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Firmware development for the Raspberry Pi Pico (original, RP2040 — dual Cortex-M0+ @ 133 MHz, 264 KB SRAM, 2 MB QSPI flash) — its 26 exposed GPIO, 4-channel 12-bit ADC, 16-channel PWM, 2× UART/I2C/SPI, PIO state machines, USB 1.1 CDC, VSYS/VBUS/SMPS power wiring, and the PlatformIO + arduino-pico (earlephilhower) setup around them. Use when working on this board, any Pico H or Pico clone, or a bare RP2040: project setup, platformio.ini, the earlephilhower-vs-Mbed core choice, pin mapping, USB CDC serial, analogRead/analogWrite behaviour, Wire/SPI defaults, battery power, EEPROM emulation, BOOTSEL/UF2 flashing, or debugging why something on the board does not work.

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Understand what SKMTC is, how its engine works, and the architectural invariants — for agents building or extending infrastructure *around* SKMTC rather than authoring generators or running the CLI. Covers the three-phase pipeline, the host/Worker boundary, cross-generator coordination, the manifest, the attribution / gen-maps (provenance) subsystem, the package graph, the dependency substrate, and the design decisions that make SKMTC behave unlike a typical codegen tool. Use this skill when the user asks "what is SKMTC", "how does the SKMTC engine work", "explain the SKMTC architecture", or is building platform infrastructure around SKMTC — a hosted generate API, a schema or generator registry, tracing or provenance tooling, a web app or SaaS that wraps the engine, or platform-level CI integration. This skill is the system mental model. It does NOT cover authoring generators (→ skmtc-generator), running CLI commands (→ skmtc-cli), or diagnosing broken runs (→ skmtc-debug).

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Write, restructure, or review documentation — tutorials, how-to guides, reference pages, concept/explanation docs, API references, READMEs, changelogs, release notes, and troubleshooting guides. Distills documentation craft from Mintlify's guides (compiled from technical writers at Stripe, GitHub, Amplitude, and Anaconda), the Diátaxis framework (including the compass and per-type voice), the Google and Microsoft style guides, Every Page Is Page One, Write the Docs, and Docs for Developers: audience analysis, content-type selection, style and word-level rules, procedure writing, structure for humans and AI agents, code-example standards, page templates (templates.md), mechanical enforcement and llms.txt (mechanics.md), maintenance, and success metrics. Use this skill when the user asks to "write docs", "document this feature", "improve this page", "review these docs", "write a tutorial / how-to / reference page", "structure the docs", "write API documentation", "write a README / changelog", or when authoring any file under a `docs/` tree. For SKMTC docs specifically, this skill governs the *craft* (what makes the page good); the content split between skills, `llms.md`, and the docs tree is governed by `docs/skills/README.md`. Distinct from `skmtc-retro` (captures observations about work) and the `skmtc-*` operational skills (guide doing the work) — this skill guides writing *about* the work for readers.

日本語の概要は準備中です。原文の説明を表示しています。

Customize how a built-in unikit skill behaves in this specific project by writing workflow overrides into its skill-context file. These are per-skill workflow rules (e.g. "/unikit-review should also flag TODO comments", "/unikit-implement should run tests after each task"), not coding conventions. Use when the user wants to change, customize, or override how a particular unikit skill works in this project — "customize the review skill", "make /unikit-fix always add logging here", "the implement skill should do X differently", "add a skill rule for /unikit-commit". Use "validate" to find stale overrides against updated base skills. For a general coding convention (not tied to one skill) use /unikit-rules; to derive rules from past mistakes use /unikit-evolve.

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Enter explore mode for technical work — a thinking partner for {{engine_name}} code, architecture, and implementation decisions before you write any code. Use it to investigate a technical solution, design the architecture of a feature, choose between frameworks or libraries, compare implementation approaches, analyze how existing code or a system works, or deeply root-cause a complex bug without fixing it yet. The ultra mode adds adaptive artifacts (a C4 view, ADRs, a dependency graph) to the research folder — run it on "ultra research", "ultra explore" or "ultraresearch"; otherwise run the default. Trigger on "let's explore this technical solution", "how should we architect this feature", "which framework should we use", "run an ultra research on the save system", "investigate this error deeply". Research and analysis only — it never writes code. This is the CODE / engineering explorer — for GAME-DESIGN, GDD, mechanics, or balance research (no code) use /unikit-gd-explore.

