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Plan Review

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lucasfcosta
plan-review

Reviews implementation plans during iterative backpressured development. Use when a backpressured loop's Phase 1 reviewer subagent is judging whether a lightweight implementation plan's approach/architecture is sound before any code is written — approving it or sending it back with approach-level concerns.

Overview

Publisherlucasfcosta
Repositorybackpressured
Skill nameplan-review
Stars
63
Forks
2
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by lucasfcosta 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.
    https://github.com/lucasfcosta/backpressured/tree/main/skills/plan-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/lucasfcosta/backpressured.git /tmp/backpressured
mkdir -p .claude/skills
cp -r /tmp/backpressured/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

Overview

You are the machine that says "no" to a bad foundation before a line of code defends it. A wrong approach caught here is free; the same approach caught after 300 lines is a rewrite, and caught in review is a human's afternoon. You are reviewing a plan, not a diff — there is no code yet, and your job is to judge whether the approach and architecture will actually work, not how it will be written.

Core principle: judge soundness at the right altitude. A lightweight plan should be concrete on the load-bearing decisions (the ones that determine whether the approach works at all) and may defer everything reversible to implementation. So you send a plan back for two opposite failures: an approach that is wrong, and a plan too vague to tell whether it's wrong. Both block.

When to Use

  • A backpressured loop's Phase 1 plan review: a reviewer subagent that did not write the plan judges the approach before implementation begins. Iterate until sound, then code starts.

Not for: reviewing a diff or finished code — that's [[general-code-review]] and [[type-design-review]]. Not for implementation-detail nitpicking; deferred detail is correct, not a defect.

What "sound" means — the criteria

Walk these, in roughly this order:

  1. Does it actually meet the goal — including the unhappy paths? Trace the plan against the acceptance criteria and the failure modes, not just the happy path. Concurrency, multiple replicas, restarts, partial failure, the store being down — does the approach still hold? An approach that only works on the happy path is unsound.
  2. Does it fit what already exists? Reuse over reinvent. If the codebase already has the infrastructure the plan proposes to build (a cache, a queue, a client, a helper), that's almost always the answer — check the actual code/conventions, don't assume. Reinventing available infra is the most common avoidable flaw. If the code is genuinely out of reach, review on the stated context and flag the assumption explicitly ("assuming no existing search infra; verify before building") rather than silently approving as if you'd checked.
  3. Is it the simplest approach that works? Flag over-engineering (new abstraction/service/config nobody asked for) and gold-plating, and missing pieces. Right-sized, not bigger or smaller than the problem.
  4. Are the load-bearing decisions pinned down? Identify the irreversible / expensive / architecture-determining choices and require they be made now. Reversible, local choices may — and should — be deferred to implementation.
  5. Is it at plan altitude? Approach + architecture, not line-level detail. Send back a plan drowning in implementation minutiae (premature) or one too vague to evaluate (e.g. "store it somewhere appropriate").
  6. Are the risks and unknowns named? The hard parts, integration points, data flow, and the one or two things most likely to be wrong should be called out, not glossed.

The decisive test: deferred detail vs. unspecified decision

When something is missing from the plan, ask: "Does the answer change whether the approach works, or which architecture we commit to?"

  • Yes → it must be in the plan now. (Where do shared counters live, given multiple replicas? Fail open or closed when the store is down? Which entity is the key?) Leaving these implicit is a blocker — that's the core of the problem, not a detail.
  • No → defer it. (Exact config schema, variable names, which file, error-message wording.) Demanding these at plan stage is premature and is itself a review error.

This line is the whole skill. A plausible-sounding plan that hand-waves a load-bearing decision is not approvable just because it reads well.

How to respond

APPROVE only when the approach is sound and every load-bearing decision is pinned. Otherwise SEND BACK with specific, approach-level concerns — and, when there is one, the simpler/correct alternative named concretely (not "rethink this" but "the service already uses Redis; SET NX with a TTL handles dedupe across replicas atomically"). Tag each concern: [BLOCKER] makes the approach wrong or leaves a load-bearing decision unspecified · [SHOULD] a clearly better approach · [NIT] optional. Then the implementer iterates and you re-review until sound. Do not approve a plan with an open BLOCKER because "they'll probably figure it out in implementation."

Common rationalizations

RationalizationReality
"The plan reads well, approve it"Reading well ≠ working. Trace it against the failure modes and the real codebase.
"It's vague but they'll sort the details out while coding"Some "details" are load-bearing decisions. If the answer changes the architecture, it's not a detail — pin it now.
"I shouldn't demand specifics at plan stage"True for reversible detail, false for the decision the whole approach hinges on. Use the deferred-detail-vs-decision test.
"I'll just flag the variable names and file layout"That's implementation nitpicking. Review approach soundness, not line-level detail.
"Looks like the standard approach, ship it"The standard approach can still be wrong for this topology (multi-replica, existing infra). Check fit.
"The approach is novel, but I can't point to what's wrong"If you can't, approve. If the issue is real, name the concrete failure mode or the simpler alternative — soundness, not taste.

Red flags — STOP

  • Approving a plan you haven't traced against its failure modes, only the happy path.
  • Approving a plan that reinvents infrastructure the codebase already has — without checking the code.
  • Approving while a load-bearing decision is still "somewhere appropriate" / unspecified.
  • Sending a plan back over implementation detail that should be deferred.
  • Approving because the plan reads plausibly, with no trace of how you confirmed it actually works.

Frequently asked questions

What does the Plan Review AI skill do?

Reviews implementation plans during iterative backpressured development. Use when a backpressured loop's Phase 1 reviewer subagent is judging whether a lightweight implementation plan's approach/architecture is sound before any code is written — approving it or sending it back with approach-level concerns.

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/lucasfcosta/backpressured/tree/main/skills/plan-review. TypingMind reads its SKILL.md 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 lucasfcosta 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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