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Ideate a brand-new game concept with the user, from a blank page or a one-line hint to a finished CONCEPT card. Use it whenever the user wants to come up with a new game — whether the project has no design document yet, or already has one and they're exploring a fresh idea. Through a structured divergence/convergence dialogue it explores ideas, frames the hook, generates and scores concepts (market validation delegated to /unikit-gd-explore), and settles pillars, loops, motivation, and a pre-mortem. Trigger whenever the user wants to make or invent a game, doesn't know where to start, or gives a genre/theme hint, e.g. "let's come up with a game", "I don't know what game to make", "I want to make a roguelike", "a game about zombies", "a farming game", "help me come up with a concept". For researching or dissecting existing games use /unikit-gd-explore; to write an already-chosen concept as the master GDD use /unikit-gd-spec.

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Mechanical consistency check for game design — answers "is the design consistent with itself?" — plus changed-scope impact analysis across all three GDD axes: systems, flows, and content types. Offline and deterministic: greps the facts registry against the documents (numbers, terms, IDs, duplicate & dangling references, roster↔disk, map freshness, Depends 3-way, status/version, AC presence, placeholder leaks, flow & content schema), and from a git diff computes which dependent systems, flows, and content a change affects. Scope is inferred: a named document checks that document; an unverified diff triggers a changed-scope pass; otherwise everything. Use when the user wants a consistency or impact check, e.g. "verify the design", "what did this change affect", "find broken references in the design". Conflicts triage into four tracks; the entailed fixes hand off to /unikit-gd-apply in one pass. This is the mechanical pass — for a subjective "is this design good" quality critique use /unikit-gd-review.

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。

Navigator and help guide for the UniKit framework — use when the user is unsure what to do next, where to start, or which unikit skill fits their situation. It diagnoses intent and points to the right skill or pipeline: starting a project, writing code, testing, planning, the spec-driven code workflow, authoring or detailing game design, finding a new mechanic, or deciding what game to build. Trigger on questions like "what do I do next", "where do I start", "how do I start coding", "how do I test my code", "how do I create the game design", "how do I detail a system", "how do I find a new mechanic", "which unikit skill should I use", "how does the code pipeline relate to game design", "I'm lost in unikit", "help me with unikit". Read-only — it explains and points to the right command, it never edits project files. For technical code research use /unikit-explore; for game-design research use /unikit-gd-explore; to initialize the framework use /unikit.

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。

Firmware development for the Waveshare ESP32-S3-RLCD-4.2 (SKU 33298 / 33507, and the -EN variant) — an ESP32-S3-WROOM-1-N16R8 AIoT board built around a 4.2" fully reflective 300x400 monochrome LCD on a Sitronix ST7305/ST7306, with no backlight, plus an ES8311 speaker codec, an ES7210 dual-microphone ADC, an SHTC3 temperature/humidity sensor, a PCF85063A RTC, a microSD/TF slot on SDMMC, an 18650 holder with battery sensing, BOOT/KEY/PWR buttons and a 2x8 2.54 mm expansion header. Use when working on this board: project setup, platformio.ini, the qio_opi PSRAM memory type, the pin map and which header pins are traps, U8g2 and the ST7305 constructor, a blank or shifted panel, ST7305 low-power mode HPM/LPM, the shared I2C bus and its four slaves, I2S audio directions and the speaker amplifier enable, mounting the TF card, battery voltage, USB CDC console with no UART bridge, flashing over Type-C, deep sleep, or debugging why something on the board does not work.

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。

Household assistant for a whole family. Runs a morning brief, plans meals and builds grocery lists (hunting the best prices), looks ahead at the week's logistics, stays on top of the kids' school, watches prices on things the family wants, and helps book appointments and reservations. Use it for anything that keeps the family's day or week running. Trigger even on implicit asks - "what's on today", "what do the kids have this week", "what should we make for dinner", "add milk to the list", "when's the dentist", "did anything come from school", "remind me about the permission slip", "watch this jacket and tell me if it drops", "book us a table Friday".

