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claude-skills/スキル
SKILLOfficialdevelopment

agent-advisor

プラグイン
migration-to-aws
ソース
GitHub で見る ↗
説明

AWSでのAIエージェント(AI技術を使って自動判断・実行するシステム)作業の統一されたアクセスポイント。ランタイム(実行環境)を評価して選択し、既存のシステム移行の完全な計画を生成し、実行可能なPOC(概念実証)を構築する — すべてが1つのフローで完結します。 **以下のような場合に使用:** - 自分のエージェントに適したランタイムは何か - AgentCore対ECS対EKS対Lambda - AgentCore対Lambda MicroVMs - AWSにAIエージェントをデプロイする - AWSでのエージェントアーキテクチャ(設計構成) - エージェントのアイデアはあるが何を構築すればいいか分からない - エージェントをAWSに移行する - エージェントをAWSに移行する際に計画が必要 - エージェント移行計画 - AgentCoreサービスを追加する - メモリ・ゲートウェイ・認証・ポリシーをエージェントに追加する - AgentCore Memoryを有効にする - エージェントに監視機能を追加する - 既にAWSを使用していて、エージェント機能を追加したい - Temporal(スケジュール・ワークフロー管理ツール)のワーカーをAWSに移行する - Temporal to AWS - TemporalをAWSで実行する - 既存のTemporal関連システムをAWSに移す **処理の流れ:** 段階的なプロセスを実行します:入力受け取り(背景の確認)→ 軽量なコード検査 → 適応的な質問 → スコア算出 → 設計(ランタイム・デプロイ方法・サービス・モデルを決定)→ コスト概算 → 推奨事項ドキュメント生成。その後、オプションの次段階として:移行計画(このプラグインのGCP-AWS変換エンジンを再利用して完全な計画を生成)、POC(推奨ランタイム上でのデプロイメント計画と実行可能な実装)。 複数のワークロード(相互作用するエージェント、独立したエージェント、バッチ処理、サービス)を持つシステムは個別の単位に分解され、各々が独自の評価を受けます。既にAWSでエージェントを運用中のチーム向けの「機能追加モード」では、どのAgentCoreサービスを有効にすべきかを推奨します。Temporalシステムは同じ単位フローに統合されます — ワーカーのポーリング層とActivity実行クラスが単位になります。ワークフロー制御コードの書き直しは行いません。 **対象外:** - AIエージェント成分が1つもないシステム(純粋なサービス・バッチ処理・HTTPエンドポイントのみ、またはActivityがすべて非エージェント型のTemporalワーカー) - 純粋な計算・データ移行でAIエージェントを含まない場合 - エージェント設計のないLLM(大規模言語モデル)SDK単体の書き換え - モデルごとの詳細な料金比較

原文を表示

Unified entry point for AI-agent work on AWS: evaluate and pick a runtime, generate a full migration plan (for existing workloads), and build an executable POC — all in one flow. Triggers on: which runtime for my agent, AgentCore vs ECS vs EKS vs Lambda, AgentCore vs Lambda MicroVMs, deploy an AI agent on AWS, agent architecture on AWS, I have an agent idea what do I build, move my agents to AWS, migrate my agents to AWS with a plan, agent migration plan, add AgentCore services, add memory/gateway/identity/policy to my agent, enable AgentCore Memory, add observability to my agent, I'm already on AWS and want to add agent capabilities, migrate Temporal workers to AWS, Temporal to AWS, run Temporal on AWS, Temporal workers on AWS, we use Temporal and want to move to AWS, our service is orchestrated by Temporal, what do I build on AWS for my Temporal workers, move a Temporal-based service to AWS, Temporal Cloud or self-hosted on AWS. Runs a phased flow: Intake (entry point + technical background), Discover (lightweight code detection), Clarify (adaptive questions), deterministic scoring, Design (runtime + deployment model + services + model), Estimate (coarse cost), Generate (layered recommendation doc + scaffolding), then optional gated stages: Migration Plan (full plan generated in-skill by reusing this plugin's gcp-to-aws engine, with the advisor's decisions carried over) and POC (deployment plan + deployable proof-of-concept on the recommended runtime — AgentCore, ECS, EKS, or Lambda; generated deliverables by default, or assisted build in your account on explicit opt-in). Systems with several workloads (interacting or independent agents, batch jobs, services) are decomposed into workload units, each getting its own verdict with a consolidation option. An add-capabilities branch (for teams already running agents on AWS) recommends which AgentCore services to enable on any runtime — no runtime scoring. Temporal systems dissolve into the same unit flow — worker polling tiers and Activity execution classes become units (rules in the Temporal decision reference); Workflow orchestration code is never rewritten — never a Step Functions translation. Requires at least one agentic component: a purely non-agent system (only plain services / batch jobs / HTTP endpoints, or a Temporal worker whose Activities are all non-agent) is out of scope — Clarify halts it (scope gate) and points to migration-to-aws / heroku-to-aws / llm-to-bedrock. Not for: pure compute/data migration with no AI agent; pure LLM SDK rewrite without agent architecture (use llm-to-bedrock); or detailed per-model pricing.

