「ada」の検索結果

Render the GAME-DESIGN document (the GDD) into a human-readable form — read-only Markdown chapters under docs/design/ (index, systems, flows, content, economy, glossary), with facts resolved inline from GD-IDS.yaml and drafts flagged 🚧. Optional --web also renders an HTML site from the unikit-docs template (falls back to Markdown-only if the template is absent). Use to export or publish the design for people to read, e.g. "render the GDD", "export the game design to docs", "generate readable design docs", "publish the GDD", "make a design doc site". Read-only — it never edits the GDD or registry, only renders it. This renders the game DESIGN — for the project's CODE documentation (README, docs/*, modules, architecture) use /unikit-docs.

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Elite website image-to-code skill for Codex. For visually important web tasks, it must first generate the design image(s) itself, deeply analyze them, then implement the website to match them as closely as possible. In Codex, it must prefer large, readable, section-specific images instead of tiny compressed boards, generate fresh standalone images for sections or detail views instead of cropping old ones, avoid lazy under-generation, avoid cards-inside-cards-inside-cards UI, and keep the hero clean, spacious, readable, and visible on a small laptop.

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Concise communication mode. Cuts ~60-70% of output tokens while keeping natural, readable English. Drops filler, hedging, and pleasantries but maintains grammar and sentence flow. Elaborate on request -- ask for detail and get it, then auto-return to concise. Use when user says "concise mode", "be concise", "less verbose", or invokes /concise. Also triggers when user requests token efficiency with readability.

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。

Write, audit, or improve an iOS App Store listing — app name, subtitle, keyword field, screenshots, app icon, preview video, creative assets, tags, ratings strategy, localization, and featuring nominations. Use when creating or reviewing App Store metadata, doing keyword research, planning a metadata update, or when the user mentions ASO, App Store Optimization, app store listing, keywords, subtitle, screenshot captions, preview video, app icon, Liquid Glass, creative assets, product page header, search results creative, Asset Library, featuring, ratings prompt, or Apple Ads.

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。

Adaptive testing engine with IRT/CAT, AI question generation, and personalized learning recommendations

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Design or change dygo Entity metadata, Fields, Record Collections, links, naming, routes, indexes, and constraints. Use when a business data model or metadata contract is the main task.

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Language models exhibit predictable degradation patterns as context length increases. Understanding these patterns is essential for diagnosing failures and designing resilient systems.

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Define la identidad visual de un producto — propone dos o tres direcciones de color y tipografía justificadas por lo que el producto es, las escribe en design/tokens.css y las deja listas para verlas aplicadas en preview.html antes de decidir. Úsalo tras definir el producto y ANTES de prototipar (p. ej. "define la identidad", "qué paleta usamos", "elige los colores del producto"). No construye vistas.

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Instancia esta plantilla en un proyecto real mediante una entrevista guiada — detecta si el proyecto es nuevo o existente, rellena los placeholders, adapta los docs al tipo de proyecto y decide la política de permisos. Úsalo cuando la persona clone/adopte esta plantilla y quiera configurarla para su proyecto (p. ej. "instancia esta plantilla", "configura este template para mi proyecto", "arranca el proyecto desde esta base").

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Porta el cambio de la rama actual a las variantes hermanas de esta plantilla (es/en × con-IA/sin-IA) — traduce el contenido, adapta o elimina la capa de IA según el destino y deja cada variante con una rama lista para PR. Úsalo cuando un cambio del template deba replicarse en las otras variantes (p. ej. "porta este cambio al template en inglés", "replica esto en las variantes").

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Use when writing, reviewing, or refactoring Vue 3 code — components, composables, reactivity, Pinia state, Pinia Colada data-fetching, file-based routing, and forms. Targets Composition API + <script setup> + TypeScript. Auto-activates on .vue files. For deep best-practices review/recommendations or the full ordered workflow (built-in components, animations, performance pass), use mk:vue-best-practices.

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Use when running an autonomous multi-hour build of a green-field product — orchestrates planner → contract → generator ⇄ evaluator loop with adaptive scaffolding density per model tier. Triggers on /mk:autobuild, "build me a kanban app", "build a retro game maker", "autonomous build", or any green-field product spec. NOT for scoped single-task work (see mk:cook); NOT for initial project scaffolding only (see mk:bootstrap).

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hada.io RSS feed monitoring for AI agent/harness articles with automated /scout analysis

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Central catalog of available skills with metadata, capabilities, and performance history. Provides skill discovery and lookup services. Activate on 'skill registry', 'list skills', 'skill catalog', 'available skills', 'skill metadata'. NOT for matching skills to tasks (use dag-semantic-matcher) or ranking (use dag-capability-ranker).

