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Aws Well Architected Review

OrganizationPopular
aws
aws-well-architected-review

Performs a full AWS Well-Architected Framework review evaluating every framework question across all pillars discovered from the live AWS documentation by analyzing code, IaC, and configurations to produce evidence-backed findings with Eisenhower-prioritized remediation. Supports full reviews (every framework best practice with BP ID citations), quick reviews (question-level), pillar-scoped reviews, score-mode reviews (a maturity scorecard with per-pillar scores and filtered findings), and lens-specific reviews using lenses discovered from the live AWS documentation. Triggers on mentions of Well-Architected review, WA review, WAR, pillar assessment, architecture review across pillars, workload assessment, cloud readiness evaluation, or a Well-Architected score, grade, or scorecard request. Does not apply to single-pillar deep-dives, learning WA concepts, ADRs, or migration readiness assessments.

Overview

Publisheraws
Repositoryagent-toolkit-for-aws
Skill nameaws-well-architected-review
Stars
2.8K
Forks
337
Bundled files
8
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.

  • 8 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 Aws Well Architected Review 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-core/skills/aws-well-architected-review
  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-core/skills/aws-well-architected-review .claude/skills/aws-well-architected-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Aws Well Architected Review 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 Aws Well Architected Review 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 Aws Well Architected Review 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.

Well-Architected Review

Overview

Guides a systematic AWS Well-Architected Framework (WA Framework) review: discover the workload from code and IaC, acquire the live corpus inventory, evaluate every framework question and best practice against evidence, and deliver a risk-ranked, Eisenhower-prioritized report inline.

Framework content is fetched from the live AWS documentation at review time rather than embedded as a snapshot. The AWS MCP server's documentation reader (aws___read_documentation) is recommended for reliable retrieval; when it is unavailable, fetch the same docs.aws.amazon.com pages over HTTPS with the environment's web-fetch tool, and if no documentation access exists at all, proceed from internal knowledge and disclose that the framework inventory could not be verified live. Do not depend on any non-public or internal best-practice/WA-guidance MCP; those are unavailable in supported runtimes.

Detailed procedures live in reference files — read each one when its step says to:

  • Review modes — mode selection (full / quick / pillar-scoped / score), trigger phrases, score output format
  • Discovery procedure — infrastructure and application architecture discovery
  • Live corpus inventory — the gated ACQUIRE_CORPUS stage: deterministic read-only traversal of the framework, the corpus records, and the validation gate
  • Evaluation procedure — evaluating every BP against the frozen manifest, per-pillar passes, aggregation rules, coverage audit
  • Lens guidance — when and how to apply Well-Architected Lenses
  • Risk assessment — impact × likelihood matrix and cross-pillar trade-offs
  • Report template — the full report structure for the report step
  • Security considerations — secure handling of workload data, review tooling, findings, and persisted artifacts

Execution model

Before beginning any step, read and follow security considerations — secrets redaction, HTTPS-only retrieval, least privilege, and confidentiality handling must be loaded before you operate on workload code or review tooling.

A full review runs as a gated sequence. Each step has a transition gate; you MUST NOT advance to the next step, or skip a step, when its gate has not passed. The single user-visible deliverable is one complete inline report — scratch files (a run-local working directory) are execution state only, never the delivered artifact, and their paths MUST NOT appear in the report.

Non-interactive by default. When the user has explicitly requested a review and supplied sufficient scope, execute every step to completion without pausing for confirmation between steps. The discovery and risk-assessment checkpoints are internal validation gates, not user stops: validate them yourself and proceed. Pause for the user only when the user explicitly asked for interactive checkpoints, or when a required scope decision genuinely cannot be inferred (e.g. an ambiguous pillar-scoped request).

Non-negotiable invariants — ALL modes (full / quick / pillar-scoped / score):

  • Step 4 (ACQUIRE_CORPUS) is mode-independent: every mode acquires and validates the live corpus inventory before any assessment and reads its questions/BPs against that frozen manifest. Assessment MUST NOT begin against an incomplete or unvalidated manifest, and no mode may skip Step 4.
  • Canonical IDs only — never fabricate a PILLAR##-BP## ID. If corpus acquisition cannot produce an ID, that is a surfaced error, not a gap to invent around.

Non-negotiable invariants — full review only:

  • Every BP in the frozen manifest receives exactly one status from the five-value vocabulary.
  • All sections of the report template are present (it is the authoritative list — the numbered items in Step 7 are the recall-critical subset, NOT the complete set: the template also mandates the Executive Summary, Architecture Overview, Cross-Pillar Trade-offs, Next Steps, and others). The report is emitted inline, first line # Well-Architected Review:. No "see file", attachment, or scratch-path deferral.

Step 1: Define the workload scope

Establish the workload from what the user provided:

  • Workload name and brief description
  • Code packages/directories to analyze (IaC, application code, CI/CD configs)
  • Business criticality (critical, high, standard, low)
  • Current pain points (optional)

If the user has already provided architecture details or you are in a codebase with IaC, proceed with discovery without prompting. When no code or IaC is available (the user describes their architecture verbally), proceed using the description as evidence; mark findings you cannot verify in code as "Based on description — verify in code." Do NOT ask for code when the user has already given enough context for a meaningful review.

Determine the review mode (full / quick / pillar-scoped / score) from the user's phrasing — read review modes. Determine whether the workload matches a live lens — read lens guidance when one does.

