「implement」の検索結果

Deep agentic-flow@alpha integration implementing ADR-001. Eliminates 10,000+ duplicate lines by building claude-flow as specialized extension rather than parallel implementation.

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Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive 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。

Verify implementation matches OpenSpec change artifacts. Use when the user wants to validate that implementation is complete, correct, and coherent before archiving. Also use when the user says "openspec verify" or "opsx verify".

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。

On an explicit planning request, always call `read_skill` for this skill before answering. Research first, then return one concise inline plan; do not create a plan file unless the user explicitly asks or a governing workflow requires one. Start and end the reply by saying this is not a special mode and `go` executes the plan; do not implement in the planning turn. For ordinary implement, build, fix, debug, or refactor requests, work directly. When an explicit plan divides work into separate diffs, commits, or PRs and the user asks to publish, preserve that split and tell the user before deviating.

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Clarify a material user-owned product decision before substantive implementation commitments. Use only when request and named/governing evidence still leave a choice that would materially change scope, platform support, user-facing surface, data/API contract, domain values, project placement, or cross-system architecture; also use when a truncated request omits such a choice. On the initial turn, inspect only named or governing requirements evidence, then ask directly without loading: prefer structured input; ask one blocking outcome question or at most three coupled questions; wait. After the answer, load the body before write or delegation. Do not use for implement/build/fix/debug/refactor work when expected behavior is settled, plan/design deliverables owned by Plan, complexity/greenfield/integration alone, agent-owned technical choices, locally discoverable facts, reversible defaults, permission to begin, or stop/no-tool turns. Never re-ask supplied facts or turn clarification into refusal.

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Deliver a runnable local browser app so the user can operate it on the first handoff. This delivery skill does not choose whether a requested local system should be a browser app. On a current turn that explicitly says to implement a brand-new browser project now, load bundled:greenfield-project-scaffolding before project work only after no-peek or permitted top-level placement facts prove that no named or suitable current-folder target resolves the root; never hand off from planning or answer-only turns, existing work, a named or suitable target, or standalone or single-file delivery. Resolve user-blocking product or interface choices with an available interaction surface; resolving those choices alone never authorizes implementation. Always call read_skill for bundled:browser-app-delivery before creating or materially changing a local browser app that owns its start path or a playable browser game. Also load it when the user requests a paste-ready, single-file, offline, or immediately playable browser artifact, even if no local start path should remain, unless the user says they will open or check it themselves or explicitly declines verification. On completed local-server delivery, include exact start commands, a concrete local URL, and explicit browser-open wording. On completed standalone delivery, hand off the exact artifact path or paste URL and smoke result without inventing a server. Do not load it for a self-contained static file that has no owned start path if the user either says they will open or check it themselves or explicitly declines verification. Do not load it for component/style-only edits, deployed sites, backend/API-only work, browser QA, review, explanation, plan-only, or explicit stop/no-tool turns.

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Inspect Figma designs and generate implementation context for screens and interfaces. Use to read component variants, spacing and design tokens, inspect layouts and design-system libraries, and extract image assets for implementing a design through Figma's official MCP server.

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Trigger when: (1) User wants to create an educational/explainer video, (2) User has a vague concept they want visualized, (3) User mentions "3b1b style" or "explain like 3Blue1Brown", (4) User wants to plan a Manim video or animation sequence, (5) User asks to "compose" or "plan" a math/science visualization. Transforms vague video ideas into detailed scene-by-scene plans (scenes.md). Conducts research, asks clarifying questions about audience/scope/focus, and outputs comprehensive scene specifications ready for implementation with ManimCE or ManimGL. Use this BEFORE writing any Manim code. This skill plans the video; use manimce-best-practices or manimgl-best-practices for implementation.

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Contributing to the Twenty codebase itself (twentyhq/twenty server internals), not for building apps on top of Twenty. Implement action handlers for executing workspace migrations in Twenty. Use when creating database operations for syncable entities, implementing universal-to-flat entity transpilation, or handling create/update/delete actions in the runner layer.

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When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program," or "experiment playbook." Use this whenever someone is comparing two approaches and wants to measure which performs better, or when they want to build a systematic experimentation practice. For tracking implementation, see analytics. For page-level conversion optimization, see cro.

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Use when you need a deep, implementation-ready engineering plan for a code change — built from GitNexus graph intelligence, statement-level PDG analysis, and targeted source verification, compact enough that an implementation agent can start without re-investigating. Also strengthens existing plans via Deepen mode. Examples: "/gitnexus-plan Add retry support to the ingestion pipeline", "/gitnexus-plan deepen docs/plans/<plan>.md", "plan this change using the knowledge graph".

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Implement centralized audit logging and SIEM integration. Configure log retention and security monitoring. Use when implementing audit trail requirements.

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Implement or review authentication and authorization with explicit token, session and resource-access boundaries.

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Generate verified recipe playbooks from AIPs with PR implementations (post mode), or speculative user stories from AIPs without implementations (pre mode). Use when the user provides an AIP URL or AIP content, optionally with PR URLs and file paths.

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Design and implement microinteractions, motion design, transitions, and user feedback patterns. Use when adding polish to UI interactions, implementing loading states, or creating delightful user experiences.

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Implement modern responsive layouts using container queries, fluid typography, CSS Grid, and mobile-first breakpoint strategies. Use when building adaptive interfaces, implementing fluid layouts, or creating component-level responsive behavior.

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Implement WCAG 2.2 compliant interfaces with mobile accessibility, inclusive design patterns, and assistive technology support. Use when auditing accessibility, implementing ARIA patterns, building for screen readers, or ensuring inclusive user experiences.

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。

Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.

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Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. Use when writing Python tests, setting up test suites, or implementing testing best practices.

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Implement distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks. Use when debugging microservices, analyzing request flows, or implementing observability for distributed systems.

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Implement PCI DSS compliance requirements for secure handling of payment card data and payment systems. Use when securing payment processing, achieving PCI compliance, or implementing payment card security measures.

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Implement Stripe payment processing for robust, PCI-compliant payment flows including checkout, subscriptions, and webhooks. Use when integrating Stripe payments, building subscription systems, or implementing secure checkout flows.

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Implement Kubernetes security policies including NetworkPolicy, PodSecurityPolicy, and RBAC for production-grade security. Use when securing Kubernetes clusters, implementing network isolation, or enforcing pod security standards.

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Implement comprehensive testing strategies using Jest, Vitest, and Testing Library for unit tests, integration tests, and end-to-end testing with mocking, fixtures, and test-driven development. Use when writing JavaScript/TypeScript tests, setting up test infrastructure, or implementing TDD/BDD workflows.

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。