数据分析、机器学习、LLM 应用、提示词设计（9428 个）
Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
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Forensic root cause analyzer for Antigravity sessions. Classifies scope deltas, rework patterns, root causes, hotspots, and auto-improves prompts/health.
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Analytics de produto — PostHog, Mixpanel, eventos, funnels, cohorts, retencao, north star metric, OKRs e dashboards de produto.
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Set up, audit, and debug analytics tracking implementation — GA4, Google Tag Manager, event taxonomy, conversion tracking, and data quality. Use when building a tracking plan from scratch, auditing existing analytics for gaps or errors, debugging missing events, or setting up GTM. Trigger keywords: GA4 setup, Google Tag Manager, GTM, event tracking, analytics implementation, conversion tracking, tracking plan, event taxonomy, custom dimensions, UTM tracking, analytics audit, missing events, tracking broken. NOT for analyzing marketing campaign data — use campaign-analytics for that. NOT for BI dashboards — use product-analytics for in-product event analysis.
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Build natural-language crypto/DeFi agents and EVM MCP plugins (Claude Code, Cursor, Codex, Gemini). Aomi turns prompts into wallet-signed txs on Ethereum, Base, Arbitrum, Optimism, Polygon, Linea — non-custodial, fork-simulated. 40+ apps: Uniswap, Aave, Lido, Morpho, GMX, Hyperliquid, Polymarket.
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Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo.
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Automate AI ML API tasks via Rube MCP (Composio). Always search tools first for current schemas.
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Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.
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AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
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Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
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This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.
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Fetch any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics. No scraping or browser required.
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Premiere Pro 動画編集ワークフローを Claude Code で自動化するスキル集。/cut は Premiere Pro XML の無音・雑音区間をジェットカット、/srt は WAV+Premiere XML から日本語テロップ用 SRT を並列Whisper→LLM意味区切り→全体アライメントSRT の3ステップ(v6)で生成、/srt-fast は改行工程まで3チャンク並列化した高速版、/telop-check は書き出し済みMP4を全編スキャンしてテロップの数字表記・誤字脱字・い抜き・ら抜き・固有名詞ミスを検出する。faster-whisper / ffmpeg / Premiere Pro を要するローカル実行型。日本語トーク動画のショート / ロング編集向け。
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WAV/動画音声を単一パスGPU転写（mlx-whisper）した後、fulltextを意味区切りでN分割しLLM改行だけを並列化してSRT字幕を高速生成する /srt の高速版。日本語トーク動画用。「/srt-fast」で実行。
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n8n workflow automation knowledge base. Provides n8n node information, node functionality details, workflow patterns, and configuration examples. Covers triggers, data transformation, data input/output, AI integration, covering 10 nodes. Keywords: n8n, workflow, automation, node, trigger, webhook, http request, database, ai agent.
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Replace this with a precise trigger description. State the Amazon PPC workflow, user intent, required data, and when Codex or Claude should use this skill.
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Act as a senior Amazon account growth operator that combines Amazon performance-drop analytics, Amazon growth-opportunity analytics, Amazon Ads data, retail readiness, BSR/rank signals, margin, inventory, and campaign/search-term evidence into a prioritized profitable growth action plan. Use when Codex needs to decide what to protect, fix, scale, reduce, pause, harvest, launch, or monitor for an Amazon brand, agency, marketplace, ASIN set, or account; when building daily, weekly, or monthly Amazon account operating plans; when reallocating ad budget; when deciding which ASINs are safe or risky to push; or when turning performance-drop and growth-opportunity findings into approval-gated actions with success/failure criteria.
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Save, load, update, delete, merge, and navigate entity objects using TMS Aurelius ORM (TObjectManager). Use when the user asks how to persist or retrieve data with Aurelius, manage object lifetime, work with associations or collections at runtime, handle transactions, use cached updates, or control concurrency. Does NOT cover query DSL or criteria/projections (those are in a separate skill). Triggers on requests like "save an entity with Aurelius", "load a customer from the database", "add items to a collection", "how do I update an object", "flush changes", "delete a record with Aurelius", "merge a transient object", "how do transactions work in Aurelius", "batch updates Aurelius".
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Discover prospect companies from a structured OpenClaw search brief, including public search and user-authorized data sources, then output a conservative candidate list ready for lead screening.
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Find the first batch of foreign-trade prospect companies from public web, LinkedIn, and user-authorized data sources such as customs exports, trade show lists, association directories, CRM/Excel files, or historical customer data, then output a conservative, structured candidate list ready for lead screening.
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Interact with Langfuse and access its documentation. Use when needing to (1) query or modify Langfuse data programmatically via the CLI — traces, prompts, datasets, scores, sessions, and any other API resource, (2) look up Langfuse documentation, concepts, integration guides, or SDK usage, or (3) understand how any Langfuse feature works. This skill covers CLI-based API access (via npx) and multiple documentation retrieval methods.
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Adds Wasp knowledge, LLM-friendly documentation fetching instructions, and best practices to your project's CLAUDE.md or AGENTS.md file
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维护每个功能一份给人看的「需求设计文档」（docs/modules 下，一个功能一份）：待拍板置顶，按需写背景、目标、流程、布局、状态、交互细节、业务规则、召回、AI 生成（prompt）、效果、成本、系统，先图后字，只写当前设计，末尾记关键改动，附出图与上线前检查。新建、更新、同步、补充这份文档都用它，别直接手改——它有固定写法和检查。用户说「出设计文档」「PRD 太长看不懂」「把设计文档同步一下」「重拍截图」「上线前看文档跟上没有」时用；用户抱怨看不到功能现在的设计、不知道最近改了什么时也用；forge 各阶段改了规则、文案、交互、布局、阈值、prompt、模型、架构后收尾也用，哪怕用户没明说。不处理写给 AI 的 PRD/DESIGN（forge-prd、forge-design）、骨架上定布局（forge-design-lab）、改 prompt 本身（prompt-craft）、进度（forge-status）。
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上线前 PR 审查。分析当前分支与基础分支的 diff，检查 SQL 安全、竞态条件、 LLM 信任边界、枚举完整性等测试捕获不到的结构性问题。发现问题直接修复。 触发方式：用户说"review"、"审查代码"、"上线前检查"、"forge-review"，或 forge-dev --full 调度。 与内置 code-review/verify 的分工：forge 工作流内的上线前结构性审查用本 skill；用户点名 /code-review 或 /verify 时用对应内置 skill。
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