Loading...
Loading...
Found 596 Skills
Process this skill enables AI assistant to forecast future values based on historical time series data. it analyzes time-dependent data to identify trends, seasonality, and other patterns. use this skill when the user asks to predict future values of a time ser... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
Summarize Codex token usage from local Codex Desktop or CLI session JSONL logs. Use when the user asks to count, audit, total, compare, or report Codex/OpenAI token usage for a period such as today, this week, last month, a calendar month, a rolling 30-day window, peak week, peak day, input/output/cached/reasoning breakdown, or net token usage.
Analyzes DevOps Center test failures and Code Analyzer violations in plain language — failure category, offending file/class/method/line, rule violated, fix direction, and prioritized improvement suggestions (test-code vs production-code) — then optionally creates a tracked fix WorkItem on explicit request. Analysis is pure reasoning; work-item creation is a confirmation-gated write. Use this skill to explain failures or improvement suggestions, translate Code Analyzer violations, or track a fix as a work item. TRIGGER when: a run failed and the user wants root cause; a quality gate failure needs explaining; violations need translating; the user shares a failure payload and asks how to address it; wants to strengthen tests; or wants to create a fix work item, log a remediation, or assign a failure. DO NOT TRIGGER when: the user wants fix code written (use platform-apex-generate) or new test classes authored (use platform-apex-test-generate).
Knowledge base for designing, reviewing, and linting agentic AI infrastructure. Use when: (1) designing a new agentic system and need to choose patterns, (2) reviewing an existing agentic architecture ADR or design doc for gaps/risks, (3) applying the lint script to an ADR markdown file to get structured findings, (4) looking up a specific agentic pattern (prompt chaining, routing, parallelization, reflection, tool use, planning, multi-agent collaboration, memory management, learning/adaptation, MCP, goal setting, exception handling, HITL, RAG, A2A, resource optimization, reasoning techniques, guardrails, evaluation, prioritization, exploration/discovery). All rules and guidance are grounded in the PDF "Agentic Design Patterns" (482 pages).
Lead qualification engine with conversational intake. Asks structured questions to understand your qualification criteria, generates a reusable qualification prompt, then batch-enriches leads via Apify LinkedIn scraping and scores them with parallel processing. Outputs qualified/disqualified verdicts with confidence scores and reasoning to Google Sheets (via Rube) or CSV. Supports calibration mode for prompt refinement.
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
Complex research requiring deeper analysis, multi-step reasoning, and sophisticated source evaluation for technical, academic, or specialized domain queries needing expert-level analysis, high-stakes decisions, or multi-layered problem solving.
Grill the user relentlessly about a plan, decision, or idea via structured multiple-choice questions, recording decisions and rejection reasons as a side effect of choosing. Use when the user wants to stress-test their thinking, or uses any 'grill' trigger phrases.
Implement GraphRAG patterns combining knowledge graphs with retrieval for complex reasoning. Use this skill when building RAG over interconnected data or needing relationship-aware retrieval. Activate when: GraphRAG, knowledge graph, graph retrieval, entity relationships, Neo4j RAG, graph database, connected data.
Advanced context engineering techniques for AI agents. Token-efficient plugins improving output quality through structured reasoning, reflection loops, and multi-agent patterns.
High-dividend stock screen via Longbridge — analyse high-dividend-yield strategies for A-shares / HK / US, filter for sustainable payout (reasonable payout ratio, free-cash-flow coverage), stable dividend history, and evaluate long-term total return potential. Triggers: "高分红", "股息率", "红利股", "高股息", "分红稳定", "现金分红", "股息策略", "红利策略", "高分紅", "股息率", "紅利股", "高股息", "分紅穩定", "現金分紅", "high dividend", "dividend yield", "dividend stock", "income stock", "dividend strategy", "payout ratio", "free cash flow coverage", "dividend growth", "dividend stability".
基于Sorftime数据的亚马逊多维度产品搜索与筛选，涵盖14个站点，支持历史月份快照回看。当用户提到Sorftime产品搜索、亚马逊产品筛选、竞品调研、类目分析、品牌热销、卖家分析、季节性产品、历史快照回看、产品搜索、月销量月销额、ABA关键词找产品、价格范围筛选、新品发现、多条件组合筛选、product search, competitor research, category analysis, brand bestsellers, seller analysis, seasonal products, historical snapshot时触发此技能。即使用户未明确提及"Sorftime"，只要其需求涉及亚马逊产品搜索、筛选、对比或类目/品牌/卖家维度的产品探索，也应触发此技能。