ユースケース
  • AWSで実行するランタイムを選択したいとき
  • AIエージェントをAWSにデプロイするとき
  • 既存エージェントをAWSに移行するとき
  • エージェントに機能を追加したいとき
  • TemporalワーカーをAWSに移行するとき
本文

AWS Agent Advisor

Helps startups decide how and where to run AI agents on AWS. Deterministic scoring recommends a runtime; the conversation adapts to the user's technical background.

Definitions

  • "Load" = Read the file with the Read tool and follow it. Do not summarize or skip.
  • $RUN_DIR = the run directory under .agent-advisor/ (e.g. .agent-advisor/0630-1430/), created in Intake.
  • $PLUGIN = ${CLAUDE_PLUGIN_ROOT} (the installed plugin root). On Claude Code this token substitutes inline. If ${CLAUDE_PLUGIN_ROOT} does not resolve (some Cursor/Codex builds, or a literal ${CLAUDE_PLUGIN_ROOT} string showing up in a path error), fall back to the skill's own directory: this SKILL.md lives at <plugin>/skills/agent-advisor/SKILL.md, so the engine and its data are all inside this skill — scripts at ./scripts/..., runtime profiles at ./references/runtimes/..., and decision refs at ./references/decision-refs/... relative to it. Prefer ${CLAUDE_PLUGIN_ROOT}/skills/agent-advisor/...; use the relative fallback only when it fails to resolve.

Prerequisites

  • uv available (for scoring). Check: uv --version. If missing, tell the user to install it from the official install guide (https://docs.astral.sh/uv/getting-started/installation/ — e.g. brew install uv or pipx install uv) and stop.

Phase Structure (frontmatter)

Phase, fragment, and assembler files carry a YAML frontmatter block that declares how each phase is composed — its inputs, triggers, fragments, assembler, artifacts, gates, and ordering. The execution contract is the vendored references/vendored/dsl/INTERPRETER.md: it defines every frontmatter key, the fragment/assembler model, the gate protocol (HANDOFF_OK / GATE_FAIL), and the interpreter loop. Load it first (once, at the start of a run), then execute each phase file's prose body. Elsewhere in this skill, INTERPRETER.md (without a path) refers to this loaded contract.

Execution

This skill is driven by the interpreter loop in INTERPRETER.md (§ The interpreter loop): it reads .phase-status.json, determines the current phase, runs each phase's _preconditions / fragments / _assemble / _postconditions, advances on HANDOFF_OK via _advances_to, and validates state. The backbone (intake → discover → clarify → confirm → design → estimate → generate → migration-plan → poc → complete) and the one sidebar branch (add-capabilities) are derived from the phase files' frontmatter — they are not restated here.

Cold start (entry phase). With no run under .agent-advisor/ carrying a .phase-status.json, begin at references/phases/intake/intake.md — this skill's entry phase (the one carrying _init: true). On a warm start, current_phase in .phase-status.json is authoritative (INTERPRETER.md § The interpreter loop).