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Research and ideation partner for GAME DESIGN — think through design ideas before writing them into the GDD; produces a brief, never edits the GDD itself. Use to assess a genre or market for viability, dissect a reference game (mechanics → dynamics → aesthetics), explore how to improve or extend an existing system, flow, or content type, research new mechanics the design lacks, investigate a part of the GDD in code, or develop the research bucket of a /unikit-gd-review report into decided edits. Trigger on "explore how to improve the flow", "ways to improve the combat system", "is this genre saturated", "break down the combat of Hades", "work out the review's open questions". GAME-DESIGN research only — for CODE or technical research use /unikit-explore; to reconstruct a design from code use /unikit-gd-recon; to write a change into the GDD use /unikit-gd-system or /unikit-gd-spec; to apply edits you know use /unikit-gd-apply; to invent a new game use /unikit-gd-brainstorm.

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Mellanox SX6036 InfiniBand スイッチのシリアルコンソール操作。ステータス確認、show コマンド、IB 設定、FW 管理を行う。

Build an animation from scratch, making the decisions in the order that determines whether it feels right — should it animate at all, what purpose, which tool, which properties, which curve and duration, how it interrupts, how it exits. Writes the implementation. Use when asked to animate something, add motion, make a component feel alive, or build a transition. For critiquing existing motion use review-animations; for auditing a whole codebase use improve-animations.

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When the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' 'zero-click search,' 'how do I show up in AI answers,' 'LLM mentions,' 'optimize for Claude/Gemini,' 'llms.txt,' 'OKF,' 'Open Knowledge Format,' 'knowledge bundle,' or 'agent-readable site.' Use this whenever someone wants their content to be cited or surfaced by AI assistants and AI search engines. For traditional technical and on-page SEO audits, see seo-audit. For structured data implementation, see schema.

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Create or refresh AEO-optimized content that gets cited by AI assistants (Gemini, ChatGPT, Perplexity) using only free tools. Two modes: CREATE new content targeting a specific prompt, or REFRESH existing content to improve AI citation-worthiness. Researches what AI models currently cite, builds a competitive brief, and produces citation-worthy content. Use when a user wants to: write content optimized for AI citations, create articles that show up in AI answers, refresh/update existing content for better AI visibility, build authority content for answer engines, or produce AEO content without paid tools. No API keys required — uses web_fetch, web_search (free tier), and LLM reasoning only. Pairs with aeo-prompt-research-free (which identifies WHAT to write about; this skill handles HOW to write or refresh it).

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Use when building a frontend and the user wants a distinctive / animated / "wow" UI element in React + Tailwind - e.g. a laptop-opening-on-scroll, 3D tilt card, spotlight hero, animated beams/background, marquee, bento grid, interactive globe, meteor/particle background, shimmer button, or scroll/reveal motion. Fetches the showpiece component LIVE at build time from code libraries (Aceternity, Magic UI, Cult UI, ReactBits, 21st.dev) via their shadcn registry command (or WebFetch fallback), then adapts it to brand tokens. Also covers plain components (button, form, modal, table, etc.) as a fallback. Triggers on "make it pop", "something flashy", "animated [X]", "like [cool site]", "build a [showpiece] section". NOT for non-React stacks. Inspiration galleries (Dribbble/godly) are visual reference only - never code.

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Sparkle Design の UI コンポーネントを shadcn registry 経由でプロジェクトに追加するスキル。 パッケージマネージャの自動検出、設定バリデーション、コンポーネントインストール、 Storybook 統合、CSS セットアップ、トラブルシューティングをカバーする。 「Sparkle コンポーネントを追加」「sparkle-design のコンポーネントをインストール」 「@sparkle-design のセットアップ」「コンポーネントをどう追加する」 「registry の設定」「components.json の設定」への言及で発動する。 English: "add a sparkle button", "install sparkle-design card", "add @sparkle-design/input", "set up components from sparkle registry", "how do I install components", "registry setup"

SOLID design review rules (SRP, OCP, LSP, ISP, DIP) for code reviews: what counts as a real design finding, what is only taste, how to cite it and how severe it is. Use when reviewing a diff, a pull request or recent changes for design problems, when asked for a "SOLID review", "design review", "is this class doing too much", or when a review checklist asks for SOLID. Pair it with the dry skill for duplication and with owasp-asvs for security.

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How to fetch the latest github/gh-aw release binary — either as the gh CLI extension (gh aw ...) or as a raw platform binary downloaded from GitHub Releases — including how to resolve the latest tag, pick the right asset, and verify its checksum.

