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概要と使いどころ
Growth strategy for product-led and loop-driven growth systems. Use when building growth strategy, designing growth loops, planning acquisition channels, evaluating network effects, deciding when to scale, or coordinating product, engineering, data, and marketing around growth. For SEO audits use seo-and-aeo-strategy; for page/form CRO use conversion-rate-optimization; for A/B test design use ab-test-setup.
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 optimizing, auditing, or preparing to ship web animations or rendering performance work: frame loops, scroll/viewport reveals, mount/unmount transitions, canvas/WebGL lifecycles, reduced motion, lazy rendering, and deferred off-screen work. Use for jank, per-frame allocations, forced reflows, render loops, animations that do not pause off-screen, content-visibility, requestIdleCallback, analyzing Chrome DevTools performance traces for animation or rendering issues, or choosing between browser-driven CSS/WAAPI, minimal JS, phase, and Motion/GSAP. Use when the user explicitly asks about phase APIs or behavior. Do not use during exploratory animation ideation, prototyping, visual iteration, or trying variants, even when the code already imports phase; wait until the user asks for performance guidance, an audit, production implementation, ship-readiness review, or about phase itself.
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。
Adds per-skill learnings loops for dated patterns, mistakes, and domain facts. Use when wiring skill memory, consolidation, or drift audits.
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。
Reinforces an AI agent by turning Monte Carlo's reinforcement loop diagnosis into code fixes. Reads the daily reinforcement loop report for an agent's workflows, ranks the diagnosed issues, proposes what to fix, and — with the user's approval at each step — opens a pull request. Activates on "fix my agent", "improve my agent's health", "reinforce my agent", "what should I fix in my agent". Not for investigating a specific agent alert or trace (monte-carlo-troubleshoot-agent-traces), creating agent monitors (monte-carlo-monitoring-advisor), or instrumenting a new agent (monte-carlo-instrument-agent).
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。
Closed-loop compare-adjust-repeat workflow for fitting Blender models to supplied front/side/back/top templates and originals. Use when the user asks to compare the product to templates/originals and adjust until it fits across all dimensions, or when all views must pass measurable bbox/centroid/silhouette/edge validation before export.
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。
Create if-then implementation intentions and WOOP plans for specific student behaviour change goals. Use when students know what to do but struggle to follow through on habits or routines.
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。
Design viral loops into your product — referral programs, invite systems, and share mechanics that drive exponential growth. Includes K-factor calculations and real examples from Dropbox, Slack, and Notion. By @WeiYipei.
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。
🇺🇸 Your dev team ships features nobody asked for while user-reported bugs pile up for months. Operations blames engineering for ignoring users; engineering blames operations for not understanding technical constraints. This gives you the complete Product × Engineering × Operations alignment SOP — from unified backlog to 10-day sprint cadence to veto power rules. What's inside: • Dual-layer Kanban system (master backlog + sprint board with unified tagging) • 10-day sprint standard process (Day 1 dev → Day 6 testable build → Day 10 ship) • Issue template with reproducibility requirements (3x reported = auto-severe) • Tri-party alignment meetings (daily standup / sprint planning / sprint review) • Operations veto power on releases (P0 bug = block shipping) • User feedback → product iteration closed loop (beta testing + interview SOP) • Core metrics framework (acquisition → activation → retention → monetization → referral) • Technical debt management (20-30% sprint capacity reserved) • Ready-to-use templates: Bug Report, Sprint Planning, Responsibility Matrix Built from: Real product strategy meetings + beta testing frameworks. References Supabase sprint model, Manus/DeepSeek commercialization alignment. By @WeiYipei. 🇨🇳 你的研发团队在做没人要的新功能，用户反馈的 Bug 堆了三个月没人动。运营觉得研发不听用户，研发觉得运营不懂技术。这份 SOP 给你从统一看板到 10 天迭代节奏到一票否决权的完整产研运协同框架。 🇯🇵 開発チームは誰も求めていない機能を作り、ユーザーから報告されたバグは何ヶ月も放置。このSOPは、統一バックログから10日スプリント、リリース拒否権まで、プロダクト×エンジニアリング×オペレーションの完全な連携フレームワークを提供します。 🇰🇷 개발팀은 아무도 요청하지 않은 기능을 만들고, 사용자가 보고한 버그는 몇 달째 방치됩니다. 이 SOP는 통합 백로그부터 10일 스프린트, 릴리스 거부권까지 제품×개발×운영 완전 협업 프레임워크를 제공합니다. Triggers: "product ops" | "engineering operations" | "product development SOP" | "sprint planning" | "iteration management" | "cross-functional alignment" | "product engineering ops" | "dev ops collaboration" | "产研运协同" | "迭代管理" | "产品研发运营" | "プロダクト開発運営" | "제품개발운영"
🇺🇸 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"
Run a Meta-Harness-style optimization loop NATIVELY — automatically search over the scaffolding around a FIXED base model (memory, retrieval, context construction, prompt templates, summarization, tool-selection logic) by proposing candidate variants, scoring each on a cheap deterministic eval, and keeping a Pareto frontier of quality vs cost — using native Agent / Workflow / loop tools instead of a standalone Python harness. Use this whenever the user wants to optimize, evolve, tune, distill, or search over a harness, scaffold, prompt system, memory or retrieval policy, context-assembly code, or summarizer while keeping the model fixed; whenever they mention Meta-Harness, harness optimization, scaffold evolution, automatic prompt/memory optimization, an evolutionary or Pareto search over candidate implementations, or "make the harness/agent better without retraining"; and whenever the gain must come from the code AROUND the model rather than the model weights. Reproduces the Meta-Harness paper's method natively, with no claude_wrapper.py and no metered solver API.