Skill bindings (INTERPRETER.md § Skill bindings). This skill declares:

  • Run root: .agent-advisor/ — $RUN_DIR is this skill's name for the run directory (.agent-advisor/[MMDD-HHMM]/). Intake's own prose performs the _init bootstrap.
  • State shape: § State file below (advisor-specific keys such as entry_point, audience, recommendation_reviewed, migration_plan_ctx, migration_plan_unavailable); the shared state schema is not vendored.
  • Run seed (optional): $RUN_DIR/seed.json, else .agent-advisor/seed.json at the run root (schema scripts/schemas/seed.json) supplies machine-readable answers for a non-interactive run — the Clarify dimensions, the two gate answers, the POC mode, the live-probe answer, and a co_recommend tie-break. It is the HIGHEST-precedence source for every value it carries (clarify.md Step 2.5), which is what makes a repeated run's score comparable: the deterministic engine gets byte-identical input. A gate the seed omits is declined; a dimension the seed omits falls through to detection, then prose, then an assumed value that MUST be recorded in $RUN_DIR/UNANSWERED.md. With no seed, the interactive flow is unchanged.
  • Resolved statuses: skipped (routing resolved the phase without running it), plus not_applicable for migration_plan only.
  • Conditional backbone routing: the entry-point routing below. When a routing rule marks a phase not-applicable, set it skipped and advance through its _advances_to in the same state write.

Routing & gates (orchestration)

Sidebar placement and conditional backbone routing are orchestration prose owned by this file (INTERPRETER.md § Skill bindings, § Backbone vs sidebar).

Entry-point routing:

  • build_scratch → skip Discover; Clarify → Confirm → Design → Estimate → Generate → Gate 2 → POC (any winning runtime). No migration plan (nothing existing to migrate).
  • build_deploy → Discover (if code) → Clarify → Confirm → Design → Estimate → Generate → Gate 1 → Migration Plan (if existing non-AWS AI workload detected and user confirms) → Gate 2 → POC (any winning runtime).
  • migrate → Discover (if code) → Clarify → Confirm → Design → Estimate (target-state run cost; migration TCO comparison stays with the Migration Plan engine) → Generate → Gate 1 → Migration Plan (in-skill, reusing the sibling gcp-to-aws skill) → Gate 2 → POC (any winning runtime, when the plan was produced). Declining Gate 1 keeps the classic handoff: pointer to /migration-to-aws:llm-to-bedrock with handoff-summary.md.
  • add_capabilities → load references/phases/add-capabilities/add-capabilities.md and follow it (no runtime scoring; writes capabilities-recommendation.md). This is a self-contained branch — it does NOT pass through Clarify / Confirm / Design / Estimate / Generate, so the phase gate below never applies to it.
  • Temporal detection routes into migrate with temporal units pre-seeded (see discover).

Gate semantics (backbone tail):

  • Gate 1 → migration_plan runs only when generate is done AND recommendation_reviewed == true (generate.md Step 5.5) AND entry point ∈ {migrate, build_deploy} AND the run is migration-eligible (generate.md Step 6) AND the user confirmed Gate 1. Otherwise resolve it: not_applicable (build_scratch / no migratable workload) or skipped (declined) — and advance.
  • Gate 2 → poc runs only when phases.poc == "in_progress" (set when the user answers Gate 2 "yes" — asked in generate.md Step 7 or migration-plan.md Step 6) AND recommendation_reviewed == true. Any winning runtime (agentcore / ecs / eks / lambda / lambda_microvms) — the POC shape follows the verdict (poc.md Step 3 dispatch on references/decision-refs/poc-shapes.md). Gate 2 is only offered when migration_plan ∈ {completed, skipped, not_applicable} — or in_progress on build_deploy only (Stage 2 failed/aborted; fallback POC from design.json per migration-plan.md failure handling); for entry point migrate, only when migration_plan == "completed" (the POC implements the plan) OR when the stage resolved not_applicable with migration_plan_unavailable == "engine_absent" — a standalone deployment that does not bundle the migration engine, where Gate 2 is offered by migration-plan.md Step -1 and the POC is design-backed. A migrate-POC with no plan for any OTHER reason (the user declined) has nothing to implement.
  • Persisting Gate 2 as phases.poc = "in_progress" BEFORE poc.md loads makes the confirmation resumable: if the session breaks between the "yes" and the load, the interpreter re-enters poc without re-asking. (A declared deviation from INTERPRETER.md § The interpreter loop step 5's gate-then-in_progress ordering — the user's confirmation is the entry event worth persisting.)