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The GraphQL pipeline for SKMTC generators — authoring generators whose input schema is GraphQL SDL rather than OpenAPI. Covers `toGqlOperationEntry`, `GqlOperation`, `synthesizeArgsObject` (mutation args -> object schema), the GQL enrichment routing (`[id][rootKind][fieldName][variant]` — two nested subject keys where OAS has path+method), the `to<Lang>GqlOperationProjectionBase` companion factories, and the `GeneratorKey` shape `id|rootKind|fieldName|variant`. Use this skill ALONGSIDE `skmtc-generator` whenever the schema source is GraphQL SDL or the task mentions "GraphQL", "SDL", "GqlOperation", "toGqlOperationEntry", or GraphQL query/mutation generators. Engine rules (producers, register/insert, the axioms) stay in `skmtc-generator`; this skill carries only what differs for GraphQL.

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Run a self-retrospective on a SKMTC-related session — generator authoring, CLI configuration, debugging, or any sustained interaction involving `@skmtc/core`, `@skmtc/cli`, or `@skmtc/gen-*` packages. Captures three distinct outputs: (1) friction entries (mistakes, surprises, overridden defaults, multi-cycle struggles), (2) a "knowledge acquired" block recording facts the agent learned during the session that weren't in its training — the primary signal for doc gaps — and (3) a closing priority list ranking the top 2-3 findings for docs/skills triage. Wins are logged only when they identify a pattern worth codifying that isn't already in a skill. Use this skill when the user asks to "retro", "skmtc retro", "log friction", "reflect on this session", "what did we learn", "run a retrospective", or after substantive SKMTC work (generator authoring, multi-step debugging, non-trivial CLI configuration) where capturing observations would help improve skills, docs, or SKMTC itself. Distinct from the `skmtc-cli` and `skmtc-generator` skills — those guide *doing* the work; this skill captures observations *about* the work. Do not invoke this skill during ordinary work; it is end-of- session (or mid-session-checkpoint) only.

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Aggregate friction log files across a time period to identify recurring patterns, classify each cluster by intervention type, produce a prioritized action plan with success criteria, and calculate convergence metrics. Complements `skmtc-retro` (which captures per-session signal) by acting as the system's actuator: turning accumulated observations into decisions. The primary output is a review document that makes the "is it getting better?" question answerable. Use this skill when the user asks to "review retros", "review friction", "run a retro review", "what's the pattern across sessions", "what should we fix next", or on a periodic cadence (monthly, pre-release). Also run after a cluster of sessions on the same feature area to surface systemic issues before they compound. Distinct from `skmtc-retro` — that skill captures per-session observations; this skill synthesizes them into decisions. Do not run this skill as a substitute for a per-session retro.

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Master the `agentbrain` CLI (npm package `agentbrain-cli`). Use this skill whenever the user runs, installs, configures, or asks about `agentbrain` commands; when they interact with the AgentBrain enterprise data hub — organizations, connectors, knowledge bases, workflows, media/documents, knowledge graph, permissions, LLM/prompt config, cost & usage, governance, audit logs; when uploading documents/files to AgentBrain (`agentbrain media upload`); when authenticating (login/logout/token/apiKey); when retrieving MCP context (`agentbrain retrieve-context`); when running SQL against a connector; when the user pastes any `agentbrain …` command; when they edit `~/.agentbrain/config.json`; or when they mention AgentBrain, agent-brain, agentbrain-cli, or the `.agentbrain` folder.

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Write a short, fact-checked reply that answers a colleague's question in one meaning only, ready to copy and paste. The input is their pasted message. Check the claims against the code first, use the project glossary terms, and print only the reply in chat. Read-only — no writes to code, the issue tracker, GitHub, chat or any MCP. Use on "/cortex-communication:answer-buzzkill", "help me answer", "reply to this question", or when the user pastes a colleague's question and asks for an answer in the same manner.

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QA a change by driving the running dev app like a real QA engineer - start the server and a real browser session, write a checklist from the agreed requirements and the diff, execute every item with evidence, then go looking for anything that looks off even where the diff did not touch. Reports findings by severity and separates regressions from pre-existing problems. Use after a change is implemented and the gates pass, when the user asks to "QA this", "test it in the app", or "check it actually works".

