「implementation」の検索結果

Use when a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.

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Implements an approved spec. Validates that the state means "Approved" (in any language), creates a git branch named after the spec, switches to it, and starts the implementation step by step with pauses to review diffs.

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。

Break the PRD into sequenced implementation RFCs under RFCs/ with an RFCS.md index, then cold-read each RFC for gaps.

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。

Scaffold or standardize a production-ready repository structure with specs, source layout, tests, CI, agent context, config examples, release notes, and operational docs. Use when starting a new repo, turning a prototype into a maintainable project, adding missing repository foundations, or creating a repo skeleton before implementation. For AGENTS.md-only context scaffolding, use agentsmd-scaffold.

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Use before an agent-produced diff is committed, pushed, opened as a PR, merged, landed, or applied to user files when explicit implementation approval is missing. Trigger for review gate, review pack, approve before landing, diff first then land, human approval, merge gate, commit gate, push gate, or PR readiness. Produces a concise review pack, records risks and verification, and blocks landing until a human explicitly approves or approves with required fixes.

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Threat-model product features, APIs, data flows, secrets, permissions, supply-chain changes, auth boundaries, and risky code paths before or during implementation. Use when touching authentication, authorization, payments, secrets, user data, uploads, webhooks, admin tools, innerHTML/eval/exec, dependency upgrades, or cross-tenant access.

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Extract design systems from reference UI images and generate implementation-ready UI design prompts. Use when users provide UI screenshots/mockups and want to create consistent designs, generate design systems, or build MVP UIs matching reference aesthetics.

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Route substantial coding or repository-review work through a two-stage Sol commander/verifier and separate Luna Max CLI worker. Use when the user explicitly asks Sol to direct or supervise Luna, explicitly requests this router, needs an isolated auditable Luna-owned implementation with a Sol review loop, or asks to diagnose or measure the router itself. Do not use merely because a task involves coding, verification, progress reporting, or token saving. For small or straightforward tasks, keep work in Sol unless the user explicitly requests Luna; router configuration, usage, and efficiency questions should use read-only inspection without launching a worker.

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Route full software-development architecture work from product intent through design, implementation, testing, release, and operations. Use when the user asks for a complete development architecture, wants to know which Spellbook skills to combine, needs an execution path across PRD/spec/API/data/security/performance/release/SRE, or asks to turn an idea or repo into a production-ready engineering plan.

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Design architecture foundations before implementation. Use when asked to design or refactor architecture, choose Rust/Go crate, package, module, runtime, workflow, or service boundaries, compare mature project architecture, prevent stacked one-off PRs, audit migration debt in existing repos, or produce a spec before coding.

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Use when you want a second-opinion review via Codex CLI, cross-verification after another agent implements changes, debugging help, or alternative implementation proposals. Requires Codex CLI to be installed and authenticated.

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Mandatory audit workflow for UI/UX changes that reads current state FIRST, checks for redundancy, respects clean design philosophy, and identifies genuine gaps before implementation. Auto-invoked when user mentions UI, UX, design, layout, homepage, page improvements, visual changes, or interface modifications.

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 after research (Z01 files exist) to create implementation plan - follow structured workflow

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Investigate a GitHub issue and produce a structured implementation plan. Fetches issue details, explores the codebase, identifies affected layers (UI, BLoC, Repository, Client), and outputs a step-by-step plan with files to modify, testing strategy, and risk assessment. Works for bugs, features, and tasks. Invoke with /plan.

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Fix NIP-98 HTTP authentication 401 errors caused by URL mismatch between the `u` tag and what the server expects. Use when: (1) NIP-98 auth returns 401 with "URL mismatch" error, (2) Token creation succeeds but server rejects authentication, (3) Server logs show expected vs actual URL difference. CRITICAL: Server implementations vary - some strip query params, some don't. Check the actual error message to determine which behavior applies.

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Review an openai-guardrails-python release plan or final release candidate against the previous remote tag, determine the compatible release type, audit runtime and packaging risk, inspect current CI, and produce an English ship-or-block report. Use for pre-release readiness checks, not ordinary PR review or implementation.

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Write detailed feature specifications with functional requirements, edge cases, data models, API contracts, and UX flows. Create comprehensive technical specifications that enable clear implementation.

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Use when implementing any feature or bugfix, before writing implementation code

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Build business cases with cost-benefit analysis, ROI calculations, risk assessment, implementation timeline, and stakeholder analysis

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You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

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Use when Code implementation and refactoring, architecturing or designing systems, process and workflow improvements, error handling and validation. Provide tehniquest to avoid over-engineering and apply iterative improvements.

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Automatically generate TLA+ specifications from source code (C/C++, Python) for formal verification of distributed systems. Use when users need to: (1) Generate TLA+ specs from program implementations, (2) Model distributed systems, consensus protocols, or concurrent algorithms, (3) Extract state variables, actions, and invariants from code, (4) Create formal specifications for model checking with TLC, (5) Verify safety and liveness properties of distributed systems. Particularly effective for message-passing systems, replication protocols, consensus algorithms, and distributed transactions.

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Interview-based strategic planning for complex software tasks. Conducts structured requirements gathering, gap analysis, and generates detailed work plans before implementation begins.

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Generates comprehensive test scenarios from requirements including BDD/Gherkin scenarios, unit tests, integration tests, and end-to-end test cases. Use when converting requirements, user stories, or specifications into testable scenarios with full coverage including happy paths, error cases, edge cases, and boundary conditions. Outputs structured test suites ready for implementation.

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。