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Architecture Review

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owainlewis
architecture-review

Reviews a technical proposal before implementation. Use for system architectures, feature specs, RFCs, ADRs, and issues that define how a system change should work. Finds material ambiguity and flaws in correctness, scalability, performance, security, operations, and proof.

Overview

Publisherowainlewis
Repositoryblueprint
Skill namearchitecture-review
Stars
419
Forks
73
Bundled files
1
LicenseMIT
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Architecture 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/owainlewis/blueprint/tree/main/skills/architecture-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/owainlewis/blueprint.git /tmp/blueprint
mkdir -p .claude/skills
cp -r /tmp/blueprint/skills/architecture-review .claude/skills/architecture-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Architecture 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 Architecture 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 Architecture 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.

Architecture review

Find choices that could make a technical proposal wrong, unsafe, or impossible to prove. Review the proposed behavior and tradeoffs, not the document's size or format.

Use one fresh subagent that did not write the proposal. Give the reviewer the complete context. Tell the reviewer to work directly without delegating. If you are that reviewer, review directly. Stay read-only.

If fresh subagents are unavailable, stop and report that independent review is blocked. Continue only if the user explicitly accepts a documented self-review.

Process

  1. Read the goal, proposal, requirements, intended architecture, repository instructions, relevant current code, tests, schemas, configuration, and linked material. Treat claims about the current system as unverified until code, tests, schemas, configuration, infrastructure, or relevant runtime evidence supports them.
  2. State the problem, affected user, intended outcome, success measure, scope, constraints, and main tradeoff. Report any that the proposal leaves unclear.
  3. Trace one real case from input to observable outcome. Include ownership, validation, state changes, side effects, response timing, failure, retry, cleanup, and what the user sees where they matter.
  4. Challenge the chosen design with the review focus below. Look for a simpler choice that reaches the same outcome and proof with less state, coupling, duplication, or operational work.
  5. Surface material open questions. Recommend an answer when evidence supports one. Do not invent questions that cannot change the design.
  6. Return findings, open questions, a short assessment, and one verdict. Do not rewrite the proposal, plan the work, review implementation code, or implement changes.

Review focus

  • Check fit with requirements and accepted architecture. Verify current-state claims without requiring a new system to have code.
  • Check ownership, boundaries, interfaces, data, compatibility, migration, rollout, and rollback.
  • Trace partial failure, retry, cancellation, concurrency, startup, shutdown, and recovery where they affect the proposal.
  • Check claimed scale, limited resources, latency, throughput, storage, cost, dependency failure, and operator recovery only where they can change the choice.
  • Check identity, authorization, untrusted input, credentials, destructive authority, and sensitive data handling.
  • Require observable acceptance criteria and proof for important rules and failure paths. Do not let implementation invent user-visible behavior, interfaces, data rules, security policy, or failure behavior.

Material questions

Report an open question only when two capable implementations could answer it differently in a way that affects users, data, interfaces, security, scale, performance, operations, cost, compatibility, or proof.

  • Blocking: implementation should not start without the answer.
  • Important: the proposal should record the answer, but the reviewer can recommend a safe default from available evidence.

Omit questions that are stylistic, safely local to implementation, outside the stated scope, or speculative beyond the scale the proposal claims to support.

Findings

Report only flaws that can change the design or its safety:

  • Blocker: the choice is unsafe, contradicts the goal or current system, or cannot recover from an important failure.
  • Important: the proposal permits materially different implementations or leaves a meaningful risk in behavior, scale, performance, security, operations, compatibility, or proof.

For each finding or open question include its location, concrete failure or ambiguity, impact, evidence, and the smallest correction or recommended answer. Report unclear wording when it prevents a new teammate from explaining, evaluating, or implementing the design. Omit other writing preferences.

Use plain words. State the exact condition, failure, and effect. Do not hide an unknown behind vague language such as may have issues or could be risky.

Verdict

  • Approve: no blocker or important findings or open questions remain.
  • Request changes: the proposal has a fixable blocker or important finding or open question.
  • Blocked: the review lacks required context, repository evidence, specialist coverage, or an independent reviewer.

State what remains unverified. Do not approve because the document is detailed or because every template section exists.

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 Architecture Review AI skill do?

Reviews a technical proposal before implementation. Use for system architectures, feature specs, RFCs, ADRs, and issues that define how a system change should work. Finds material ambiguity and flaws in correctness, scalability, performance, security, operations, and proof.

Why use Architecture Review on TypingMind?

Because you install it once and use it with any model. Architecture 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 Architecture Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/owainlewis/blueprint/tree/main/skills/architecture-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 Architecture 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 Architecture Review?

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

Is the Architecture Review AI skill free?

Yes. It is published on GitHub by owainlewis under the MIT 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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