「decision」の検索結果

Protocol for handling ambiguous decisions and missing/conflicting knowledge during code generation, design, and review. Ensures AI surfaces genuine judgment calls with structured options and stops on hallucination risk instead of silently assuming. Use when a decision has multiple valid approaches, when facts are missing or contradictory, when the user asks 'what should we do here?', 'is this a judgment call?', 'should I ask about this?', 'am I guessing here?', 'what are the tradeoffs?', or when deciding between two reasonable architectural or design options. Also composed by molecules to define how judgment calls and clarification requests are surfaced and resolved.

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Conduct market research, competitive analysis, investor due diligence, and industry intelligence with source attribution and decision-oriented summaries. Use when the user wants market sizing, competitor comparisons, fund research, technology scans, or research that informs business decisions.

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Guides God Committee members through consensus-building for collective decisions. Use for proposals, voting, and disagreement resolution. Triggers on: consensus, voting, proposal, committee decision.

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Define how system components connect, establishing boundaries, patterns, and integration approaches during PRD v0.6 Architecture. Triggers on requests to design architecture, create system design, define component relationships, or when user asks "design architecture", "system design", "how do components connect?", "architecture decisions", "technical architecture", "system overview". Consumes TECH- (stack selections), RISK- (constraints), FEA- (features). Outputs ARC- entries documenting architecture decisions with rationale. Feeds v0.6 Technical Specification.

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This skill should be used when the user says "/council", "council", "deliberate", "multi-perspective decision", "트레이드오프 분석", "위원회 소집", "여러 관점으로 검토", or wants deep multi-perspective deliberation with tradeoff mapping. Combines tribunal (structured adversarial review), agent-council (external LLM opinions), dev-scan (community sentiment), and step-back (meta-level review) into a unified decision-making committee. Uses Agent Teams for real peer-to-peer debate with iterative step-back moderation loop.

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Keep a reviewable decision trail for long-running or unattended work: a TSV log with one row per decision (what, why, evidence, result). Local by default; commit it when a reviewer needs the trail to trust the result. Use for /show-me-your-work, autonomous or multi-phase runs, or work a human reviews after stepping away.

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Building modular, debuggable AI behaviors using behavior trees for game NPCs and agentsUse when "behavior tree, bt, npc ai, ai behavior, game ai, decision tree, blackboard, ai, behavior-trees, npc, game-ai, decision-making, agents" mentioned.

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The practice of collecting, analyzing, and acting on data to drive product decisions. Great analytics isn't about dashboards—it's about insights that lead to action. Every metric should answer a question that changes behavior. This skill covers event tracking, metrics design, dashboards, user behavior analysis, and data-driven decision making. The best analytics teams measure what matters, not what's easy to measure. Use when "analytics, metrics, tracking, dashboard, funnel, cohort, retention, events, KPI, measure, data, insights, conversion, engagement, analytics, metrics, data, dashboards, tracking, funnels, cohorts, KPIs, insights" mentioned.

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API Patterns workflow skill. Use this skill when the user needs API design principles and decision-making. REST vs GraphQL vs tRPC selection, response formats, versioning, pagination and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

API Patterns workflow skill. Use this skill when the user needs API design principles and decision-making. REST vs GraphQL vs tRPC selection, response formats, versioning, pagination and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

API Patterns workflow skill. Use this skill when the user needs API design principles and decision-making. REST vs GraphQL vs tRPC selection, response formats, versioning, pagination and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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This skill should be used when the user asks to "design system architecture", "evaluate microservices vs monolith", "create architecture diagrams", "analyze dependencies", "choose a database", "plan for scalability", "make technical decisions", or "review system design". Use for architecture decision records (ADRs), tech stack evaluation, system design reviews, dependency analysis, and generating architecture diagrams in Mermaid, PlantUML, or ASCII format.

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Applies Judea Pearl's causal reasoning frameworks to distinguish correlation from causation, evaluate AI capabilities, and make counterfactual decisions. Reach for this skill whenever Claude encounters questions about causal inference, structural causal models, the limitations of deep learning, AGI, experimental design, covariate selection, or personalized decision-making. Trigger this skill for topics involving Bayesian networks, the do-calculus, the Ladder of Causation, or when a user tries to answer 'what if' or 'why' questions using purely observational data. Pearl's principles are essential for moving beyond probability calculus into true causal understanding.

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Use when a product decision or feature needs a written record - what to capture in a task document (PRD, decision record) and where to attach it, since TaskTrooper has no epic

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Translate technical decisions into human language. Explain in cost, speed, stability.

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Channeling the documented thinking of named notable figures (an 'itako' medium) — their mental models, heuristics, and decision-style as advisory lenses. Not for decisions (Magi) or personas (Cast).

