Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add FlorianBruniaux/claude-code-plugins --skill plan-pipeline-executegit clone --depth 1 https://github.com/FlorianBruniaux/claude-code-pluginsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/florianbruniaux/claude-code-plugins/plan-pipeline-execute)<a href="https://agentmods.dev/skills/florianbruniaux/claude-code-plugins/plan-pipeline-execute"><img src="https://agentmods.dev/badge/skills/florianbruniaux/claude-code-plugins/plan-pipeline-execute/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/florianbruniaux/claude-code-plugins/plan-pipeline-execute"><img src="https://agentmods.dev/badge/skills/florianbruniaux/claude-code-plugins/plan-pipeline-execute.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00043 | $0.01730 |
| Opus 5.5 | $0.00017 | $0.00692 |
| Sonnet 5.5 | $0.00009 | $0.00346 |
| Haiku 4.5 | $0.00004 | $0.00173 |
Grade A, and why
plan-pipeline-execute scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 243 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/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:
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:
- Write the failing test(s) that define the acceptance criteria
- Run tests to confirm they fail (red)
- Commit the failing tests
- 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:
- Identify all tasks in the layer
- Spawn one agent per task in parallel (Task tool, run_in_background: true)
- Each agent receives: its task description, files to modify, acceptance criteria, and relevant ADRs
- Monitor all agents by reading the output path reported by the agent via
Read(TaskOutput was removed in v2.1.278) - Each agent commits on task completion:
git commit -m "feat: {task-description}" - 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}"
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 243 lines · 43 tokens per session scan A 9ed523980676
plan-pipeline-execute is a skill published in the GitHub repository FlorianBruniaux/claude-code-plugins (43 stars, last pushed 2d ago), licensed MIT. It adds 43 tokens to every session and 1,730 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-29.
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