Data & AI skills

Use when implementing or debugging ANY network request, API call, or data fetching. Covers fetch API, React Query, SWR, error handling, caching, offline support, and Expo Router data loaders (`useLoaderData`).

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Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.

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Build a throwaway prototype to flesh out a design before committing to it. Routes between two branches — a runnable terminal app for state/business-logic questions, or several radically different UI variations toggleable from one route. Use when the user wants to prototype, sanity-check a data model or state machine, mock up a UI, explore design options, or says "prototype this", "let me play with it", "try a few designs".

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Generate Mermaid diagrams from code, architecture, or concepts. Use when you need to visualize code flow, system architecture, data relationships, state machines, sequences, class structures, or any other diagrammable concept.

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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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Integrate Expo and React Native into an existing native iOS or Android app. Use when the user mentions brownfield, embedding React Native in a native app, AAR/XCFramework, or adding Expo to an existing Kotlin/Swift project. Covers both the isolated approach and the integrated approach.

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Deterministic external-evidence web validation workflow for Sanguo ETL/RAG. USE FOR: 外部證據網站檢查、來源健康度、allowlist 驗證、來源 seed 覆蓋、3kweb-check、external evidence source health、fetch/parse/hash first、LLM reviewer second。

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Strict routing mode for prompts prefixed with `(best)`. USE FOR: any turn where the user explicitly wants the safest, smallest-context, highest-discipline execution path. This skill must route into context-budget-guard first, then continue into the task-specific workflow.

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Sanguo RAG unresolved mention resolution loop. Use for: 三國 RAG, unresolved mentions, alias review, observed mentions, MCQ triage, manual roster seeds, false-positive exclusion, running run_resolution_loop.py, applying unresolved-triage answers, delegating 三國名詞查證 to a web-capable agent, or Pilot ETL chunk experiments with LangChain text splitters / LangGraph / LangFlow around 張飛長坂橋等小樣本流程。

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武將資料管線 SKILL — 整合「公開資料爬取 → 欄位映射 → 合併去重 → 自動分類 → 品質驗證 → 匯出入庫」的端到端流程。USE FOR: 批次新增武將、從光榮三國志 Wiki 匯入參考數值、建立 master 資料集。DO NOT USE FOR: 故事文案生成（用 general-story-writer）、細部數值微調（用 general-balance-tuner）。

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Idiomatic Go concurrency - sync primitives, channel semantics, the select statement, and the standard channel patterns (cancellation/done-channel, fan-out/fan-in, pipeline, or-done, context, worker-pool). Use when writing or reviewing Go that spawns goroutines, shares state, or coordinates over channels, even if the user never says "concurrency" - e.g. data races, deadlocks, goroutine leaks, sync/context/select usage, or "make this parallel". Distilled from luk4z7/go-concurrency-guide.

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Generate 5–10 business idea candidates from a blank page or a founder's domain context — using pain mining, jobs-to-be-done, trend × capability mapping, constraint relaxation, adjacency search, and founder-market-fit prompts. Each candidate is a structured idea card (segment, JTBD, current alternative, why-now, distribution wedge, monetisation, "feels like"). Load when the user asks to generate business ideas, brainstorm startup ideas, find ideas to work on, says "what business should I start", "give me startup ideas", "I don't know what to build", "ideate ventures", "blank-page idea generation", "find me a startup idea", "explore business opportunities". Sub-skill of `venture-exploration`. Hard-bans "Uber for X" / "AI for X" with no specific JTBD, "everyone" segments, and idea cards missing any of the 7 required fields. Does NOT design or evaluate ideas generated — for that use `idea-evaluation`.

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Create focused role prompts for agents in multi-agent topologies. Load when agent-builder needs role prompts for agents, or when a user asks to "create an agent prompt", "write a role prompt", "define agent identity", "write an agent role", "prompt for this agent", "write instructions for this agent", "agent persona". Scope: agent role prompts only (v1). System prompts, task prompts, and skill invocation prompts are future TODOs.

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Score LLM and agent outputs using LLM-as-judge techniques — direct scoring against rubrics or pairwise comparison between two outputs. Includes built-in bias mitigation for position bias, length bias, and self-enhancement bias. Load when the user asks to score an output, judge a response, evaluate against a rubric, compare two outputs, do direct scoring, run pairwise comparison, or says "rate this", "which response is better", "score this against the rubric", "judge this output", "LLM as judge this". Sub-skill of eval-output orchestrator.

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Decompose an unknown quantity into 3-5 estimable factors and produce a defensible order-of-magnitude answer without needing precise data. Load when the user needs to size something without data — market size, resource requirements, effort estimates, user numbers, costs — or when a decision is blocked by "we don't know the numbers". Also triggers on "ballpark this", "rough estimate", "how big is this market", "how long would this take", "how many users", or when deep-thinking diagnoses a sizing/estimation frame. The goal is not precision — it is a defensible answer that enables a decision to be made. Based on Enrico Fermi's estimation method.

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Orchestrator for the eval-output skill suite — evaluate LLM and agent outputs for quality, accuracy, helpfulness, and safety using structured rubrics and LLM-as-judge techniques. Load when the user says "evaluate this output", "score this response", "run an eval", "LLM as judge", "evaluate agent output", "how good is this response", "rate this answer", "eval this", or provides an LLM output that should be assessed for quality. Single entry point for all output evaluation workflows.

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Build the smallest, highest-signal context package for an AI coding task — goal, constraints, repo facts, boundaries, and a verification plan. Load when prompts are underspecified, the agent is missing key files or decisions, the user says "use the right context", "here's the repo", or when work is drifting due to missing constraints. Also triggers on "context engineering", "gather context", "what do you need from me", "before you start". Not for cross-session continuity (use memory-startup/memory-recall).

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Required reference for Prisma ORM 7 SQL driver adapter work. Use when implementing or modifying adapters, adding database drivers, or touching SqlDriverAdapter, Transaction, savepoint, result mapping, or DriverAdapterError behavior. Covers current transaction lifecycle, optional savepoint hooks, original database-error preservation, and verification.

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Implement several variations of a design directly inside the running app, on the real page/route, with a live switcher to toggle between them in the browser, then recommend one and fold the winner into the real code. Variants are real implementations judged against real data, layout, and density (not an isolated mockup). Use this skill whenever the user wants to try design options live in the app, prototype changes directly on a real page, add a variant switcher, compare layouts in-context, asks to "build a few versions of this page", "try variants on the real screen", "switch between designs in the app", "implement this with a toggle", or says "real-prototype this". For a safe standalone .html copy that never touches the app, use the prototype skill instead.

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Automate College Football Data tasks via Rube MCP (Composio). Always search tools first for current schemas.

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Automate Asin Data API tasks via Rube MCP (Composio). Always search tools first for current schemas.

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Automate AI ML API tasks via Rube MCP (Composio). Always search tools first for current schemas.

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Use this skill when the user uploads Excel (.xlsx/.xls) or CSV files and wants to perform data analysis, generate statistics, create summaries, pivot tables, SQL queries, or any form of structured data exploration. Supports multi-sheet Excel workbooks, aggregation, filtering, joins, and exporting results to CSV/JSON/Markdown.

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Help address review/issue comments on the open GitHub PR for the current branch using gh CLI; verify gh auth first and prompt the user to authenticate if not logged in.

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