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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).

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。

App Store and Google Play submission automator with Fastlane, screenshot automation, metadata management, and TestFlight/internal testing. Activate on: app store submission, Fastlane, TestFlight, Google Play Console, screenshot automation, metadata management, app review, code signing, provisioning profiles. NOT for: CI/CD pipeline setup (use github-actions-pipeline-builder), app architecture (use react-native-architect), analytics (use mobile-analytics-crash-reporting-expert).

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Aligns sample metadata with count matrices and constructs design matrices for downstream DE, handling the alphabetical-reference-level trap (relevel BEFORE DESeq), LRT reduced-model rules, the interaction-term resultsNames trap, continuous-covariate scaling and splines, repeated measures via duplicateCorrelation or dream, high-cardinality categorical pseudo-singular designs, sample swap detection via XIST/RPS4Y1 expression and somalier/NGSCheckMate genotypes, SABV (sex-as-biological-variable) mandate, Simpson's-paradox collapsing of technical replicates, and the `~ 0 + group` parameterization for clean contrasts. Use when building a design matrix, troubleshooting reversed fold-change direction, encoding paired or repeated-measures designs, detecting sample swaps, deciding sex-as-covariate, or aggregating technical replicates.

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Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 (GC-bias-aware differential via its bam_ip/bam_input control + bam_treated_ip/bam_treated_input treatment arms), QNB beta-binomial, MeTDiff HMM, and RADAR, plus the paired-symmetric edgeR/DESeq2-on-peak-counts route when batch/lot covariates need fixed-effect handling that exomePeak2's API does not accept. Covers paired vs unpaired vs interaction designs, batch confounding and per-lot meta-analysis, the stoichiometry-vs-expression-vs-IP-efficiency confound, and effect-size filtering against under-powered N=2 designs. Use when comparing m6A across two or more conditions, choosing between exomePeak2/QNB/RADAR/MeTDiff for a design, handling batch confounding when exomePeak2's API is too rigid, distinguishing real hyper/hypo-methylation from expression shifts, applying effect-size thresholds, or planning orthogonal stoichiometry validation (GLORI/SAC-seq/m6Anet mod_ratio).

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Aligns and QCs methylated-RNA-immunoprecipitation (MeRIP / m6A-seq) IP and input libraries using STAR or HISAT2 splice-aware mapping, samtools sort/index, IP/input matched-pair tracking, antibody-lot metadata recording, replicate concordance via deepTools multiBamSummary + plotCorrelation, IP enrichment QC via plotFingerprint and per-transcript IP/input ratio distributions, library-complexity saturation curves via PreSeq, and the explicit do-NOT-deduplicate convention for standard non-UMI MeRIP. Use when preparing paired IP and input BAM files for exomePeak2 / MeTPeak / MACS3 peak calling, evaluating MeRIP replicate concordance and IP enrichment, deciding whether to deduplicate (standard MeRIP typically NOT), choosing genome-vs-transcriptome alignment for downstream peak vs m6Anet workflows, recording antibody clone and lot metadata for cross-batch reconciliation, detecting failed IPs via saturation curves and IP/input distribution shape, or generating IP-over-Input bigWig tracks for visualisation.

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Preprocess CLIP-seq reads (eCLIP, iCLIP, iCLIP2, iCLIP3, irCLIP, PAR-CLIP, FLASH) with protocol-specific UMI extraction, adapter trimming, length filtering, and post-alignment PCR-duplicate collapse. Use when raw CLIP FASTQ must be turned into deduplicated, crosslink-preserving BAM input for peak calling; choosing between two-pass and single-pass adapter trimming; deciding minimum read length; or mapping UMI patterns to specific eCLIP/iCLIP/iCLIP2/iCLIP3 library preps.

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Considers adapting the basal insulin rate at hospital admission for patients using insulin pump therapy. Triggered by clinician questions such as "Should we adjust the basal rate upon admission for pump therapy?" or when evaluating pump continuation versus transition to scheduled subcutaneous insulin.

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。

Use when female Chinese web novel writing or 女频小说写作 needs Fanqie female-rank market radar, 言情, 古言, 现言, 快穿, 团宠, 权谋, 情绪流, 女性向人设, 人物关系, 评论区, 短视频文案, or platform voice references for AI-assisted fiction writing.

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。

Expert-level industrial automation covering PLCs, SCADA, industrial networks, safety systems, robot integration, and Industry 4.0 technologies.

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Expert-level battery technology covering electrochemistry, lithium-ion battery physics, battery management systems, degradation mechanisms, and next-generation battery chemistries.

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。