Plan Pipeline Eng Review logo

Plan Pipeline Eng Review

CommunityPopular
FlorianBruniaux
plan-pipeline-eng-review

Engineering architecture gate: lock architecture, diagrams, edge cases, and test matrix before writing implementation code

Overview

PublisherFlorianBruniaux
Repositoryclaude-code-ultimate-guide
Skill nameplan-pipeline-eng-review
Stars
6K
Forks
782
Bundled files
Instructions only
LicenseCC-BY-SA-4.0
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.

  • Self-contained

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

  • Open source

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

Installation

Install the Plan Pipeline Eng 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/FlorianBruniaux/claude-code-ultimate-guide.git /tmp/claude-code-ultimate-guide
mkdir -p .claude/skills
cp -r /tmp/claude-code-ultimate-guide/examples/skills/plan-pipeline/eng-review .claude/skills/plan-pipeline-eng-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Plan Pipeline Eng 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 Pipeline Eng 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 Pipeline Eng 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-pipeline:eng-review: Engineering Architecture Gate

Post-direction, pre-implementation command. Takes validated product direction and returns a buildable technical spec with diagrams. Forces the system to think through architecture before a single line of implementation code is written.

Use after /plan-pipeline:ceo-review has locked direction. Still in plan mode.


The Problem This Solves

Once product direction is locked, the next failure mode is vague architecture. "The system will handle it" is not a plan. This command forces explicit answers to the hard technical questions before they become production incidents.

The key unlock: forcing diagram generation. Diagrams surface hidden assumptions that prose keeps vague. A sequence diagram makes you specify who calls what. A state machine makes you enumerate every failure mode explicitly.


When to Use

  • After product direction is validated (post /plan-pipeline:ceo-review or equivalent)
  • Before any implementation work starts on a non-trivial feature
  • When the feature has async components, external dependencies, or multi-step flows
  • Any time "the architecture is clear" needs to be proven, not assumed

What It Should Produce

OutputWhy it matters
Architecture diagram (Mermaid)Makes component boundaries explicit
Data flow diagramShows where data transforms and who owns what
State machine for core flowForces enumeration of all states including failures
Sync vs async boundary decisionsPrevents "just make it async" without reasoning
Failure mode inventoryEvery failure path, not just happy path
Trust boundary mapWhere do you accept external input? What do you validate?
Test matrixWhat needs to be tested and at which layer

Prompt Template

markdown
# /plan-pipeline:eng-review

You are in engineering manager / tech lead mode. Direction is locked.
Your job is to make it buildable: turn the product direction into a
technical spec that an engineer can implement without making architecture
decisions on the fly.

Do NOT question the product direction. Do NOT suggest scope changes.
Do NOT implement anything. Return a technical spec.

## Step 1: Restate the Feature

1-2 sentences: what is being built. Confirm you are working from the
correct brief.

## Step 2: Architecture Diagram

Draw the component architecture in Mermaid:
- All components involved (frontend, backend, jobs, storage, external APIs)
- Boundaries between components
- Data flow directions

```mermaid
graph LR
    ...

Step 3: Core Flow (Sequence Diagram)

Draw the happy path as a sequence diagram:

  • Which components call which, in what order
  • What data passes at each step
  • Where async handoffs happen
mermaid
sequenceDiagram
    ...

Step 4: State Machine

Draw the state machine for the core domain object:

  • All valid states
  • All transitions and their triggers
  • Terminal states (success AND failure)
mermaid
stateDiagram-v2
    ...

Step 5: Sync vs Async Decisions

For each operation in the flow, decide:

  • Synchronous (blocks the request): why, and what is the latency budget
  • Asynchronous (background job): why, what triggers retry, how does the caller know it succeeded

Step 6: Failure Mode Inventory

For each step in the flow, enumerate:

  • What can fail
  • How it fails (silently? loudly? partial success?)
  • What the recovery path is
  • What the user sees

Flag any failure that is currently silent.

Step 7: Trust Boundaries

For each external input (user uploads, API responses, webhook payloads):

  • What do you trust? What do you validate?
  • Where could malicious input cause harm?
  • Is any external data flowing into further processing (prompt injection risk)?

Step 8: Test Matrix

LayerWhat to testWhy
Unit......
Integration......
E2E......

Identify any failure mode from Step 6 that does not have a corresponding test.

Step 9: Open Questions

List any architectural decision that is genuinely unclear and needs a human decision before implementation can start. Not a comprehensive list, only blockers.


---

## Example

**Feature**: Smart listing creation from photo (post-`/plan-pipeline:ceo-review`)

**Output excerpt**:
```mermaid
graph LR
    Upload[Photo Upload] --> Storage[Object Storage]
    Storage --> Classify[Vision Classification Job]
    Classify --> Enrich[Web Enrichment Job]
    Enrich --> DraftGen[Draft Generation]
    DraftGen --> DB[(Listings DB)]
    DraftGen --> UI[Listing Editor UI]

State machine:

mermaid
stateDiagram-v2
    [*] --> pending
    pending --> classifying
    classifying --> enriching
    classifying --> classification_failed
    enriching --> draft_ready
    enriching --> enrichment_partial
    enrichment_partial --> draft_ready
    draft_ready --> published
    draft_ready --> discarded

Failure modes:

  • Classification fails -> degrade to manual listing (not silent failure)
  • Enrichment partially fails -> use what succeeded, flag missing fields
  • Upload succeeds, classification job never starts -> orphaned file, cleanup job required
  • Web data in draft generation -> prompt injection vector, sanitize before passing to LLM

Pipeline Position

/plan-pipeline:ceo-review    -> product direction locked
/plan-pipeline:eng-review    -> architecture locked        <- you are here
/plan-pipeline:start         -> produce implementation plan
/plan-pipeline:validate      -> validate before execution
/plan-pipeline:execute       -> execute to merged PR

Frequently asked questions

What does the Plan Pipeline Eng Review AI skill do?

Engineering architecture gate: lock architecture, diagrams, edge cases, and test matrix before writing implementation code

Why use Plan Pipeline Eng Review on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/FlorianBruniaux/claude-code-ultimate-guide/tree/main/examples/skills/plan-pipeline/eng-review. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Plan Pipeline Eng 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 Pipeline Eng Review?

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

Is the Plan Pipeline Eng Review AI skill free?

Yes. It is published on GitHub by FlorianBruniaux under the CC-BY-SA-4.0 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.

View all

Set up your own AI workspace now

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