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案件预算和持续的 WIP/偏差监控。在案件设立时构建分阶段费用估算，按法域或工作流运行自下而上预算，计算应急准备金，并构建 AFA 安排（固定费用、上限费用、分阶段固定费用）。持续监控：对照预算的 WIP 跟踪、比例性评估（支出与进度）、带根本原因分析的偏差评述、完成预测、变现率监控、核销分析。触发词：'build a budget'、'fee estimate'、'what will this cost'、'WIP review'、'budget vs actual'、'how are we tracking against budget'、'we're over budget'、'realisation is poor'、'what's our ETC'、'budget for the German workstream'、'model the financial impact of this scope change'、'draft a fee adjustment'、'write-off analysis'、'how much contingency'、'AFA structure'、'fixed fee estimate'、'budget update'、'forecast to complete'。

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Naturalistic decision-making research showing how experts make decisions through recognition-primed processes

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Designs human-in-the-loop review points for DAG workflows. Determines what to present to the human, how to collect feedback, and how to route approve/reject/modify decisions back into the DAG. Use when adding approval gates, designing review UX, or handling human feedback in agent workflows. Activate on "human review", "approval gate", "human-in-the-loop", "human gate", "approval workflow", "user review step". NOT for executing human gates at runtime (use dag-runtime with Temporal signals), general UX design, or chatbot conversation design.

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Decides HOW to mutate a DAG when a node fails, quality is below threshold, or new information changes the plan. Selects from mutation strategies (add node, replace agent, fork paths, loop back, downgrade model) based on failure type, cost budget, and execution history. Activate on "DAG failed how to fix", "mutation strategy", "replan on failure", "adaptive DAG", "recovery strategy", "what to do when node fails". NOT for detecting failures (use dag-quality), executing mutations (use dag-planner), or runtime execution (use dag-runtime).

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How to design contextual inputs for an always-on AI agent with episodic memory. Covers what data to feed the agent, how to structure observations and triggers, ambient context capture (screen, audio, calendar), context window budgeting, and retrieval strategies that keep the agent grounded in what's actually happening. Activate on: "what should the agent observe", "context inputs for agent", "ambient context capture", "agent triggers", "agent input design", "screenpipe integration", "context window budget", "what data to feed my agent", "/always-on-agent-inputs". NOT for: memory architecture and storage (use always-on-agent-architecture), application ideas (use always-on-agent-applications), safety concerns (use always-on-agent-safety).

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Use when copy-catting, replicating, or adapting features from external systems, repos, apps, or ideas into the current project. Triggers on 'copy this from', 'replicate', 'clone this feature', 'how does X do Y', 'chom', 'like how X does it', 'port from', 'build like', '1:1 copy'. NOT for packing the current project (see mk:pack); NOT for one-shot URL-to-markdown fetches (see mk:web-to-markdown).

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Use when taking a repo open source, or hardening one that already is. Run /oss-launch and it scans the repo (stack, existing files, git remote, secrets), asks only what it cannot infer, then generates a tailored OSS file collection (README, LICENSE, CONTRIBUTING, CODE_OF_CONDUCT, SECURITY, CHANGELOG, .gitignore, .github/ issue + PR templates, CI/CD). Also covers launch (GitHub metadata, badges, Show HN, Reddit, YouTube), release cadence (SemVer + CHANGELOG), and demo-GIF generation. Triggers on "open source this", "OSS checklist", "prep for launch", "make this repo public", "add a license / contributing / security policy", "Show HN", "release workflow", "star growth", "get contributors".

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Find UX pains and opportunities fast through competitor scans, pattern research, and web search. Surfaces how others solve the same flow, common failure modes, and conventions users already expect. Use when the user says "research this UX", "how do others do X", "competitor scan", "find UX patterns", "what's the convention for Y", or before ideating a flow. Part of the Product Design set. Uses web search/fetch tools.

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Build Sankey, alluvial, river, and CONSORT-style flow diagrams to visualize cohort transitions, cell-state changes, or pipeline filtering using ggalluvial, networkD3, plotly, and consort. Use when showing how entities move between categories across timepoints (cell states, drug response classes, patient flow through a trial) or filtering pipelines (variants filtered through QC stages).

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Work a primary-prevention patient through the 2026 ACC/AHA multisociety dyslipidaemia guideline CPR framework — Calculate (10-year and 30-year PREVENT-ASCVD), Classify (low / borderline / intermediate / high), Personalize (demographic, clinical, biomarker, and reproductive risk enhancers), and Reclassify (coronary artery calcium scoring where appropriate). Use when a clinician asks how to assess ASCVD risk for primary prevention, who needs a statin, borderline risk patient what to do, intermediate risk should I start a statin, risk enhancers to look for, premature family history of ASCVD how does it change risk, pregnancy-related risk factors, PCOS and cardiovascular risk. Grounded in the 2026 ACC/AHA multisociety dyslipidaemia guideline.

日本語の概要は準備中です。原文の説明を表示しています。