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Plan Pipeline Execute

CommunityPopular
FlorianBruniaux
plan-pipeline-execute

Execute a validated plan: worktree isolation, TDD scaffolding, level-based parallel agents, quality gate with smoke test, PR creation and merge. Handles everything through to merged PR.

Overview

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

Use it in TypingMind

Enable Plan Pipeline Execute 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 Execute 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 Execute 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:execute: Execution to Merged PR

Execute the validated plan in an isolated worktree. Spawn per-task agents, verify quality, create and merge the PR. Handles everything through to cleanup.

Run /clear before this command.


Prerequisite

A validated plan must exist at docs/plans/plan-{name}.md with all issues resolved (output of /plan-pipeline:validate).


Step 1: Worktree Setup

Create an isolated git worktree:

bash
git worktree add .worktrees/{plan-name} -b feature/{plan-name}

All execution happens inside the worktree. Main branch remains clean throughout.


Step 2: TDD Scaffolding

Only for tasks marked as TDD in the plan.

For each TDD task, before any implementation:

  1. Write the failing test(s) that define the acceptance criteria
  2. Run tests to confirm they fail (red)
  3. Commit the failing tests
  4. Mark the test file in the task for the implementation agent to find

Do not write implementation code in this step.


Step 3: Level-Based Parallel Execution

Parse the task list from the plan. Group tasks by layer (Layer 1 = foundation, Layer 2 = depends on Layer 1, etc.).

For each layer:

  1. Identify all tasks in the layer
  2. Spawn one agent per task in parallel (Task tool, run_in_background: true)
  3. Each agent receives: its task description, files to modify, acceptance criteria, and relevant ADRs
  4. Monitor all agents by reading .claude/tasks/<id>/output.log via Read (TaskOutput is deprecated since v2.1.83)
  5. Each agent commits on task completion: git commit -m "feat: {task-description}"
  6. Wait for all tasks in the layer to complete before starting the next layer

Drift detection: after each layer, diff the actual changes against the plan spec. If implementation deviates significantly from the plan (new files not in plan, plan files not touched), flag and ask how to proceed. Do not silently continue on drift.

Agent instructions for each task:

You are implementing one task from a validated plan.
Task: {description}
Files to modify: {file list}
Acceptance criteria: {criteria}
Relevant ADRs: {adr list}

First principles:
- Build state-of-the-art. No workarounds, no legacy patterns.
- Fix at the correct architectural level, never with component-level hacks.
- If you discover that the plan is wrong or missing context, stop and report. Do not improvise architecture.

Commit your changes when complete with message: "feat: {task-description}"

Step 4: Quality Gate

Run in parallel:

  • Linter
  • Type checker (if applicable)
  • Full test suite

If all pass: proceed to smoke test.

If any fail: spawn a quality-fixer debug agent with the failure output. It gets up to 3 auto-fix attempts. After each attempt, re-run the quality gate. If still failing after 3 attempts: stop, report the failure with the full error output, and wait for human intervention.

Integration smoke test (skip for pure frontend or docs-only plans):

Run the smoke commands defined in the plan's ## Integration Verification section. Additionally:

  • If GraphQL: run an introspection probe to verify schema is accessible
  • If Docker services: scan container logs for ERROR-level entries
  • If new API routes: verify each returns expected status codes

Smoke test failures are debugged by a quality-fixer-smoke agent with the same 3-attempt limit.


Step 5: Pre-PR Documentation

In the worktree, before creating the PR.

PRD Reconciliation: compare the implemented behavior against the original PRD. Note any deviations or additions discovered during implementation. Update the PRD with actuals. These updates ship in the same PR as the feature.

Plan Archival: move docs/plans/plan-{name}.md to docs/plans/completed/plan-{name}.md. Update the status header.

Commit documentation updates: docs: reconcile PRD and archive plan for {feature-name}.


Step 6: Push and PR

Push the worktree branch and create the PR:

bash
git push origin feature/{plan-name}
gh pr create \
  --title "{feature-name}: {one-line summary from plan}" \
  --body "$(cat .pr-body.md)"

PR body template:

markdown
## Summary
{plan summary paragraph}

## Changes
{auto-generated from task list: bullet per task with files affected}

## ADRs
{list of ADRs created during this plan}

## Test Plan
{from plan test plan section}

## Smoke Test Results
{output from integration verification}

Merge using squash:

bash
gh pr merge --squash --delete-branch

Step 7: Post-Merge Metrics

Switch back to develop/main. Update docs/plans/metrics/{name}.json with execution data:

  • Task count and per-layer breakdown
  • TDD task count
  • Diff stats (files changed, lines added/removed)
  • Quality gate results (pass/fail, fix attempts)
  • Smoke test results
  • Drift score (0-1, how closely implementation matched plan)
  • PR data (number, merge commit, timestamp)

Commit metrics update.


Step 8: Worktree Cleanup

bash
git worktree remove .worktrees/{plan-name}

Usage

/plan-pipeline:execute

Picks up the most recent validated plan. Or specify:

/plan-pipeline:execute plan-user-authentication

Output

Setting up worktree: .worktrees/user-authentication
Branch: feature/user-authentication

TDD scaffolding: 2 tasks marked TDD
  ✓ Written failing tests for: auth-token-validation
  ✓ Written failing tests for: refresh-token-rotation
  Committed: "test: failing tests for auth pipeline (TDD)"

Executing Layer 1 (3 tasks, parallel)...
  [agent-1] Implementing: JWT token generation service
  [agent-2] Implementing: User session model
  [agent-3] Implementing: Auth middleware
  ✓ Layer 1 complete. 3 commits.

Drift check: Layer 1... ✓ No drift detected.

Executing Layer 2 (2 tasks, parallel)...
  [agent-4] Implementing: Login endpoint
  [agent-5] Implementing: Refresh endpoint
  ✓ Layer 2 complete. 2 commits.

Quality gate...
  ✓ Lint passed
  ✓ Type check passed
  ✓ Tests: 47 passed, 0 failed

Smoke test...
  ✓ GraphQL introspection: OK
  ✓ POST /api/auth/login: 200
  ✓ POST /api/auth/refresh: 200

Pre-PR docs...
  ✓ PRD reconciled (1 minor deviation noted)
  ✓ Plan archived to docs/plans/completed/

PR created: #142 "user-authentication: JWT auth with refresh token rotation"
PR merged (squash). Branch deleted.

Metrics committed. Worktree cleaned.
✅ Feature complete.

When to Use

After /plan-pipeline:validate confirms all issues are resolved. Never skip validation: executing an unvalidated plan skips the independent review that catches ~18 issues on average.

Pipeline Position

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

Frequently asked questions

What does the Plan Pipeline Execute AI skill do?

Execute a validated plan: worktree isolation, TDD scaffolding, level-based parallel agents, quality gate with smoke test, PR creation and merge. Handles everything through to merged PR.

Why use Plan Pipeline Execute on TypingMind?

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

Which AI models can use Plan Pipeline Execute?

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 Execute?

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

Is the Plan Pipeline Execute 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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