agent-advisor
Guides startups through a phased decision process to choose an AWS runtime for AI agents (AgentCore, ECS, EKS, Lambda, or Lambda MicroVMs), plan migrations of…
Identity proven by GitHub's verified organisation badge (aws, awslabs)
- Agents
- Claude Code
- License
- Apache-2.0 (permissive)
- Pin
- df2ab44pinned Oct 9, 2026
- Context cost
- about 4,600 tokenswhen the skill loads
- GitHub stars
- 2,830on aws/agent-toolkit-for-aws
- Scripts
- 26 scripts
What it does
“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.”
Generated summary from the skill's own text
Guides startups through a phased decision process to choose an AWS runtime for AI agents (AgentCore, ECS, EKS, Lambda, or Lambda MicroVMs), plan migrations of existing agent workloads, and generate executable proof-of-concept deployments. Uses deterministic scoring based on workload characteristics and adapts to the user's technical background.
When to use it
- Deciding which AWS runtime to deploy a new AI agent on
- Planning migration of existing AI agents to AWS
- Adding services like memory or observability to agents already on AWS
- Understanding Temporal workflow deployment options on AWS
When not to
- Pure LLM SDK rewrites without agentic components
- Compute or data migration with no AI agent involved
- Evaluating per-model pricing or non-AWS infrastructure
Install
These commands install from the publisher's repository and need no account.
skills CLI
npx skills add aws/agent-toolkit-for-aws --skill agent-advisorInstalls from the repository's default branch, which may have changed since the commit we checked.
GitHub CLI 2.90 or later
gh skill install aws/agent-toolkit-for-aws agent-advisor@df2ab44e485b28358353d827e5ebafbe267cce9fInstalls the commit we checked (df2ab44).
Copy by hand
Download the folder at df2ab44 and put it in .claude/skills/agent-advisor for Claude Code and in .agents/skills/agent-advisor for Codex, Cursor, Gemini CLI, GitHub Copilot and OpenCode.
Claude Code plugin
/plugin install amazon-selling-partner@claude-plugins-officialThe publisher's plugin in Claude's official marketplace. It may hold more than this skill, and we have not pinned it.
Claude Code plugin
/plugin install aws-agents@claude-plugins-officialThe publisher's plugin in Claude's official marketplace. It may hold more than this skill, and we have not pinned it.
Claude Code plugin
/plugin install aws-agents-for-devsecops@claude-plugins-officialThe publisher's plugin in Claude's official marketplace. It may hold more than this skill, and we have not pinned it.
Copy for your agent
A prompt for any coding agent. It installs this skill from GitHub at the pinned commit, copies the files as they are, and runs nothing unless you ask.
Prompt
Install the agent skill "agent-advisor" by Amazon (AWS). Source, pinned to commit df2ab44e485b28358353d827e5ebafbe267cce9f: https://github.com/aws/agent-toolkit-for-aws/tree/df2ab44e485b28358353d827e5ebafbe267cce9f/plugins/aws-startup-advisor/skills/agent-advisor 1. Download that folder at exactly this commit. Do not use the default branch. 2. Copy every file unchanged, keeping the folder structure, into .claude/skills/agent-advisor/ for Claude Code and .agents/skills/agent-advisor/ for Codex, Cursor, Gemini CLI, GitHub Copilot and OpenCode. Create the folders if they do not exist. 3. Do not edit the files, and do not run any script in the folder unless I ask you to. 4. When you are done, list the files you copied and where you put them. Listing and checks: https://www.agentnative.dev/skills/amazon-aws/agent-advisor-agent-toolkit-for-aws
SKILL.md at the pin
---
name: agent-advisor
description: "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."
---
# 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 `/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
| 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.
## 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`](../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`](../contextual-offers-for-startups/SKILL.md) for the full rules — but perform the check inline; it must not depend on that skill being loaded.
From aws/agent-toolkit-for-aws at df2ab44, shown as plain text under its license: Apache-2.0 (permissive). The license text is in LICENSE.
