'coding-agent' 검색 결과

ScalarDBの制約を考慮したデータモデル設計を行います。Partition Key・Clustering Key・Secondary Index の設計、ホットスポット評価、メタデータオーバーヘッド見積もり、バックエンドDB選定を含みます。ワークフローStep 04（データモデル設計）で使用します。

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ScalarDBのトランザクション設計を行います。トランザクション境界定義、パターン選定（単一集約内/2PC/Saga/ハイブリッド）、OCC競合率評価、バッチ処理設計、v3.17最適化適用計画を含みます。ワークフローStep 05（トランザクション設計）で使用します。

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ScalarDBを前提としたデータベース設計を行います。スキーマ設計、トランザクション設計、マルチストレージ構成を対話形式で決定し、スキーマファイルと設計書を生成します。

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RESTful API の設計を行います。エンドポイント定義、リクエスト/レスポンス仕様、エラーハンドリング、ScalarDBトランザクション例外のマッピングを含みます。

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DDDに基づいたドメインモデルを設計します。戦略的設計（境界コンテキスト、ユビキタス言語）と戦術的設計（エンティティ、値オブジェクト、集約、ドメインサービス、ドメインイベント）を定義し、ヘキサゴナルアーキテクチャとの統合を行います。ScalarDBのトランザクション境界や管理テーブル分類の考慮事項を含みます。中間状態はresearch/に記録されます。

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Kubernetes、Kong API Gateway、ScalarDB Cluster v3.17を使用したインフラストラクチャ設計を行います。マニフェストファイルと設定ファイルを生成します。

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設計ドキュメントに基づいて実装計画を生成します。ScalarDB特有のスキーマ定義、トランザクション実装、統合テストを含む実装可能なタスク指示書を作成し、フェーズ別の実装ロードマップを提供します。

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Creates and maintains concise AGENTS.md and CLAUDE.md project instruction files. Use when asked to create AGENTS.md, update AGENTS.md, maintain agent docs, set up CLAUDE.md, document repository agent conventions, or keep coding-agent instructions minimal and reference-backed.

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Keep GPT-5.6 and other coding-agent work proportional to the request by locking scope, choosing the smallest sufficient implementation, matching verification to risk, and stopping when success is proven. Use for implementations, bug fixes, refactors, reviews, or maintenance where scope creep, speculative abstractions, unrelated cleanup, repeated test runs, unnecessary dependencies, or polishing beyond the request are risks.

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Provider account and authenticator setup through Matrx Vault. Use when creating a service account, saving its login, enrolling 2FA, or verifying agent sign-in with Vault credentials.

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Ask which skill or flow fits your situation. A router over the user-invoked skills in this repo.

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Reconstruct workflow behind a past coding-agent artifact using local ax sessions/commits/skills/tool traces. Use when asked how X was built.

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Route agent work through AccInt's MCP memory loop: retrieve prior outcomes, resolve frames, and close commitments with evidence.

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Audit local AI coding-agent sessions with agenttrace for cost, tool failures, latency, anomalies, health, diffs, and CI gates.

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The PRIMARY development workflow for the Archon project (remote-coding-agent). Use this skill instead of any PRP skills when working on Archon code. Routes to 10 specialized cookbooks based on what the user is trying to do: RESEARCH — "how does the orchestrator work?", "where is session state defined?", "trace the workflow execution flow", "what is IWorkflowStore?" INVESTIGATE — "should we use Drizzle or Prisma?", "what's the best way to add WebSockets?", "can we migrate to Turso?", "how do other projects handle rate limiting?" PRD — "write a PRD for dark mode", "spec out the notification feature", "product requirements for webhook retry" PLAN — "plan the auth refactor", "design the caching layer", "create an implementation plan for #42" IMPLEMENT — "implement the plan", "execute .claude/archon/plans/auth.plan.md", "build the feature from the plan", "code this up" REVIEW — "review PR #123", "review my changes", "code review the diff" DEBUG — "debug the failing test", "why is streaming broken?", "root cause analysis on the timeout issue" COMMIT — "commit these changes", "commit the auth refactor" PR — "create a PR", "open a pull request for this branch" ISSUE — "report this to gh", "create a gh issue", "log it in github", "file a bug for this", "create a feature request" This skill triggers on ANY development task: researching, investigating, planning, building, reviewing, debugging, committing, or shipping code. NOT for: Running Archon CLI workflows in worktrees (use /archon instead).

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Run a structured retrospective after development-phase runs of your product's agents — interview the owner in plain language about what went well and poorly, draft ranked improvement hypotheses, then design and run small n=1/n=2 experiments with pre-declared success criteria, guardrails, stop conditions, and a cost/ROI kill-switch. Load when the user says how did that run go, retro this run, the agent output was bad, what should we improve, draft hypotheses, run a small experiment, or after repeated dev runs of an agentic system produce uneven quality. Priority: output quality over performance over cost, each with diminishing-returns stops. NOT a product A/B test (experimentation), NOT coding-agent harness repair (harness-evolution), NOT production-scale learning (runtime-learning-loop).

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Design a normalized, machine-readable "work receipt" that answers what changed, why, by whom, and with what validation, for any AI-coding-agent task regardless of backend (Claude Code, Codex, Cursor, Aider, CI, or homegrown). Use when defining a receipt schema, normalizing heterogeneous agent logs into one evidence shape, gating PR merges on artifact-backed proof, or designing an OSS maintainer's fast-reject/fast-merge workflow. NOT for validating a DAG node's output against its own schema (use output-contract-enforcer), continuous runtime monitoring of a live coordination daemon (use runtime-verification-for-agents), or general Port Daddy claims/notes/salvage mechanics (use port-daddy-agent-skill).

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Configures AI agents (GitHub Copilot, Claude Code, Cursor, Codex) to understand a codebase and self-improve as the project evolves. Use when setting up a new project for AI-assisted development, onboarding AI agents to an existing repo, creating AGENTS.md, configuring MCP servers for code navigation, packaging project workflows as agent skills, or establishing self-improvement feedback loops. Covers AGENTS.md authoring, skill packaging, MCP configuration, context management, and living-documentation strategies.

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Delegate coding to OpenHands CLI (model-agnostic, LiteLLM).

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Delegate coding to xAI Grok Build CLI (features, PRs).

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Operate the Antigravity CLI (agy): plugins, auth, sandbox.

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Delegate coding tasks to Blackbox AI CLI agent. Multi-model agent with built-in judge that runs tasks through multiple LLMs and picks the best result. Requires the blackbox CLI and a Blackbox AI API key.

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Delegate coding to OpenAI Codex CLI (features, PRs).

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Delegate coding to OpenCode CLI (features, PR review).

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