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。
TDD-based autonomous development loop with checkpoint recovery and observability changelog
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。
Self-Evolving Development Loop - Dynamic skill generation with learning and evolution
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。
Pull Request をグリーンにするスキル。CI チェックの失敗解消と、PR に紐づく Issue の 修正・クローズを一括で実行する。GitHub Actions の CI ログ分析、ローカル CI （`./scripts/ci-check.sh`）の実行、エラーの根本原因特定と修正、PR コメントや リンクされた Issue の解消を含む。解消した Issue は確認後にクローズする。 ループモード（`/loop 5 /pr-green`）にも対応し、自律的に CI 監視・修正・プッシュを繰り返す。 トリガー：「PR をグリーンにして」「CI を通して」「CI エラーを直して」 「PR の issue を解消して」「PR を修正して」「CI が落ちている」「テストが失敗している」 「PR のレビュー指摘を直して」「PR を mergeable にして」 といった PR 修正・CI 解消関連リクエストで起動。PR 番号が指定されなくても、 現在のブランチから自動検出して対応する。
「Tell, Don't Ask」原則に基づくコードレビューと設計支援。オブジェクトの状態を問い合わせて 外部で判断するパターンを、オブジェクトに直接命じるパターンに変換する。カプセル化を強化し、 責任をデータを持つオブジェクトに集約する設計を促進する。コードレビュー、新規実装、 リファクタリング時にgetterの乱用やFeature Envyの改善が必要な場合に使用。 対象言語: Java, Kotlin, Scala, TypeScript, Python, Ruby, Go, Rust。 トリガー：「getterを減らしたい」「カプセル化を改善して」「Feature Envyを直して」 「オブジェクトに責任を持たせたい」「デメテルの法則」といったOOP設計関連リクエストで起動。
Act as a project-management copilot (PM/PMO) for the delivery loop: charters, plans and schedules, estimates, risk registers, status reports, scope changes, stakeholder communication, meeting actions, lessons learned, and portfolio roll-ups, in a per-project `_project/` workspace. Use whenever the user wants to start, plan, track, rescue, or report on a project: "kick off this project", "make a project plan / timeline / WBS", "what date can I commit to", "build a risk register", "run a pre-mortem", "write the weekly status report", "the deadline slipped, what now", "turn this meeting into action items", "review my project portfolio", in any language ("quản lý dự án", "gestion de projet", "プロジェクト管理", "项目管理"), even when they never say "project management". Also use whenever a `_project/` directory exists and the user asks about schedule, deadline, risk, status, scope, budget, or stakeholders. Not for what to build (product-manager) or company-level bets (strategy-board).
Expert guide for AI-assisted coding workflows — agentic code generation, multi-agent code swarms, self-healing CI/CD, automated PR review, spec-to-code pipelines, codebase knowledge graphs, and human-in-the-loop approval gates / Panduan ahli untuk workflow pengkodean berbasis AI — generasi kode agentic, code swarm multi-agen, CI/CD self-healing, review PR otomatis, pipeline spec-to-code, knowledge graph codebase, dan gate persetujuan human-in-the-loop.
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。
Classical control and system dynamics for engineered systems — feedback and closed-loop control, transfer functions and block diagrams, poles/zeros and stability, time-domain response (rise time, overshoot, settling time), PID controllers and tuning, and frequency response (Bode plots, gain and phase margin). Traces feedback control from the flyball governor to modern controllers. Use when analyzing feedback loops, designing or tuning a PID controller, assessing stability from poles or margins, or characterizing transient response of a dynamic system.
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。
Knowledge reference for Autonomous Refinement Loop research — pattern research into prerequisites for autonomous execution without synchronous human blocking gates. Defines failure categories, prerequisites, and conditions for replacing human judgment with machine-verifiable checks. Use when designing or evaluating autonomous agent loops, gate conditions, or HOOTL execution patterns.
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。
Patterns and architectures for autonomous Claude Code loops — from simple sequential pipelines to RFC-driven multi-agent DAG systems.
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。
Patterns for continuous autonomous agent loops with quality gates, evals, and recovery controls.
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。
Многотуровый диалог писателя (Claude) с одним или несколькими напарниками (любой набор из kimi/codex/hermes/claude-headless) по задаче пилота (DP.SC.154). Ведёт turn-loop (2 участника) или round-loop (3+, WP-509), обнаруживает CONSENSUS/ESCALATE, после консенсуса — Decision Gate (зафиксировать vs реализовать → ревью → проверить → задеплоить), синтезирует report.md через Agent tool.
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。
Design and operate multi-agent orchestration patterns (ReAct loops, evaluator-optimizer, orchestrator-workers, tool routing) for LLM systems. Use when building or debugging agent workflows, tool-use loops, or multi-step task delegation; triggers: agentic, multi-agent, orchestration, ReAct, evaluator-optimizer, tool-use, handoff.
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。
AI agent workflow patterns including ReAct agents, multi-agent systems, loop control, tool orchestration, and autonomous agent architectures. Use when building AI agents, implementing workflows, creating autonomous systems, or when user mentions agents, workflows, ReAct, multi-step reasoning, loop control, agent orchestration, or autonomous AI.
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。
Batteries-included agent component for React/Next.js from ui.inference.sh. One component with runtime, tools, streaming, approvals, and widgets built in. Capabilities: drop-in agent, human-in-the-loop, client-side tools, form filling. Use for: building AI chat interfaces, agentic UIs, SaaS copilots, assistants. Triggers: agent component, agent ui, chat agent, shadcn agent, react agent," agentic ui, ai assistant ui, copilot ui, inference ui, human in the loop
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。