Loading...
Loading...
Found 540 Skills
Produces a standardized requirements brief from any source: DevOps work items, mockups, natural language descriptions, existing reports, or documents. This is a utility skill — it gathers and structures requirements but does not build anything. If the goal is to create a report, datasource, or other artifact, use the appropriate creation skill as the entry point; it will invoke this skill when it needs requirements.
Use when modifying EXISTING Datex Studio reports on a branch. Handles label/style changes, field rearrangement, adding/removing columns, datasource modifications, and adding new data sections. Trigger for: "edit a report", "modify a report", "change the label", "add a column", "update the report on branch X", "fix the report layout". For creating NEW reports from scratch, use `report-creator`.
亚马逊ABA（品牌分析）搜索词数据的查询与分析，涵盖15个站点近3年的周维度数据。当用户提到ABA数据、亚马逊搜索词分析、关键词挖掘、搜索排名趋势、市场机会分析、季节性关键词、高点击低转化分析、蓝海词发现、竞品关键词分析、ABA data, search term report, keyword mining, search ranking trends, blue ocean keywords, click share, conversion share, seasonal keywords, market opportunity analysis, competitor keywords时触发此技能。即使用户未明确提及"ABA"，只要其需求涉及亚马逊搜索词数据和排名分析，也应触发此技能。
agent-team: Cancel a non-terminal task with a reason.
Build autonomous self-evolving AI agents with vision-grounded memory that operate computers through a perceive-reason-act cycle
Audit an AI agent skill for security risks before installing or trusting it. Runs a deterministic scanner (regex patterns, Python AST analysis, source-to-sink taint tracking, and YARA signatures) and then reasons about intent — catching prompt injection, credential exfiltration, persistence, memory poisoning, malicious code, supply-chain risks, and description-vs-behavior mismatch. Make sure to use this skill whenever the user wants to scan, audit, vet, review, or check the safety of a skill, plugin, SKILL.md, or agent tool — whether it is a local folder, a zip/.skill file, or a cloned repo — and whenever someone asks "is this skill safe to install?".
Keeps a long Claude Code task on-track — breaks out of looping/circular thinking, watches the context budget, bounds internal reasoning, and triggers a clean handoff before the window fills. Use when the model is repeating steps, re-reading the same files, second-guessing in circles, stuck or spinning, or running a long multi-step task at risk of exhausting context. Also use when the user says it is "looping", "going in circles", "stuck", "repeating itself", or asks for a handoff before running out of context.
Graph-based drug discovery toolkit. Molecular property prediction (ADMET), protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis, GNNs (GIN, GAT, SchNet), 40+ datasets, for PyTorch-based ML on molecules, proteins, and biomedical graphs.
Vercel AI Elements for workflow UI components. Use when building chat interfaces, displaying tool execution, showing reasoning/thinking, or creating job queues. Triggers on ai-elements, Queue, Confirmation, Tool, Reasoning, Shimmer, Loader, Message, Conversation, PromptInput.
Expert technical advisor with deep reasoning for architecture decisions, code analysis, and engineering guidance. Masters complex tradeoffs, system design, security architecture, performance optimization, and engineering best practices. Use when making critical architecture decisions, after implementing significant work, when debugging complex issues, encountering unfamiliar patterns, facing security/performance concerns, or evaluating multi-system tradeoffs. Provides comprehensive analysis with clear recommendations and rationale.
Gemini 3 Pro API/SDK integration for text generation, reasoning, and chat. Covers setup, authentication, thinking levels, streaming, and production deployment. Use when working with Gemini 3 Pro API, Python SDK, Node.js SDK, text generation, chat applications, or advanced reasoning tasks.
Chain multiple AI steps into one reliable pipeline. Use when your AI task is too complex for one prompt, you need to break AI logic into stages, combine classification then generation, do multi-step reasoning, build a compound AI system, orchestrate multiple models, or wire AI components together. Powered by DSPy multi-module pipelines.