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予測モデリング（教師あり機械学習 / 予測モデル / 精度評価 / 交差検証 / リーク、決定木 / ランダムフォレスト / GBDT / LightGBM / XGBoost / CatBoost、モデル解釈 / 特徴量重要度 / SHAP / PDP / ICE、時系列 / 季節性 / ARIMA / SARIMA / Prophet / VAR / 変化点 / 時系列予測）を実行したら必ずセットで出す図と値のルーター。train_test_split, cross_val_score, GridSearchCV, Pipeline, TimeSeriesSplit, DecisionTree, RandomForest, lgb., xgb., CatBoost, dtreeviz, shap., permutation_importance, PartialDependenceDisplay, statsmodels.tsa, SARIMAX, STL, adfuller, plot_acf, Prophet, ruptures がコードに現れたとき、または ユーザーが「予測モデルを作って」「精度を出して」「どの変数が効いているか」「来月を予測して」「季節性を見て」と 言ったときに使う。診断・リーク・ベースライン・残差に言及がなくても適用する。SKILL.md のルーティング表で手法を特定し、 対応する references/<手法>.md を読んでから実行する。回帰係数の推論・検定は statistical-inference-diagnostics、 効果推定は causal-inference-diagnostics を使う。

Auto-review pipeline — reads the full CEO, design, eng, and DX review skills from disk and runs them sequentially with auto-decisions using 6 decision principles. (gstack)

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When the user wants to apply psychological principles, mental models, or behavioral science to marketing. Also use when the user mentions 'psychology,' 'mental models,' 'cognitive bias,' 'persuasion,' 'behavioral science,' 'why people buy,' 'decision-making,' 'consumer behavior,' 'anchoring,' 'social proof,' 'scarcity,' 'loss aversion,' 'framing,' or 'nudge.' Use this whenever someone wants to understand or leverage how people think and make decisions in a marketing context. For applying psychology to specific pages, see cro; for pricing tactics, see pricing; for copy framing, see copywriting.

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Records decisions and documentation. Use when you need to document an architecture decision (ADR) or the reasoning behind a design choice, when changing public APIs, shipping features, or when you need to record context that future engineers and agents will need to understand the codebase.

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Universal context persistence across sessions. Auto-saves decisions, conventions, and progress to .rune/ files. Loads state at session start. Use when any skill makes architectural decisions or establishes patterns that must survive session boundaries.

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Persistent state tracking and Architecture Decision Records across sessions. Use when recording a decision, ADR, or progress that must survive session boundaries. Manages progress state, module health, dependency graphs, and ADRs for any workflow.

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🇺🇸 Open-Source Marketing & GitHub Stars Growth System — Decision framework for each growth stage. Pre-launch readiness → Show HN / Reddit / Product Hunt launch → 6-month growth loop. README rules, KOL outreach, dev-content matrix (dev.to / Zenn), anti-star-farming. Built from taking AFFiNE 0 → 60K stars. 🇨🇳 开源营销与 GitHub Star 增长系统 — 每个增长阶段的决策框架。Pre-launch 就绪 → Show HN / Reddit / Product Hunt 发布 → 6 个月增长循环。README 规则、KOL 合作、开发者内容矩阵（dev.to / Zenn）、防刷 star。提炼自 AFFiNE 0 → 60K star 实战。 🇯🇵 オープンソースマーケティング＆GitHub スター成長システム — 各成長段階の意思決定フレームワーク。Pre-launch → Show HN / Reddit / Product Hunt → 6ヶ月成長ループ。READMEルール、KOL連携、開発者コンテンツマトリックス。 🇰🇷 오픈소스 마케팅 & GitHub 스타 성장 시스템 — 각 성장 단계별 의사결정 프레임워크. 사전 출시 → Show HN / Reddit / Product Hunt → 6개월 성장 루프. README 규칙, KOL 아웃리치, 개발자 콘텐츠 매트릭스. Triggers: "open source marketing" | "GitHub stars" | "star growth" | "Show HN" | "Hacker News launch" | "OSS launch" | "developer marketing" | "Reddit launch" | "dev.to" | "open source growth" | "开源营销" | "GitHub star 增长" | "开源发布" | "Show HN 怎么发" | "开发者营销" | "オープンソース" | "오픈소스 성장"

Act as an end-to-end data scientist: turn business questions into defensible analysis, validated models, and decision-ready reports. Use whenever the user asks to analyze, explore, or profile a dataset or CSV/Parquet/Excel file; asks what drives a metric or why a number changed ("why did churn go up?"); wants to test whether a difference is real (A/B tests, experiments, "is this significant?", "how many samples do I need?"); wants a predictive model (churn, forecast, scoring, segmentation, classification, regression); asks to review an existing analysis, notebook, or model for flaws; or needs results written up for decision-makers. Triggers in any language ("phân tích dữ liệu", "xây model dự đoán", "kiểm định A/B"), even when they never say "data science" or "statistics".

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