Conducts structured business analyses: problem and stakeholder analysis, as-is/to-be gap analysis, user personas, scope definition. Creates BA documents as the foundation for requirements engineering. Uses innovation phases EXPLORATION, IDEATION, and VALIDATION. Use this skill when the user mentions "Business Analysis", "BA", "Stakeholder Analysis", "Problem Analysis", "As-Is Analysis", "Gap Analysis", "User Personas", "Define Scope", "Analyze Project", "Explore", "How might we", "Value Proposition", "Idea Potential", "Innovation", or similar. Also when the user wants to start a new project and does not yet have a clear requirement. This skill helps understand the problem before discussing solutions.

当用户明确要求“推荐投稿期刊”“帮我的论文选 SCI 杂志”“这篇 manuscript 适合投哪些 journal”“期刊匹配/选刊/投稿建议”时必须使用。适用于用户提供全文、摘要、Markdown、LaTeX、PDF、Word 或混合材料的场景；本 skill 会基于 manuscript 与用户偏好，先用内置 `2023IF.xlsx` 做最小硬过滤生成候选池，再由宿主模型自主规划 Set1/Set2/Set3，并联网核验 scope / 质量 / 近 3 个月 PubMed 论文，最后输出 1 份按推荐度排序的 Markdown 选刊报告。⚠️ 不适用：用户只是想润色论文、只想翻译摘要、或只问某个单一期刊的官网信息而不需要系统选刊。

数据分析员工（Data Analyst Agent）的唯一总入口：凡与数据资产、取数、指标、表/视图、 治理职责、知识网络、统计或分析相关的问题，必须先经本 skill 做编排与路由，再进入找表或问数等子流程。 负责 kn 分域、上下文注入（accountId / date）、多候选 KN 时的 LLM 决策、 问数分支的 SQL 生成；与 smart-search-tables / smart-ask-data / ontology-core 的交接。 当用户提出任何数据类自然语言任务、或需在多条业务 KN 间切换时使用； 所有 ontology CLI 执行均委托 ontology-core 完成，本 skill 不直接执行 CLI。

Summarizes the content of a GitHub issue, pull request (PR), or notification, providing a concise overview of the main points and key details. ALWAYS use the skill when asked to summarize an issue, PR, or notification.

Routes the weakest VCN samples (output of `tao-analyze-gaps-visual-changenet`) into per-augmentation-module subsets based on each module's label eligibility. Use when the user asks to "route VCN gap samples", "split AOI gaps for k-NN mining and AnomalyGen", or prepare the immediate next step after DEFT gap analysis in a VCN AOI SDA iteration.

Run the root-level workflow runtime policy or rule-based rollouts and verify simulator success. Use for evaluation, checkpoints, or local controllers; do not use for replay or dataset annotation.

Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation verification workflows.

Optimizes transformer attention with Flash Attention for 2-4x speedup and 10-20x memory reduction. Use when training/running transformers with long sequences (>512 tokens), encountering GPU memory issues with attention, or need faster inference. Supports PyTorch native SDPA, flash-attn library, H100 FP8, and sliding window attention.

Finalize bookkeeping for a story already completed by QA. Never changes lifecycle status. Use when: close story, *close-story, story Done, /close-story.

Conversational agent that guides developers through creating complete Azure PowerShell cmdlet design specifications. Walks through an interactive interview covering service release details, contacts, scenarios, cmdlet syntax, parameter sets, piping, test cases, and spec links. Validates against PowerShell design guidelines, pre-populates fields from TypeSpec, and files the design as a GitHub Issue in Azure/azure-powershell-cmdlet-review-pr. USE FOR: 'create PowerShell design', 'PS design review', 'cmdlet design', 'PowerShell cmdlet review', 'submit PS design'. DO NOT USE FOR: SDK generation, TypeSpec authoring, releasing packages.

Run Karpathy-style autoresearch optimization on any content. Generates 50+ variants, scores with a 5-expert simulated panel, evolves winners through multiple rounds, outputs optimized version + full experiment log. Use when optimizing landing pages, email sequences, ad copy, headlines, form pages, CTA text, or any conversion-focused content. Triggers on "optimize this page", "run autoresearch", "score these variants", "A/B test this copy".

Cloud file hosting, static site deployment, and cloud compilation using x hub. Use when the agent needs to share files/images, deploy a static website, or perform cloud compilation.

MCP Apps integration for json-render. Use when building MCP servers that render interactive UIs in Claude, ChatGPT, Cursor, or VS Code, or when integrating json-render with the Model Context Protocol.

Clear CCB managed agent conversation context with `ccb clear`. Use when the user writes `$ccb-clear`, `$ccb_clear`, or asks to clear/reset one or more CCB agent contexts without restarting or deleting project state.

Server-side analytics tracking with pluggable providers. Use when adding analytics events, registering custom tracking providers, or configuring built-in providers (PostHog, Mixpanel, Amplitude, Webhook).

Keep apps and templates loading fast. Read when adding a data model, a list/read action, a page or sidebar that loads data, or when something loads slowly, or when adding a dependency to the deployed server bundle. Covers column projection, indexing hot-path queries, avoiding N+1 and round-trip waterfalls, cheap polling, not recomputing on every read, and cold-start artifact size.

Cross-platform pattern for handling messaging integration webhooks (Slack, Telegram, WhatsApp, email, etc.) on serverless hosts. Use when adding a new integration adapter, debugging dropped messages, or wiring long-running agent work into a webhook handler.

Decompose large tasks across parallel agents — use for migrations, multi-file refactors, or batch work

Core principles and workflow router for changes to the Agents Window under src/vs/sessions.

Vercel Workflow DevKit (WDK) expert guidance. Use when building durable workflows, long-running tasks, API routes or agents that need pause/resume, retries, step-based execution, or crash-safe orchestration with Vercel Workflow.