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Explicit-only last resort. Draws game art from code, with no image model - pixel sprites from a PixelSpec, flat vector props, UI and icons from portable SVG, rigged characters with planted feet, FX with a JS runtime, seam-proven autotiles, playable top-down layouts with collision, stylized parallax and ambient plate motion - palette-exact, with QA and review sheets. Use only when the user explicitly asks for code-drawn art, or when generate2dmedia route_media.py finds no image or video route (no-route, exit 3). Never the default - image generation (generate2dsprite, generate2dmap, video2dsprite) comes first for characters, props, maps and FX. Not for painterly, detailed or likeness art, HD-2D plates or realistic fire, smoke or water. Always disclose the result as code-drawn.

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Processes GCP infrastructure design and deployment workflows within Application Design Center (ADC). Use when: - Designing GCP infrastructure with Terraform. - Validating local HCL. - Performing best-practice plan scans. - Importing templates to Application Design Center (ADC). - Deploying templates. - Troubleshooting deployment failures. Boundaries: - Only use for GCP-specific cloud infrastructure. - Only use for Terraform coding within the ADC context.

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Read-only repository explorer. Use PROACTIVELY for cold-start exploration, broad cross-file localization, or when a direct search has failed and you need to find where something lives. Skip it when the issue already names the exact file or symbol, or a previous turn already returned usable file:line evidence. Returns only compact path:line citations; its reads and greps never enter the main conversation.

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Catalog-then-fix slop removal on an existing prose draft. Parallel catalog subagents inventory four slop categories (theatrics, overwrought register, corrective antithesis, dramatic short sentences), fix subagents repair only what a catalog names, mechanical lint + frequency analysis bracket the run, and a regression gate (lint delta, word count, cold meaning check) decides whether the result ships. Use when the user says "deslop this draft", "catalog deslop", "strip the AI patterns from this file", or as the final stage of the article-factory and mosaic-writing skills. For writing or redrafting prose from scratch, use the prose skill instead - this skill only repairs an existing draft in place.

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xCloud Docker Deploy v1.4.1 — a confirmation-gated skill for preparing projects for xCloud. It inspects or changes files only after user consent, generates Docker/GHCR deployment files, fixes xCloud port rules, and routes live API work only after a separate explicit token-consent step.

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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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The model-generator shape for Skmtc: one definition per component schema, built by copying the shipped SKELETON package and filling its SLOT markers with the target library's syntax. Covers the edge cases every model generator must survive — refs, recursion, optional vs nullable, additionalProperties, enums, readOnly/writeOnly. Use when authoring or editing a generator that maps schemas to a validator/schema/type library ("write a gen-<lib>", "map OpenAPI models to <lib>"). Load ALONGSIDE skmtc-generator (engine rules) and skmtc-lang-typescript (TS layer).

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Generate and maintain the project's TECHNICAL documentation from its codebase — scans the project structure, tech stack, and module boundaries, then writes a lean README landing page plus detailed topic pages (architecture, modules, setup, build, APIs), only the docs that are relevant. Use whenever the user wants to create, update, or validate documentation of the CODE or the project itself, e.g. "generate documentation", "create docs", "write the README", "update the project docs", "document the codebase", "document this project's architecture", "validate the project documentation". This is CODE / project documentation only — it never documents the game design. To render the GAME DESIGN (the GDD: systems, flows, content, economy) for people to read use /unikit-gd-docs.

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Write, open and — when allowed — merge a pull request for the current feature branch of a {{engine_name}} project, with or without a plan. Builds a plain-language PR text (one feature per line) from the plan's `## Modules` and task `WHY:` lines, or — without a plan — from the commits and the code changes themselves; pushes the current state (asking where to end when it stands inside a plan module), opens or updates the branch's single open PR through the GitHub MCP, and merges with a merge commit only when the merge brings nothing from the base branch. Levels: remind (text only), create, merge; capped by `git.pull_requests.max_level`. Trigger phrases: "pull request", "open a PR", "create PR", "PR description", "merge the PR". For commit messages use /unikit-commit; for reviewing a PR's code use /unikit-review.