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Score an Instagram creator's (or a list of creators') audience authenticity and engagement quality using Xpoz. Measures real engagement rate against views rather than follower count, the bot/spam share of their comments, and an overall 0-100 authenticity score for influencer vetting and partner selection. Use when asked to "vet this influencer", "are their followers real", "check engagement quality for @username", "rate these Instagram accounts", "compare creators before a sponsorship", or when the user pastes a list of Instagram handles to evaluate.

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Capstone/orchestration skill — build an M-Agent + N-Human cooperative IDE in Rust gpui (the Harbor): many agents and humans co-editing the same files as co-equal CRDT replicas, governed by claims/guard/salvage, across LAN/shared/remote harbors. The INDEX that dispatches into the sibling rust skills. Use when building the collaborative editor, the agent-fleet console, multiplayer editing with agents-as-peers, or any slice of the Harbor. Trigger on: cooperative IDE, collaborative editor, multiplayer editor, agents and humans co-editing, gpui IDE, Loro CRDT editor, harbor editor, claims/salvage, "build the cooperative IDE". NOT for: a single non-collaborative gpui screen (compose the siblings directly), web editors, or non-editor apps.

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Turn a raw idea into ONE honest recommendation: interview one question at a time until the problem, requirements, goals, non-goals, and constraints are CONFIRMED, then deliver a single verdict — do, don't, cheaper alternatives, trade-offs, ordered checklist, success metrics. Required: the user wants exactly ONE recommendation — not options, not a debate — and their framing is open to challenge (advise may answer a different question than the one asked). Triggers on "what should I do about X", "advise me on X", "am I approaching this right", "is this the right move". Do NOT use to be interrogated with no recommendation (→ mk:grill) — advise also interviews one question at a time, but only to reach a verdict. Do NOT use when the framing is settled and only the options are open (→ mk:brainstorming). Do NOT use to decide whether something is worth building (→ mk:office-hours). Do NOT use when you want several perspectives to argue it out (→ mk:party).

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。

Spin up a working LiveAvatar demo from a curated catalog — clones the reference repo, installs dependencies, provisions the LiveAvatar account, and fills the env file, then hands off the parts only a human can do. Use when: (1) User wants to try, test, see, or demo LiveAvatar, (2) User asks what LiveAvatar demos or examples exist, (3) User wants a sales agent / lead-qualification avatar, (4) User wants a LiveKit agent driving a LiveAvatar avatar, (5) User wants an avatar joining their own LiveKit room, (6) User wants a speech-to-speech avatar, a GPT-Live demo, a language-learning or tutor avatar, a customer-support avatar, a poker coach / game-style avatar, or an avatar with on-screen visuals/overlays, (7) User says "run the LiveAvatar demo", "show me LiveAvatar", "get me started with LiveAvatar", or "clone the LiveAvatar example", (8) User is evaluating LiveAvatar and wants something running before writing their own integration.

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。

Up-to-date Zig programming language patterns for version 0.17.0-dev (0.16.0 stable APIs plus the 0.17 deltas). Use when writing, reviewing, or debugging Zig code, working with build.zig and build.zig.zon files, or using comptime metaprogramming. Critical for avoiding outdated patterns from training data - especially std.net→std.Io.net (requires Io instance), std.time timestamps removed (use clock_gettime), std.Thread.Mutex/Condition/sleep removed (use pthreads), std.crypto.random removed, build system APIs (root_module, Compile methods→Module methods), I/O APIs (buffered writer pattern), container initialization (.empty/.init), allocator selection (DebugAllocator), ArrayList now unmanaged by default, @typeInfo lowercase fields (.@"struct" not .Struct), and removed language features (async/await, usingnamespace). Also covers 0.17.0-dev deltas: b.args→run_cmd.addPassthruArgs(), std.gpu→std.spirv, @bitCast logical-bit (endian-agnostic) semantics, build configurer/maker split, and the zig-pkg/ package directory. Also covers quality tooling Zig does not ship: coverage with kcov (and why in-file tests wreck the denominator), detecting unused private functions (neither the compiler nor zlint's unused-decls finds them), duplicate detection with jscpd, zlint (NOT the same-named X.509 linter in Homebrew), and the built-in fuzzer whose testOne now takes *std.testing.Smith rather than []const u8.