Step 2: Infrastructure Discovery

Read and follow the discovery procedure.

Step 3: Application Architecture Discovery

Continue following the discovery procedure, including its internal completeness gate before evaluation.

Step 4: Acquire and freeze the live corpus inventory (ACQUIRE_CORPUS gate)

Read and follow the live corpus inventory procedure. First create the run-local working directory this review uses for scratch state — the corpus/ folder that holds questions.jsonl, best-practices.jsonl, and manifest.json referenced below. Then build the complete live corpus inventory (the question + best-practice manifest) by a bounded, read-only traversal of the canonical framework pages (prefer aws___read_documentation; see the reference for the non-MCP HTTPS fallback), reduce each page to structured records immediately, and validate the manifest.

Gate: you MUST NOT begin Step 5 until corpus/manifest.json reports valid: true. The frozen manifest is the sole authority for the expected question and BP sets used by evaluation, the coverage audit, and the report.

Step 5: Evaluate EVERY BP against the frozen manifest

CRITICAL — DO NOT PRODUCE A SHORT REVIEW. The most common failure is citing a subset of BPs and stopping. A full review MUST evaluate every BP in the frozen manifest, each with a status from the five-value vocabulary (with rationale).

Read and follow the evaluation procedure. It defines per-pillar passes against the frozen manifest, aggregation, per-mode adjustments, and the coverage audit (its sub-steps are labelled 5a–5d). For each BP assess:

  • Status: exactly one of "Implemented", "Partially Implemented", "Not Implemented", "Not Applicable", "Cannot Determine"
  • Evidence: specific file paths and line numbers (or "Based on description")
  • Gaps: what is missing or could be improved
  • Risk: what could go wrong due to the gap

Evaluate every pillar in the frozen manifest, using its pillar names, prefixes, and question categories.

Step 6: Risk Assessment

Read and follow the risk assessment procedure, including its internal gate.

Step 7: Produce the report

Mode gate: For score mode, emit only the scorecard output from review modes. For quick and pillar-scoped reviews, apply the mode adjustments from review modes. For a lens-only request (the user named a single WA Lens and did not ask for a full framework review), produce a standalone lens report — the core framework tables are omitted and the deliverable is the Lens Findings section plus a lens scorecard, following lens guidance; when a lens is applied on top of a full review, keep the full structure and add the Lens Findings section. Otherwise — a full review — read the report template and produce the report with that exact structure. The report template is the authoritative list of mandatory sections; the following are the recall-critical ones that a weaker model most often drops (do NOT treat them as the complete set):

  1. Coverage audit (from the evaluation procedure, Step 5d) before the executive summary
  2. Pillar scorecard with per-pillar scores (1-5)
  3. Per-question assessment table — every question in the frozen manifest, no truncation
  4. Full BP Ledger — one row per evaluated BP, concatenated verbatim from the pillar passes
  5. Risk-classified findings (Critical/High expanded, Medium condensed, Low tabular)
  6. Eisenhower-prioritized remediation plan (Do First / Plan / Delegate / Defer) with SMART goals

Emit the report inline as the final response, first line # Well-Architected Review:. Do not defer any section to a file.

Step 8: Offer follow-up

After delivering the report, offer:

Would you like me to:

  • Deep-dive into a specific pillar with expanded analysis?
  • Generate IaC templates to remediate a specific finding?
  • Create a migration plan for a specific architectural change?
  • Compare your workload against a specific WA Lens in detail?
  • Generate automated checks (Config rules, custom metrics) for ongoing compliance?
  • Produce a WA Tool import for tracking in the AWS console?

Calibration Guidance

  • A workload with multi-AZ, encryption, CI/CD with rollback, monitoring, and auto-scaling is MATURE — most findings should be improvements, not Critical
  • Do NOT manufacture Critical findings for a well-built workload — accuracy over alarm
  • When business criticality is "low"/"standard", accept simpler architectures (single-region is fine for internal tools)
  • When business criticality is "critical", apply stricter standards (multi-region DR, chaos testing, sub-minute RTO expected)
  • Every finding MUST have code evidence — no generic recommendations without backing
  • If something cannot be determined from code, say "Cannot Determine" and explain what runtime/interview data is needed
  • Acknowledge strengths prominently — a mature workload should feel validated, not just criticized

Security Considerations

Read and follow security considerations before using review tooling, sharing findings, or persisting artifacts.

Bundled files

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

Frequently asked questions

What does the Aws Well Architected Review AI skill do?

Performs a full AWS Well-Architected Framework review evaluating every framework question across all pillars discovered from the live AWS documentation by analyzing code, IaC, and configurations to produce evidence-backed findings with Eisenhower-prioritized remediation. Supports full reviews (every framework best practice with BP ID citations), quick reviews (question-level), pillar-scoped reviews, score-mode reviews (a maturity scorecard with per-pillar scores and filtered findings), and lens-specific reviews using lenses discovered from the live AWS documentation. Triggers on mentions of...

Why use Aws Well Architected Review on TypingMind?

Because you install it once and use it with any model. Aws Well Architected Review 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 Aws Well Architected Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-core/skills/aws-well-architected-review. 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 Aws Well Architected Review?

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 Aws Well Architected Review?

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

Is the Aws Well Architected Review 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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