Plan Review logo

Plan Review

Community
Mathews-Tom
plan-review

Pre-implementation plan audit stress-testing scope, assumptions, risks, and failure modes before code is written. Triggers on: "review this plan", "is this plan solid", "what am I missing", "challenge my assumptions", "stress-test this", "/plan-review".

Overview

PublisherMathews-Tom
Repositoryarmory
Skill nameplan-review
Stars
318
Forks
47
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

    Published by Mathews-Tom on GitHub. Read the source before you install it.

Installation

Install the Plan 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.
  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/Mathews-Tom/armory.git /tmp/armory
mkdir -p .claude/skills
cp -r /tmp/armory/skills/plan-review .claude/skills/plan-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Plan 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 Plan 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 Plan 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.

Plan Review Skill

Purpose

Execute a structured pre-implementation audit of a technical plan, proposal, or design document. The goal is to surface risks, bad assumptions, missing pieces, and scope problems before any code is written — when course corrections are cheapest.

This skill is read-only. It never modifies code. It produces a severity-tagged review document with a final ship/rethink/reject verdict.

Step 0 — Mode Selection

Ask the user a single question via AskUserQuestion:

Which review lens? (1) Product — scope, user impact, business alignment. (2) Engineering — architecture, failure modes, test strategy, performance. (3) Combined (default) — both lenses integrated.

Accept the answer and proceed. Do not ask follow-up configuration questions.

Also assess scope size from the plan:

  • Small change (single file, ~100 lines or fewer): deliver a compressed 4-section review — Scope, Risks, Missing, Verdict. Skip the full multi-section template.
  • Standard change: execute the full review sequence below.

Full Review Sequence

1. Plan Comprehension

Read the plan end-to-end. Produce a 2–3 sentence summary confirming understanding. Explicitly list:

  • Stated goals — what the plan claims to achieve.
  • Non-goals — what is explicitly out of scope.
  • Constraints — budget, timeline, compatibility, team size, or technology constraints mentioned or implied.

If the summary is wrong, the user corrects it here before the rest of the review proceeds on a false foundation.

2. Assumption Challenge

Extract every implicit assumption. For each one:

AssumptionIf wrong?Supporting evidenceWhat falsifies it?

Common assumption categories to probe:

  • Data availability and shape
  • Third-party API stability and rate limits
  • Team familiarity with chosen tools
  • Performance characteristics of dependencies
  • Backward compatibility requirements
  • Deployment environment capabilities

3. Risk & Failure Mapping

For each component or subsystem in the plan, fill a failure mode table:

ComponentFailure ModeBlast RadiusRecovery Strategy

Additionally identify data flow shadow paths — side effects, async callbacks, event propagation, or cache invalidation chains that are not on the happy path but will execute in production.

Use ASCII diagrams to illustrate non-obvious data flow or failure propagation where the plan involves three or more interacting components.

4. Component-by-Component Review (Engineering Lens)

For each major component, assess:

  • Error handling strategy — Are errors classified and routed through a registry, or silently swallowed by catch-all handlers?
  • Data integrity invariants — What invariants must hold? How are they enforced? What happens when they break?
  • Concurrency and race conditions — Shared state, lock ordering, optimistic vs. pessimistic strategies, idempotency guarantees.
  • Performance under load — Expected throughput, latency budget, resource consumption at 10x current scale.
  • Test strategy adequacy — Unit, integration, and end-to-end coverage for the component. What is untestable and why?

This section is language- and framework-agnostic. Reference references/project-detection.md for framework-aware examples when the user's stack is known.

Skip this section when running product-lens-only mode.

5. Scope & Priority Assessment (Product Lens)

  • Needed vs. nice-to-have — Which features are load-bearing for the stated goals? Which are speculative?
  • Deferral candidates — What can ship in a follow-up without increasing risk?
  • Over-engineering indicators — Abstractions, configurability, or extensibility that no current requirement demands.
  • User-facing impact — Does the complexity produce proportional user value?

Skip this section when running engineering-lens-only mode.

6. Integration Review

How components connect to each other and to the outside world:

  • API contracts — Request/response shapes, versioning, error codes between modules.
  • State management across boundaries — Who owns state? How is it synchronized? What happens during partial failure?
  • Migration and deployment ordering — Which components must deploy first? Are there intermediate states where the system is inconsistent?
  • Rollback compatibility — Can each deployment step be reversed independently? What data is irreversible?

7. What's Missing

Things the plan does not address that it should:

  • Monitoring and observability (metrics, logs, alerts, dashboards)
  • Error recovery paths beyond the first retry
  • Edge cases outside the stated happy path
  • Security considerations (authn, authz, input validation, secrets management)
  • Load and scale implications (connection pools, queue depth, storage growth)
  • Operational runbooks for incident response

8. Execution Assessment

Evaluate the proposed implementation order:

  • Dependency ordering — Are prerequisites built before dependents?
  • Parallel work opportunities — Which tasks have no mutual dependency and can proceed simultaneously?
  • Risk-first vs. value-first — Does the plan tackle the highest-risk unknowns early, or defer them?
  • Prototype candidates — Which components should be spiked before committing to the full implementation?

9. Verdict

Deliver exactly one of:

VerdictMeaning
ShipPlan is solid. Proceed as written.
Ship with changesViable, but specific modifications listed below are required before proceeding.
RethinkFundamental structural issues require re-planning. Itemize what must change.
RejectPlan is not viable. Explain why and what alternative direction to consider.

Include a one-paragraph rationale for the verdict.

Compressed Review (Small Changes)

For small-scope changes (single file, ~100 lines), deliver four sections only:

  1. Scope — What the change does and its boundaries.
  2. Risks — Failure modes and blast radius (brief table).
  3. Missing — Gaps worth addressing even at this scale.
  4. Verdict — Ship / Ship with changes / Rethink / Reject.

Interaction Protocol

  • Use AskUserQuestion one issue at a time. Never batch multiple questions into a single prompt.
  • For HIGH-severity findings, surface them immediately and ask whether to continue or pause for discussion before proceeding to the next section.
  • This skill is read-only. It does not create, modify, or delete any files.
  • Use ASCII diagrams for data flow and component relationships where they clarify failure propagation or integration topology.

Output Format

Structured review document with:

  • Numbered sections matching the sequence above
  • Severity tags on every finding: [HIGH], [MEDIUM], [LOW]
  • Summary table of all findings at the end, grouped by severity
  • Final verdict with rationale

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 Plan Review AI skill do?

Pre-implementation plan audit stress-testing scope, assumptions, risks, and failure modes before code is written. Triggers on: "review this plan", "is this plan solid", "what am I missing", "challenge my assumptions", "stress-test this", "/plan-review".

Why use Plan Review on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Mathews-Tom/armory/tree/main/skills/plan-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 Plan 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 Plan Review?

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

Is the Plan Review AI skill free?

Yes. It is published on GitHub by Mathews-Tom 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