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Found 11,817 Skills
Visual UI annotation tool for AI agents. Drop the React toolbar into any app — humans click elements and leave feedback, agents receive structured CSS selectors, bounding boxes, and React component trees to find exact code. Supports MCP watch-loop, platform-specific hooks (Claude Code / Codex / Gemini CLI / OpenCode), webhook delivery, and autonomous self-driving critique with agent-browser.
A-share multi-agent AI investment research and analysis tool - 15 AI analysts collaborate to complete technical analysis, fundamental analysis, market sentiment judgment, capital flow tracking (northbound capital/main capital), macroeconomic analysis and game theory deduction, and output structured trading suggestions and risk assessment. Supports Shanghai and Shenzhen A-share stock codes and Chinese names. Multi-agent AI stock analysis for China A-shares. 15 specialized analysts collaborate across technical analysis, fundamental analysis, sentiment analysis, smart money flow tracking, macro economics, and game theory to deliver structured buy/sell/hold recommendations with risk assessment.
Practical AI agent workflows and productivity techniques. Provides optimized patterns for daily development tasks such as commands, shortcuts, Git integration, MCP usage, and session management.
Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports.
Orchestrates end-to-end software development using the addyosmani/agent-skills framework. Guides the user through define → plan → build → verify → review → ship phases, spawns subagents for each step, tracks state persistently, and never loses focus on workflow completion. Use when the user says "let's build X", "help me implement X", "walk me through X", or wants structured multi-phase dev guidance. Also triggers when a task is clearly non-trivial and would benefit from phased execution.
Index skill for the blockint-skills bundle—includes a “choosing a skill” routing map and routes to focused skills on blockchain intelligence fundamentals, address clustering, analytics, tokenomics, investigation ethics, Phalcon Compliance documentation pointer, Chainalysis public Sanctions API/oracle router, FATF official AML/CFT glossary, Arkham Intel research article on leading crypto analysis tools for traders, Christoph Michel cmichel.io guide on becoming an EVM smart contract auditor, risk exposure, behavioral risk, address and transaction screening workflow concepts, Range AI investigation playbook (MCP), crypto market mechanics, OSINT (Bellingcat toolkit), Solana external stacks (Helius, Range MCP, Tavily, PayAI, React Flow, Solana Policy Institute), DeFi/MEV/rug skills, privileged-access mitigation lessons (Chainalysis Drift case study), coral-xyz sealevel-attacks Solana security examples, Neodyme Solana Security Workshop (workshop.neodyme.io), Osec (osec.io) Solana auditor introduction blog post, canonical X post citation for @armaniferrante status 1411589629384355840, BlockchainSpider open-source data collection, MoTS (Know Your Transactions / transaction semantics research repo), Impersonator dApp devtools (EVM + Solana read-only address presentation), Katana web crawling, lcamtuf American Fuzzy Lop (AFL) classic documentation (lcamtuf.coredump.cx/afl), and the official Agent Skills open-format specification (agentskills/agentskills, agentskills.io/llms.txt doc index). Use when the task spans multiple topics or the user needs help picking which named skill to load.
Build applications where agents are first-class citizens. Use this skill when designing autonomous agents, creating MCP tools, implementing self-modifying systems, or building apps where features are outcomes achieved by agents operating in a loop.
Comprehensive guide to why and how AI agents should use email. Use when evaluating whether an agent needs email, comparing email infrastructure options (AgentMail vs Gmail API vs Resend vs SendGrid vs SES), understanding security risks like prompt injection via email and OAuth credential exposure, or exploring common agent email use cases such as customer support agents, sales outreach, verification flows, and browser automation.
Run comprehensive agent-native architecture review with scored principles
Workbench agent panel system — ef-edit CustomEvent pipeline, registry roll-up, selector grouping, and element property schema. Use when adding new GUI edit capture points, expanding the inspector schema, or continuing development of the EFAgentPanel feature.
Authoritative reference for the neo4j-agent-memory Python package — a graph-native memory system for AI agents built on Neo4j — and for the hosted service (NAMS) at memory.neo4jlabs.com. Use this skill whenever the user mentions neo4j-agent-memory, agent memory with Neo4j, context graphs, the POLE+O model, MemoryClient/MemorySettings, the memory MCP server, or any of the framework integrations (LangChain, PydanticAI, CrewAI, AWS Strands, Google ADK, Microsoft Agent Framework, OpenAI Agents, LlamaIndex). Also use when the user mentions the hosted service at memory.neo4jlabs.com, NAMS, the Neo4j Agent Memory Service, the `nams_` API key prefix, or the hosted MCP endpoint. Also use when writing documentation, blog posts, tutorials, PRDs, or code samples for the project, when comparing agent memory approaches, or when positioning graph-native memory against vector-only approaches — even if the user doesn't explicitly name the package.
Inter-agent communication protocol for C-suite agent teams. Defines invocation syntax, loop prevention, isolation rules, and response formats. Use when C-suite agents need to query each other, coordinate cross-functional analysis, or run board meetings with multiple agent roles.