「defi」の検索結果

Guides the creation of technical design documents before writing code, producing architecture diagrams, data models, API interface definitions, implementation plans, and multi-option trade-off analyses. Use when the user asks to plan a feature, architect a system, design an API, explore implementation approaches, or requests a technical design or spec before coding — especially for complex features involving multiple components, ambiguous requirements, or significant architectural changes.

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。

Creates and structures SKILL.md files for AI coding agents, including YAML frontmatter, trigger phrases, directive instructions, decision trees, code examples, and verification checklists. Use when the user asks to write a new skill, create a skill file, author agent capabilities, generate skill documentation, or define a skill template for Claude Code agents.

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。

Turn an approved measurement charter, ICPs, and buyer jobs into a budget-aware prompt coverage blueprint across proximity bands, aided status, information acts, journey states, roles, locales, evidence grades, partitions, and measurement lanes. Use before prompt wording to define required, optional, and prohibited canonical intent cells.

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Requirements authoring guide for BRD and PRD across Discover, Define, and Govern with canonical templates and handoff contracts

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Create or assess an evidence-grounded, falsifiable outcome hypothesis: a testable prediction of what measurable business result will change, for whom, by when, and how leading and lagging indicators will prove or disprove it. Use when framing measurable outcomes, turning an MVP, POC, feature, or technical initiative into a beneficiary result, defining targets and indicators, or judging whether evidence is strong enough to invest. Also applies to business outcome hypotheses, value hypotheses, and outcome statements.

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 data-driven Godot 4.7 games with custom Resource classes: define typed data with class_name + @export, save/load .tres/.res files, instance and duplicate resources, and load on demand with ResourceLoader (incl. threaded loading). Use when modeling items/stats/configs as data in a Godot project, creating .tres resources, or working with custom Resource subclasses and ResourceLoader/ResourceSaver.

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Structure a Bevy app around its Entity Component System: build the App with plugins, define Component/Resource types, write systems with Query/Res/Commands, filter and order systems, and use the Time resource for frame-rate-independent motion. Use when building or debugging a Bevy game in Rust — when the user mentions Bevy, ECS, App::new, add_systems, Query, Commands, components/systems, or a Cargo.toml depending on bevy.

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。

Export and build a Godot 4.7 project for distribution: install export templates, define export presets (Windows/macOS/Linux/Web/Android), run headless command-line exports for CI, and handle web (HTML5) COOP/COEP and dedicated-server/headless builds. Use when exporting a Godot game, configuring export_presets.cfg, building for web/desktop/mobile, or automating builds from the command line.

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。

Build networked games with Godot 4.7 high-level multiplayer: set up an ENetMultiplayerPeer server/client, define RPCs with the @rpc annotation (call via rpc()/rpc_id()), set per-node multiplayer authority, and replicate state with MultiplayerSpawner and MultiplayerSynchronizer. Use when adding multiplayer/networking to a Godot project, writing @rpc functions, or syncing player/world state across peers.

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Consolidates redundant documentation while preserving all valuable content; keeps a doc set truthful after things change. Modes: (1) POST-CHANGE GOVERNANCE — when code, scripts, config, env vars, ports, paths, deployment, auth, tests, or a documented procedure changed; also "which docs are now wrong", "check docs for inconsistencies", "过时的命令/路径", "文档同步". Use it even when the user mentions only the change and never the docs — updating them is part of the change, not optional cleanup. (2) CONSOLIDATION — "clean up docs", "documentation bloat", "reduce documentation sprawl", "consolidate documentation", "too many doc files", "merge redundant docs", "merge these docs", "文档太多/太乱", multiple files covering the same topic, or documentation exceeding 500 lines across multiple files on similar topics. Finds stale copies of a fact changed in only one place, values that should never have been persisted (counts, totals, "N items", restated summaries), rival definitions that drift apart, and orphaned cross-references.

