces, MCP client compatibility (Claude Desktop, Claude Code, Cursor), local resource fetching, secrets and env var handling, distributing as npm + claude mcp add. NOT for MCP client implementation, MCP HTTP transport (different surface), Anthropic Agent SDK building, or non-MCP plugin systems.

Run a structured discovery interview and produce a complete, developer-ready Product Requirements Document. Load when the user asks to write a PRD, create product requirements, document a feature, define user stories with acceptance criteria, or turn a rough idea into a formal product requirements document. Also triggers on "document this feature", "write requirements for", "create a one-pager", "turn this into a PRD", "I need a PRD for", or any request to produce a structured product document for stakeholder alignment or engineering handoff. Supports Full PRD, Lean PRD, and One-Pager formats. Note: for executable feature specifications (FRs, NFRs, ACs as Given/When/Then consumable by AI coding agents), route to `feature-spec` instead — PRDs frame the product, feature-specs encode the implementable contract.