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Agent Advisor

OrganizationPopular
aws
agent-advisor

Entry point for AI-agent work on AWS: pick a runtime, plan a migration for existing workloads, and build an executable POC — one phased 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/migrate my agents to AWS, agent migration plan, add AgentCore services (memory, gateway, identity, policy, observability) to an agent already on AWS, Temporal on AWS (migrate/run Temporal workers on AWS, a service orchestrated by Temporal, Temporal Cloud vs self-hosted). Temporal Workflow code is never rewritten into Step Functions. Requires at least one agentic component — a purely non-agent system (plain services, batch jobs, HTTP endpoints, non-agent Temporal Activities) is out of scope, redirected to gcp-to-aws / heroku-to-aws / llm-to-bedrock. Not for: compute/data migration with no AI agent; pure LLM SDK rewrite (use llm-to-bedrock); per-model pricing.

Overview

Publisheraws
Repositoryagent-toolkit-for-aws
Skill nameagent-advisor
Stars
2.8K
Forks
337
Bundled files
80
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 80 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by aws on GitHub. Read the source before you install it.

Installation

Install the Agent Advisor AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-startup-advisor/skills/agent-advisor
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git /tmp/agent-toolkit-for-aws
mkdir -p .claude/skills
cp -r /tmp/agent-toolkit-for-aws/plugins/aws-startup-advisor/skills/agent-advisor .claude/skills/agent-advisor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Advisor in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Agent Advisor on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Agent Advisor is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

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 /aws-startup-advisor: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)

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

FilePurpose
references/vendored/dsl/INTERPRETER.mdVendored DSL execution contract (interpreter loop + gate protocol)
references/phases/intake/intake.mdEntry point + technical background + open context
references/phases/discover/discover.mdLightweight code detection
references/phases/clarify/clarify.mdClarify orchestrator + answer mapping to scoring keys
references/phases/clarify/clarify-technical.mdTechnical-background question wording
references/phases/clarify/clarify-business.mdBusiness-background question wording
references/phases/confirm/confirm.mdWinner-specific follow-ups
references/phases/design/design.mdAssemble recommendation; Migrate handoff branch
references/phases/estimate/estimate.mdCoarse cost magnitude
references/phases/generate/generate.mdLayered recommendation doc + scaffolding
references/phases/migration-plan/migration-plan.mdStage 2: full migration plan via the sibling gcp-to-aws engine
references/decision-refs/temporal.mdTemporal rules: Tier 1/2 tables, adapter, runbooks, commercials (consumed by discover/clarify/design/generate)
references/decision-refs/poc-shapes.mdPer-runtime POC deploy shapes (ECS/EKS/Lambda/MicroVMs/Temporal)
references/decision-refs/*.mdRuntime service cards, model defaults, freshness
references/decision-refs/workload-classes.mdDeterministic verdicts for non-agent workload units (batch/service/io)
references/runtimes/*.jsonRuntime registry (read by scoring.py)
scripts/scoring.pyDeterministic scoring engine
scripts/test_temporal_decision_refs.pyContent lock for the Temporal decision reference
scripts/test_poc_shapes.pyContent lock for the POC deploy shapes
scripts/test_workload_classes.pyContent lock for workload-classes.md (verdicts table)
scripts/test_unit_grouping.pyUnit grouping + pattern matching (workload-class assignment)
scripts/test_collapse_invariant.pyCollapse-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.

Contextual offers (final step)

Before you finish: your answer is not complete until you have run the Contextual offers (final step) below.

This is a mandatory closing step of every final recommendation — complete it before you consider your answer done. Run this offer check inline yourself; do not depend on loading another skill. The recommendation is decided on merit first; this step never changes, re-ranks, or re-opens it, and the offer is looked up only after the advice is final.

  1. Read ../knowledge-base-for-startups/references/offers.md — the offer index (Offer · Summary · Keywords), one row per offer.
  2. Judge relevance against what the founder actually surfaced this session. Surface an offer only if one holds: a named-product match (you recommended a specific partner product by name and it has a row); a direct intent/component match (an offer's Keywords directly serve a concrete workload, a component in the final recommendation, or a need the founder raised). Keep a high bar — if the match is weak, or you would be inferring an unstated need, append nothing. Never surface an offer that competes with a product or provider the founder already uses or decided to keep (e.g. existing auth provider → no Auth0 unless they are already on Auth0, and then only as a credit on it; existing vector DB such as Pinecone → no Qdrant).
  3. If (and only if) one offer clears the bar, open only its detail file ../knowledge-base-for-startups/references/offers/<slug>.md and append exactly one quiet, dismissible italic line — stating the value and condensing eligibility faithfully from that file (drop no qualifying criterion; never fabricate), with the redeem link built from the canonical source_url plus a source attribution param — append ?source=ide-startupAdvisor-<host> (or &source=... if the url already has a query string), filling <host> from your execution environment (Claude Code → claude; AWS Startup Advisor IDE extension / VS Code → vscode; Cursor → cursor; Kiro → kiro; Codex → codex), or the generic ide-startupAdvisor when you cannot tell. Only append the query param; never change the destination. When an offer clears the bar you MUST append that single offer line as the last step; do not omit it. The offer is never the pick — only a line under the AWS pick — and stage gates in the references apply (e.g. no Datadog line for an early-stage founder; see ../architect-for-startups/references/observability.md). For a single-service pick (auth, payments, vector DB, observability, etc.) where missing budget/team/traffic inputs would not change the pick, assume an early-stage (pre-PMF) startup unless the founder says otherwise and answer in one turn without a stage-only question; this only removes a stage-only question and never skips the Intake/Clarify/Confirm gates before final advice or the POC. Show exactly one, never a list. If none clears the bar, append nothing and do not mention offers, partner alternatives, or why none appeared.

Standard line:

***<Offer>** has an exclusive offer via AWS Activate — <value>, <eligibility>. [Redeem →](<source_url>?source=ide-startupAdvisor-<host>)*

Caps and control: at most one offer per response and often none; no more than one per five messages and two per session; show a given offer at most once per session and never one already shown, claimed, or dismissed; if the founder has muted offers, skip this step entirely. These per-five-messages, per-session, and already-shown caps are session-state limits; in a fresh session with no prior offers they are non-binding, so do not withhold an otherwise-qualifying offer merely because you cannot verify session history. See ../contextual-offers-for-startups/SKILL.md for the full rules — but perform the check inline; it must not depend on that skill being loaded.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

and 20 more files.

Frequently asked questions

What does the Agent Advisor AI skill do?

Entry point for AI-agent work on AWS: pick a runtime, plan a migration for existing workloads, and build an executable POC — one phased 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/migrate my agents to AWS, agent migration plan, add AgentCore services (memory, gateway, identity, policy, observability) to an agent already on AWS, Temporal on AWS (migrate/run Temporal workers on AWS, a service orchestrated by Temporal, Temporal Clo...

Why use Agent Advisor on TypingMind?

Because you install it once and use it with any model. Agent Advisor is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Agent Advisor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-startup-advisor/skills/agent-advisor. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Agent Advisor?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Agent Advisor?

As many as you like. As long as a model supports skills, you can use Agent Advisor with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Agent Advisor AI skill free?

Yes. It is published on GitHub by aws under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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