View SKILL.md on GitHub at df2ab44Files
- SKILL.md20 KB · 945ee1831ee5
- references/
- decision-refs/
- agentcore.md5.4 KB · c7844a3fe459
- batch.md2.3 KB · d31d73a88e89
- cost-levers.md1.9 KB · 587a05f8b73e
- ecs.md1.4 KB · dbc8088ba535
- eks.md1.1 KB · 82f95fa7cd48
- freshness.md11 KB · 6348fe80767e
- lambda-microvms.md2.0 KB · c85d145904f3
- lambda.md1.1 KB · c42208635a5f
- managed-alternatives.md793 B · 2bdb983cfc8f
- maturity-readiness.md2.8 KB · 347aff7d05db
- model-selection.md4.2 KB · e143bbc42fea
- poc-shapes.md16 KB · e7eb36a0f84c
- temporal.md18 KB · 65e4a6587401
- workload-classes.md2.2 KB · 4339824f9dad
- diagram/
- build-diagram.md5.3 KB · a66ed884dd78
- handoff/
- handoff-migration.md2.1 KB · fea1e2f950bd
- models/
- anthropic-bedrock-2026-07-21.json6.6 KB · b7fd4153fff6
- openai-bedrock-2026-08-21.json17 KB · 03c029e77c05
- output-templates/
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- phases/
- add-capabilities/
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- clarify/
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- design.md28 KB · c1da786f82b2
- discover/
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- discover.md10 KB · e2ea14cab3b4
- estimate/
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- generate/
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- intake/
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- migration-plan/
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- lambda.json2.4 KB · bf612cd0e555
- lambda_microvms.json3.4 KB · f8462bd1de78
- vendored/
- README.md1.2 KB · 7d60a9af3dbf
- dsl/
- INTERPRETER.md53 KB · c333c808950d
- scripts/
- anthropic_model_recommendation.py29 KB · 5e9b3e97f748
- build_diagram.py22 KB · e1567a038557
- model_recommendation.py7.9 KB · 75a46ae2ab2b
- openai_model_recommendation.py41 KB · 190f626c2c1a
- pyproject.toml170 B · c617718c4ac9
- schemas/
- current-run-verifications.json1.2 KB · 3c44b706da9d
- model-recommendation-input.json5.5 KB · 99f07fffb1c5
- model-recommendation.json13 KB · a4d1565e12db
- model-verification.json2.6 KB · f0d385e0a14c
- scoring-result.json3.5 KB · 91c88192a021
- seed.json5.7 KB · 69019731d340
- score_units.py3.0 KB · 0fb8d6f9c78b
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- test_build_diagram.py22 KB · 1996d469841d
- test_collapse_invariant.py2.1 KB · 785ae029a4a0
- test_cost_levers.py1.8 KB · 684d79c52d98
- test_model_recommendation.py13 KB · a7603db29f3a
- test_openai_model_recommendation.py26 KB · 9c7a582b73a4
- test_poc_shapes.py15 KB · d6a6424df799
- test_scoring.py36 KB · bb34f55deca3
- test_temporal_decision_refs.py7.6 KB · 366204881b43
- test_unit_grouping.py73 KB · c07c57c14695
- test_verify_model_path.py8.3 KB · 91f949587321
- test_workload_classes.py5.4 KB · 344d797f053e
- uv.lock100 KB · d5196659551b
- verify_model_path.py8.2 KB · 5c86ef418cde
82 files, 981 KB in total. Each file has a sha256 in the pin's manifest; the first 12 characters are shown.
Skill folder on GitHub at df2ab44More from Amazon (AWS)
agents-build
This skill extends an AgentCore agent project with capabilities like memory, app integration, VPC networking, multi-agent orchestration, browser automation…
Contains scripts
Claude Code · about 2K tokens · pinned 6 days ago
amazon-bedrock
Builds generative AI applications on Amazon Bedrock, covering model invocation, Knowledge Bases for RAG, Bedrock Agents, Guardrails for content safety, and…
Contains scripts
Claude Code, Codex, Cursor +3 · about 8.3K tokens · pinned 6 days ago
amazon-eventbridge-event-bus
Enables building event-driven applications on AWS EventBridge custom event buses with publish/subscribe routing, event retention, filtering, transformation…
Claude Code, Codex, Cursor +3 · about 8.4K tokens · pinned 6 days ago
amazon-neptune
Provides guidance on Amazon Neptune Database and Neptune Analytics for graph workloads including fraud detection, agentic memory, recommendations, and…
Contains scripts
Claude Code, Codex, Cursor +3 · about 4.5K tokens · pinned 6 days ago
architect-for-startups
Provides stage-appropriate AWS architecture guidance for startups from pre-revenue through Series B+, considering runway, team size, and AWS credits.
Claude Code, Codex, Cursor +4 · about 3.4K tokens · pinned 6 days ago
authoring-mwaa-workflow
Writes and deploys production workflow artifacts for Amazon MWAA: Python DAGs for provisioned environments or YAML workflows for Serverless.
Claude Code, Codex, Cursor +4 · about 2.5K tokens · pinned 6 days ago
Related in Cloud and DevOps
temporal-serverless
This skill guides users through deploying Temporal Workers on AWS Lambda using a Worker Controller Instance that invokes Workers on demand.
by TemporalOfficial publisher
Claude Code · about 9.7K tokens · pinned 6 days ago
agent-advisor
Unified advisor that helps teams decide how to run AI agents on AWS by evaluating runtimes (AgentCore, ECS, EKS, Lambda), generating migration plans from…
by Amazon (AWS)Official publisher
Contains scripts
Claude Code · about 4.4K tokens · pinned 6 days ago
agent-advisor
Unified advisor that helps teams deploy AI agents on AWS by evaluating which runtime to use (AgentCore, ECS, EKS, Lambda), generating migration plans for…
by Amazon (AWS)Official publisher
Contains scripts
Claude Code · about 3.7K tokens · pinned 6 days ago
temporal-serverless
This skill deploys and operates Temporal Workers on serverless compute, specifically AWS Lambda, using the Worker Controller Instance to invoke Workers on…
by TemporalOfficial publisher
Claude Code · about 9.7K tokens · pinned 6 days ago
hf-cloud-aws-context-discovery
This skill reads the user's local AWS configuration to discover their active profile, region, account ID, and caller identity before any AWS work begins.
by Hugging FaceOfficial publisher
Claude Code, Codex, Cursor +4 · about 830 tokens · pinned 6 days ago
hf-cloud-python-env-setup
Sets up an isolated Python environment for SageMaker and AWS work with the correct Python version (3.10, 3.11, or 3.12) and current boto3 and awscli.
by Hugging FaceOfficial publisher
Contains scripts
Claude Code, Codex, Cursor +3 · about 1.5K tokens · pinned 6 days ago