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Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.

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。

Read-only release + backlog recap. Reports the last release, commits-since-tag classified by conventional-commit type with the semver bump they trigger and pushed-vs-origin state, the backlog bucketed (open/picked-up/dropped with epic parent→child nesting), the roadmap epics with their per-task tallies, and condensed open questions — then recommends the next pickup. One `cli.mjs recap` call returns all six recap keys. Invoke any time (on demand) to plan a release or choose the next thing to build; a compact form is also surfaced at session start. Not a workflow phase; never writes CHANGELOG; never starts or commits work.

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Deep enrichment for community-sourced enterprise PROSPECTS (the 🎯 Prospective enterprise customers subsection). For each prospect, resolve their LinkedIn from their GitHub self-linked accounts FIRST (search only if none is self-linked), verify the employer matches, capture their full name, then list the company website + company size (ARR / latest funding round / employee count). Runs as a general-purpose subagent (many web searches) so the orchestrator's context stays small. Invoked by weekly-report (enterprise section) and the enterprise skill.

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INTERNAL/HELPER skill — do NOT invoke directly from a user query. This is the shared formatter/poster used by answer-issue-questions and breakdown-issues. It formats a scope GitHub Issue body (label cc-triage-scope, used before code analysis), runs the pre-posting checklist, and executes `gh issue create`. Invoke this skill ONLY from one of the parent skills via the Skill tool, after the parent has finalized the task breakdown. If a user asks to 'create a scope issue' or similar, route them to the appropriate parent skill (/breakdown-issues for fresh breakdowns, /answer-issue-questions for derived sub-tasks) rather than invoking this one directly.

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Capture a POC's signed-off behaviour into its spec ledger (`<poc>/spec.json`) — the structured, walkable behaviour contract graduation freezes as authoritative. Run at the moment a behaviour gets signed off ("ok, this works"), while it's fresh — the same trigger as the HANDOFF.md update, but capturing the *behaviour as a testable entry* rather than the *decision as prose*. Use when a POC behaviour is approved, or asked to "capture this spec", "write the spec entry", "log this to the ledger".

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。

Day-to-day operation runbook for nuxt-crouton apps — boot any app/POC locally with auth working, seed and inspect the local database (including the miniflare-vs-.data split that makes seeded data "disappear"), turn on the review overlay / eruda, observe deployed staging (tail, smoke, row counts), and capture screenshots. Use when asked to "run the app", "boot velo/fanfare/a poc locally", "why can't I log in locally", "seed the database", "the seed data doesn't show up", "look at the local db", "check what's on staging", "screenshot the app", or "where do the artifacts land".

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Propose the test(s) FIRST and get a human to sign off on the behaviour before writing the code — the test analog of /schema-review and /ui-proposal. For hand-written LOGIC in packages/* (apps opt-in, pocs exempt). Renders the proposed failing test as a reviewable contract, holds for lgtm/approve, then you write the code to make it green. Use when an agent is about to implement packages/* logic, or when asked to "write the test first", "TDD this", "agree on the test", run /test-review.

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System reference for the crouton layout engine (@fyit/crouton-layout) — the LayoutTree/layout_configs data model, the croutonLayoutBlocks registry and sizing contract, the viability metric, the deterministic placer (#709), and how layouts persist and seed. Use when reasoning about WHY a layout renders/composes/persists the way it does, when a question is shaped like "what is a LayoutTree / where do layouts live / why did the placer pick this arrangement / why won't this pane shrink / where does minWidth get read", or when a generated POC boots with the wrong (or no) default layout. Trigger phrases: "layout tree", "layout_configs", "viability check", "composeDefaultLayout", "block sizing contract", "why is my layout not viable". For authoring a block component, use the block-authoring skill instead.

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。