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Plan Pipeline Start

CommunityPopular
FlorianBruniaux
plan-pipeline-start

5-phase planning: PRD analysis, design review, technical decisions, dynamic research team, metrics. Produces a complete implementation plan + ADRs before any code is written.

Overview

PublisherFlorianBruniaux
Repositoryclaude-code-ultimate-guide
Skill nameplan-pipeline-start
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 Start 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/start .claude/skills/plan-pipeline-start
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Plan Pipeline Start 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 Start 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 Start 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:start: 5-Phase Planning

Analyze the request and produce a complete implementation plan through structured phases. No code is written. Every significant decision is recorded. Run /clear after this command before running /plan-pipeline:validate.


Phase 1: PRD & Design Analysis

Step 1.1: PRD Analysis

Skip if no PRD exists (refactor, infra change, bug fix).

Read all PRD files and docs/INFORMATION_ARCHITECTURE.md if present. Scan the codebase to understand current implementation status.

Surface findings in 3 buckets:

Missing requirements: acceptance criteria that are absent or incomplete Ambiguous requirements: items with multiple valid interpretations Compliance concerns: security, data privacy, API contract implications

For each finding: present options with concrete pros/cons. Discuss with user. Record every decision in the plan file under a ## Decisions section before moving on. Do not proceed past unresolved ambiguities.

Step 1.2: Design Analysis

Skip if no UI changes are in scope.

Read: DESIGN_SYSTEM.md, existing UX ADRs, CLAUDE.md UX rules.

Produce specs for:

  • Screen inventory: new/modified screens, route placement, component reuse audit
  • State catalog: empty, loading, populated, error, and partial states for every interactive element
  • Interaction specs: user flows (happy path + alternates), focus/keyboard behavior
  • Animation specs: map each interaction to existing keyframes or specify new ones, include prefers-reduced-motion fallbacks
  • Responsive behavior: breakpoints, web/mobile divergence decisions
  • Accessibility: WAI-ARIA pattern selection, live regions, error visibility

Create Design ADRs for significant UX decisions (choice of interaction pattern, new animation convention, platform divergence). Record minor layout choices directly in the plan file.


Phase 2: Technical Analysis

Spawn 1-2 Explore agents for targeted codebase research. Run them in the background via Task tool.

While agents run, check:

  • Existing ADRs in docs/adr/: if 3+ ADRs confirm a decision → auto-resolve without asking
  • PATTERNS.md: apply confirmed patterns directly

When agents return: present architecture decisions with 2-3 options each, concrete pros/cons, and a recommendation. Ask for user input on each unresolved decision.

For each significant decision:

  1. Create docs/adr/ADR-XXXX.md using standard Nygard format (Context / Decision / Status / Consequences)
  2. Update docs/adr/PATTERNS.md with the new observation

Phase 3: Scope Assessment

Apply trigger rules to determine which research agents are needed. Present the proposed team with justification for each inclusion.

Research agent pool:

AgentTriggerModel
code-explorerAlwaysSonnet
arch-researcherChanges touch 2+ architectural layersSonnet
database-analystAny DB schema changeSonnet
security-analystAuth, payments, PII, RBAC, rate limitingOpus
test-analyzerNon-trivial feature (not just a bug fix)Sonnet
cross-platform-specialistWeb + mobile parity requiredSonnet
native-app-specialistTasks touch mobile/native UI packageSonnet
design-system-researcherUI changes in scopeSonnet
dependency-researcherNew packages being addedSonnet
devops-specialistDocker, env vars, CI/CD changesSonnet
integration-researcherNew services, libraries, OTEL configOpus
planning-coordinatorAlways, when 2+ agents selectedOpus

Tier labels (descriptive, not prescriptive):

  • Tier 0 (0 agents): Solo (inline research, no spawning)
  • Tier 1 (1-3 agents): Focused
  • Tier 2 (4-6 agents): Standard
  • Tier 3 (7-9 agents): Comprehensive
  • Tier 4 (10+ agents): Full Spectrum

Tell the user: "I recommend a [Tier N - Label] team: [agent list with one-line justification each]. Want to add or remove any agents?"

Wait for approval before Phase 4.


Phase 4: Research & Plan Creation

Tier 0: Conduct inline research. Write plan directly without spawning agents.

Tier 1+: Spawn approved agents in parallel using Task tool (run_in_background: true). For each agent, provide:

  • Its specific research scope
  • The relevant files/areas to investigate
  • The questions it needs to answer

Monitor agents by reading their output files directly (TaskOutput is deprecated since v2.1.83; use Read on .claude/tasks/<id>/output.log instead). Report progress: "3/6 agents complete..."

When all agents return: if planning-coordinator was spawned, send it all agent reports and have it synthesize the final plan. Otherwise, synthesize directly.

Plan file structure (docs/plans/plan-{name}.md):

markdown
# Plan: {feature-name}
Created: {date} | Branch: {branch-name} | Tier: {N}

## Summary
One paragraph: what this implements and why.

## Decisions
Decisions recorded during Phase 1 (PRD analysis).

## Architecture
ADRs created, patterns applied, architectural choices made.

## Tasks
Ordered task list with layers (1 = foundation, 2 = depends on 1, etc.)

### Layer 1
- [ ] Task A: description, files affected, acceptance criteria
- [ ] Task B: description, files affected, acceptance criteria

### Layer 2
- [ ] Task C (depends on A): description, files affected, acceptance criteria

## Test Plan
How each task will be verified. TDD tasks marked explicitly.

## Integration Verification
Smoke test commands to run post-execution (if backend/services in scope).

## Out of Scope
What this plan explicitly does not address.

Commit: plan file + ADR files + agent report manifests.


Phase 5: Finalize Metrics

Record timestamps, phase durations, agent counts, and cost estimates in docs/plans/metrics/{name}.json. Commit.


Auto-Transition

If Phase 1 produced no unresolved ambiguities and Phase 2 produced no unresolved decisions: auto-start /plan-pipeline:validate without asking.

If any human discussion occurred: ask "Ready to validate this plan?" before proceeding.


Usage

/plan-pipeline:start

Provide the feature description or point to a PRD file when prompted. The command handles the rest interactively.

When to Use

Use for any non-trivial feature: anything touching more than 2 files, involving architecture decisions, or where a planning mistake would be expensive to undo.

For simple changes (typos, trivial refactors): use /plan mode instead.

Pipeline Position

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

Frequently asked questions

What does the Plan Pipeline Start AI skill do?

5-phase planning: PRD analysis, design review, technical decisions, dynamic research team, metrics. Produces a complete implementation plan + ADRs before any code is written.

Why use Plan Pipeline Start on TypingMind?

Because you install it once and use it with any model. Plan Pipeline Start 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 Start 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/start. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Plan Pipeline Start?

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 Start?

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

Is the Plan Pipeline Start 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.

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