Phase gate: Do NOT load design.md / estimate.md / generate.md unless $RUN_DIR/.phase-status.json exists and BOTH phases.clarify == "completed" AND phases.confirm == "completed". Confirm confirms the deployment model, the service set, and (for a co_recommend tie) the user's chosen_runtime — Design and the diagram depend on its confirm.json output, so it must not be skipped. If the user asks to skip Clarify or Pass 2, refuse briefly and run it.

State file (.phase-status.json)

{
  "run_id": "0630-1430",
  "entry_point": "build_scratch",
  "audience": "technical",
  "current_phase": "clarify",
  "phases": {
    "intake": "completed",
    "discover": "skipped",
    "clarify": "in_progress",
    "confirm": "pending",
    "design": "pending",
    "estimate": "pending",
    "generate": "pending",
    "migration_plan": "pending",
    "poc": "pending"
  }
}

Status values: pending → in_progress → completed, plus skipped. Use read-merge-write: read before each update, change only the advancing keys, keep prior phases.

recommendation_reviewed (top level, boolean) is set to true by generate.md Step 5.5 when the user explicitly confirms they have seen the recommendation. Gate 1, Gate 2, and the migration_plan / poc states all require it — no gate may be asked while it is absent.

migration_plan additionally uses not_applicable (build_scratch, or no migratable workload detected). When Stage 2 runs, migration_plan_ctx is added at the top level: {"repo": "<abs path to target repo>", "migration_dir": "<abs path to .migration/<id>/>"} — Stage 3 reads gcp-to-aws artifacts ONLY via this recorded path, never by re-globbing.

Files

File Purpose
references/vendored/dsl/INTERPRETER.md Vendored DSL execution contract (interpreter loop + gate protocol)
references/phases/intake/intake.md Entry point + technical background + open context
references/phases/discover/discover.md Lightweight code detection
references/phases/clarify/clarify.md Clarify orchestrator + answer mapping to scoring keys
references/phases/clarify/clarify-technical.md Technical-background question wording
references/phases/clarify/clarify-business.md Business-background question wording
references/phases/confirm/confirm.md Winner-specific follow-ups
references/phases/design/design.md Assemble recommendation; Migrate handoff branch
references/phases/estimate/estimate.md Coarse cost magnitude
references/phases/generate/generate.md Layered recommendation doc + scaffolding
references/phases/migration-plan/migration-plan.md Stage 2: full migration plan via the sibling gcp-to-aws engine
references/decision-refs/temporal.md Temporal rules: Tier 1/2 tables, adapter, runbooks, commercials (consumed by discover/clarify/design/generate)
references/decision-refs/poc-shapes.md Per-runtime POC deploy shapes (ECS/EKS/Lambda/MicroVMs/Temporal)
references/decision-refs/*.md Runtime service cards, model defaults, freshness
references/decision-refs/workload-classes.md Deterministic verdicts for non-agent workload units (batch/service/io)
references/runtimes/*.json Runtime registry (read by scoring.py)
scripts/scoring.py Deterministic scoring engine
scripts/test_temporal_decision_refs.py Content lock for the Temporal decision reference
scripts/test_poc_shapes.py Content lock for the POC deploy shapes
scripts/test_workload_classes.py Content lock for workload-classes.md (verdicts table)
scripts/test_unit_grouping.py Unit grouping + pattern matching (workload-class assignment)
scripts/test_collapse_invariant.py Collapse-invariant ordering enforcement (A→B implies [B] ⊆ [A] outputs)

Maturity and readiness contract

Intake persists target_maturity (prototype, private_beta, or production) in run state. Clarify carries it and the readable readiness record into answers.json. Current-run verification evidence remains exclusively in the sibling $RUN_DIR/current-run-verifications.json artifact: never copy it into seed.json or answers.json. Design and Generate may consume that artifact only after validating its schema and matching run_id, and may carry forward verified outcomes but not the raw evidence records. Load references/decision-refs/maturity-readiness.md whenever target maturity is selected. A cached volatile fact may inform discovery, but only a record verified in this run can make a verification-required constraint final; otherwise the score remains provisional with deferred verification requirements.

原文・著作権は Anthropic および各プラグイン作者に帰属します。日本語訳は Claude API による自動翻訳です。