「implementation」の検索結果

Enforce traceability from natural-language intent to machine predicates, final observables, evidence, and owner-readable acceptance. Use when conducting any audit, review, QA pass, parity or compliance check, security assessment, code or architecture review, visual or behavioral comparison, data validation, system verification, completion gate, or claim that an implementation satisfies a human request.

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 the user asks to verify a story or ticket implementation against its spec - per-AC verdicts, code reuse and design conformance, dead code, then adversarial maintainer verification. Requires the qa/review agent roles registered by scripts/install-codex.sh. Keywords: story QA, acceptance criteria, spec conformance, AC coverage, QA this branch, verify story.

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 the user asks for a phase-gated refactoring or redesign loop - session-blind workers map and trace the area, empirical probes precede the design, an approval gate guards implementation, and a conformance audit re-checks the branch after any design change. State persists in .episteme/DESIGN-<slug>.md. Requires the refactor agent roles registered by scripts/install-codex.sh. Keywords: refactor loop, redesign, design gate, enumerate before design, conformance audit.

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 verifying a story or ticket implementation against its spec and acceptance criteria - per-AC verdicts with file:line evidence and proving tests, code reuse and design conformance, dead code, stale docs, comment discipline. Finds the spec from an explicit path, the ticket key in the branch name, or plan files - and asks rather than guessing. For defect-hunting code review use dotnet-techne-code-review instead. Keywords: story QA, acceptance criteria, spec conformance, AC coverage, definition of done, dead code check, reuse check, comment check, verify story, QA this branch.

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。

Sync reusable frontend decisions into persistent project documentation after UI work. Use when frontend changes introduce or modify component APIs, variants, design-system tokens, styling conventions, layout behavior, accessibility rules, interaction patterns, responsive behavior, or agent operating instructions; when the user asks to reflect decisions in components.md, design system docs, AGENTS.md, CLAUDE.md, GEMINI.md, or similar project memory files; or before finalizing frontend work where future consistency depends on decisions made during 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。

Use when adding auth, webhooks, uploads, queues, secrets, tenant boundaries, or public endpoints — produces trust boundaries + abuse cases mapped to files, BEFORE 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。

Security audit — vulnerability scan, pentest review, attack-surface sweep; explicit request only, not regular feature work. Pre-implementation threat pass → threat-modeling.

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 establishing what observability a project actually has — the four Golden Signals, required signal to detected implementation to evidence, and SLI/SLO provenance.

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 the feature is implementation-complete and the next step is 'ship it' — verifies, cleans up, routes to merge/PR/park/discard; never destroys work without explicit confirmation.

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。

Blind dual-implementation pattern where two agents independently create artifacts, then a third agent judges which is better and synthesizes learnings.

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。

Padrões de Retrieval-Augmented Generation incluindo chunking, embeddings, vector stores e otimização de recuperação. Use quando: rag, retrieval augmented, vector search, embeddings, semantic search.

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。

Implementa e treina LLMs usando LitGPT da Lightning AI com 20+ arquiteturas pré-treinadas (Llama, Gemma, Phi, Qwen, Mistral). Use quando precisar de implementações limpas de modelos, entendimento educacional de arquiteturas ou fine-tuning de produção com LoRA/QLoRA. Implementações em arquivo único, sem camadas de abstração.

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。

Apply the Opquast Digital Quality Framework (245 rules, 14 categories) when building, reviewing, or auditing websites and web applications. Use for web development quality assurance, accessibility compliance, security hardening, privacy implementation, e-commerce best practices, and holistic digital quality checks.

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。

Central entry point of the afonsoft agent harness. Use when starting a new project, resuming an existing one, planning features/Epics/releases, or running any multi-step agent-driven work. Validates and reconciles SPECs (SDD), audits the codebase and harness for gaps (security, architecture, performance, hygiene), proposes improvements, fragments work into GitHub Issues, delegates implementation/QA/review to specialized skills, and re-validates everything until delivery. Also use to review unapproved SPECs, reconcile open GitHub Issues with code, or run a final gap check before closing a release.

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 analyzing SonarQube issues and turning them into SPEC SDDs and tracked GitHub Issues for TDD 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。

Use when the user needs to create or refine a feature SPEC SDD before 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。

Use when raising code quality or test coverage across a .NET, Java, or Python repository.

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。

Critically analyze whether a product, repo, skill, plugin, harness, or architecture vision still fits the real application, user intent, implementation, evidence, and likely future direction. Use when asked to assess vision vs application, predict future fit, critique roadmap direction, analyze strategic intent, or decide whether to keep, refine, pivot, or kill a 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。

Explore competing approaches through independent agents before selecting an 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。

Produce a self-contained HTML implementation plan for explicit approval before coding.

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。

Re-analyze an existing GitHub Issue using its current title and body as input, refresh the implementation plan against the latest code state, and update the Issue in place. Use this when the user provides an Issue number (numeric, `#`-prefixed, or Issue URL) and wants to regenerate the Explore-based analysis. For reflecting comment-driven updates instead, use update-issue. For creating a brand-new Issue from a natural-language task description, use create-issue.

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。

INTERNAL/HELPER skill — do NOT invoke directly from a user query. This is the shared formatter/poster used by create-issue, create-issue-from-issue-number, and update-issue. It formats an implementation-ready GitHub Issue body, runs the pre-posting checklist, executes `gh issue create` or `gh issue edit`, and optionally posts a 確認事項 follow-up comment. Invoke this skill ONLY from one of the three parent skills via the Skill tool, after the parent has completed analysis. If a user asks to 'format an issue body' or similar, route them to the appropriate parent skill (/create-issue, /create-issue-from-issue-number, or /update-issue) rather than invoking this one directly.

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 implementing any feature or bugfix, before writing implementation code

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 starting feature work that needs isolation from current workspace or before executing implementation plans - ensures an isolated workspace exists via native tools or git worktree fallback

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。