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Switches or repairs an OpenClaw instance's model config: changes the default model, adds model definitions, and fixes 401 "Invalid token", "No available channel / model not found", "Thinking level X is not supported", or a config edit that doesn't take effect. Use for 切换模型/换模型/升级模型 or 模型配的错了. Not for plain config audits (use openclaw).

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Review what an LLM feature or agent actually puts in its context window — and find what's bloating, missing, or fighting itself. Use when asked to review a system prompt and context assembly, cut token usage without losing quality, debug an agent that ignores instructions, or audit how retrieval results, history, and tool definitions are packed into the window. Produces a context inventory with a keep/cut/restructure verdict per component, ordering and caching fixes, and a token budget. For wording-level prompt tuning use prompt-optimizer.

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。

Structure a cohort analysis for retention, LTV, or behavioural patterns. Use when asked to run a cohort analysis, analyse retention by cohort, segment users by behaviour over time, or calculate lifetime value by acquisition period. Produces a complete cohort analysis framework with methodology, cohort definitions, retention curves, and prioritised interventions.

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Build a community management playbook for a brand's social media channels. Use when asked to create guidelines for managing comments, DMs, and community interactions, define a moderation policy, or build response frameworks for social media community managers. Produces a complete playbook with response templates, escalation paths, moderation rules, and tone guidelines.

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Work out whether you're eligible for a class-action settlement or refund program — and actually file the claim before the deadline. Use when asked am I owed money from a settlement, is there a class action for [product/company], how do I claim a settlement, or did I qualify for that refund. Produces a way to check for relevant settlements, an eligibility read against the class definition, the proof you need and how to file, deadline tracking, and honest expectations on payout size — plus how to avoid fake-settlement scams. Not legal advice.

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Produce a capacity planning document for a service covering traffic forecasts, resource requirements, and scaling strategy. Use when asked to plan infrastructure capacity, forecast resource needs, model traffic growth, define scaling strategy, or produce a capacity review for a service. Produces a structured capacity plan covering current baseline metrics, growth projections, resource requirements per tier, scaling strategy, cost projections, capacity triggers, and an infrastructure action roadmap.

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 an API versioning strategy document for a service or API platform. Use when asked to define versioning policy, plan API deprecation, classify breaking changes, or document version lifecycle. Produces a complete versioning strategy with breaking-change classification table, deprecation timeline, migration guide template, and client communication template.

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Structure AI and ML product decisions with the rigour of any product decision. Use when building AI-powered features, evaluating LLM integrations, designing AI products, or assessing AI readiness. Produces a complete AI product canvas covering problem definition, model approach, data requirements, evaluation framework, UX design, responsible AI checklist, and launch monitoring plan.

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。

Specify an autonomous or tool-using AI agent before building it. Use when asked to design an AI agent, define an agent's tools and guardrails, scope what an agent is allowed to do, or write an agent spec/PRD. Produces an agent spec — goal & scope, tools with permissions, the control loop, guardrails & approval gates, memory, escalation/handoff, evaluation, and failure handling.

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Design statistically rigorous A/B tests for product features, UI changes, onboarding flows, and pricing experiments. Use when asked to set up an experiment, design an A/B test, calculate sample size, or interpret test results. Produces a complete test plan with hypothesis, variant definitions, sample size, duration estimate, guardrail metrics, and a results interpretation guide.

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Structure vague opportunities and unclear briefs into actionable one-page problem statements. Use when asked to clarify a vague brief, frame an undefined problem, make sense of an unclear opportunity, or when the user says 'we need to figure out what to do about X' or 'I've been asked to look into Y'. Produces a structured problem brief with reframed questions, scoped boundaries, and a minimum viable research plan.

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 this methodology when collaboratively shaping a solution with the user - iterating on problem definition (requirements) and solution options (shapes).

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 learning paths for programming tools, and define what information should be researched to create learning guides. Use when user asks to learn, understand, or get started with any programming tool, library, or framework.

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。

Help users define the strategic context for their product by identifying the right competitive alternatives, unique value drivers, and target segments to win a market category.

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。