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Analysis of Competing Hypotheses. Operationalizes Richards Heuer's CIA-tradition technique for systematically narrowing among multiple hypotheses against evidence. Builds an explicit hypothesis-vs-evidence matrix and focuses on disconfirmation rather than confirmation — the central insight: hypotheses cannot be proven, only disconfirmed; the surviving hypothesis is the one with the least disconfirming evidence. Spawns parallel hypothesizers (in isolation, across hypothesis-generation angles) and parallel evidence-gatherers (across evidence classes), then synthesizes into a matrix, diagnosticity analysis, sensitivity analysis, and falsification milestones. Produces feedback only — no code, no tickets, no artifacts.

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Add Telegram as a channel. Can replace WhatsApp entirely or run alongside it. Also configurable as a control-only channel (triggers actions) or passive channel (receives notifications only).

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Remediation only — repair web accessibility (a11y) violations against WCAG 2.2 with a baseline, edit, and verify loop. Takes a target (URL, files, directory) or a findings worklist from `accessibility-scan`/`accessibility-inspect`/`accessibility-audit`, applies mechanical fixes as given, leaves TODOs for visual or contextual judgment, and verifies by re-running the baseline check. It only fixes. To find issues use `accessibility-scan` (one page, automated), `accessibility-inspect` (one page, manual), or `accessibility-audit` (whole site, WCAG-EM); to check for regressions use `accessibility-diff`. Use it for 'fix the a11y issues in X', 'make this accessible', 'add missing alt text and labels', 'apply these accessibility fixes', 'remediate these violations'.

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rescue 브랜치 history-only 보존 후 삭제 검증

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diagnostic-monitor-read-only-gate

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Draft the QA scope and plain-language Given/When/Then test scenarios for a Jira ticket, built from the full ticket, the decisions made in its comments, and the actual code change read commit by commit (open PR, the ticket's merged PR, the branch against its base, or a named commit range). Saves a Markdown draft under docs/, iterates with the user, then posts it to the ticket as a new Jira comment only after explicit approval. Use on "QA scope for PROJ-123", "what should QA test", "write test scenarios for QA", "hand this ticket to QA", "prep the QA handoff", "gherkin scenarios for this ticket", or when a branch is ready for manual testing — even when the user only names the ticket or says "this branch".

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Resolve CodeRabbit review feedback on a GitHub pull request. Collects every open CodeRabbit finding (inline threads, plus the nitpick and outside-diff findings packed into review bodies), judges and fixes each one against the real code with subagents, unifies fixes that share a pattern, commits locally, asks before pushing, then shows every reply draft linked to its finding and posts only on approval. Runs only on top-tier models. Use on "resolve the CodeRabbit feedback", "address coderabbit comments", "answer the rabbit", "fix what CodeRabbit flagged on

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Discover which AI prompts and topics matter for a brand's Answer Engine Optimization (AEO) using only free tools. Crawls a website, analyzes the brand's positioning, generates prioritized prompts people ask AI assistants, and audits existing content coverage — all without paid APIs. Use when a user wants to: find what questions people ask AI about their industry, discover AEO opportunities, research prompts for content creation, audit a site's AI visibility, or build an AEO content strategy. No API keys required — uses web_fetch, web_search (free tier), and LLM reasoning only.

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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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Analyze any GitHub repository in AI-friendly format. Convert entire repos to single markdown documents, generate architecture diagrams with Mermaid, inspect structure trees, language breakdowns, and recent activity. Includes GitHub URL tricks, API shortcuts, and advanced search techniques. Read-only analysis — never executes code from repositories. Built for AI agents — Python stdlib only, no dependencies. Use for repository analysis, code architecture review, open source research, GitHub intelligence, repo documentation, and codebase understanding.

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Manual-only skill for minimizing total codebase size. Only activate when explicitly requested by user. Measures success by final code amount, not effort. Bias toward deletion.

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Validate the GenAI stack (services, authentication, models, notebooks, GPU). Arguments: [all|services|auth|models|notebooks|vram] [--full] [--auth-only] [--nodes-only] [--notebooks]

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Verify and test Jupyter notebooks with iterative fixing. Arguments: [target] [--quick] [--fix] [--python-only] [--dotnet-only]

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Use an available Codex MCP integration as a read-only second-opinion verifier for GEODE code reviews, GAP audits, and contract checks. Use only when the user requests Codex cross-checking and the current tool inventory exposes the required MCP tools.

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A radically minimal, blank-canvas interface built as a pure edge-to-edge surface, with almost no color and typography carrying the visual weight. Black serves as the only filled color, the only divider, and the sole surface tone cards.

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。