Borrowing it
Nothing to install: this file belongs to TheAstrelo/Claude-Pipeline. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/TheAstrelo/Claude-Pipeline/main/.claude/commands/auto-pipeline.mdgit clone --depth 1 https://github.com/TheAstrelo/Claude-PipelineWrote 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/commands/theastrelo/claude-pipeline/auto-pipeline)<a href="https://agentmods.dev/commands/theastrelo/claude-pipeline/auto-pipeline"><img src="https://agentmods.dev/badge/commands/theastrelo/claude-pipeline/auto-pipeline/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/commands/theastrelo/claude-pipeline/auto-pipeline"><img src="https://agentmods.dev/badge/commands/theastrelo/claude-pipeline/auto-pipeline.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 | $0.00000 | $0.01234 |
| Opus 5 | $0.00000 | $0.00617 |
| Sonnet 5 | $0.00000 | $0.00247 |
| Haiku 4.5 | $0.00000 | $0.00123 |
Grade A, and why
auto-pipeline 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 11d ago.
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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Automated Pipeline
Run: /auto-pipeline [--provider=auto|claude|codex] [--profile=yolo|fast|standard|paranoid] [--policy-rollout=legacy|shadow|enforced] [--resume=RUN_ID] [--skip-arm] [--skip-ar] [--skip-pmatch] [--model-strong=MODEL] [--model-fast=MODEL] [--no-commit] [--allow-untested-commit] [--retention-days=N] [--retention-max-runs=N] <task>
$ARGUMENTS
What this is
A thin wrapper over the real pipeline engine, run-pipeline.sh at the repo
root. It does not reimplement the 13 phases — it launches the one script that
actually executes them and relays the result. The engine owns model routing,
gates, validators, retries, and artifacts.
(This replaced ~2,400 lines of bash-embedded-in-markdown that no interpreter
ever ran. If you are editing pipeline behavior, edit run-pipeline.sh, not
this file.)
How to run it
-
Parse
$ARGUMENTSinto flags plus a task description. If there is no task, ask the user for one and stop — do not run the engine without a task. -
Launch the engine once, from the project root, via the Bash tool. Pass the flags through and give the task as a single quoted argument. Set
PIPELINE_NONINTERACTIVE=1so a failed HARD gate halts (exit 3) instead of blocking on a prompt with no TTY:PIPELINE_NONINTERACTIVE=1 bash run-pipeline.sh --profile=<profile> "<the task>"The engine parses
--profile,--skip-arm,--skip-ar,--skip-pmatch,--provider,--resume=RUN_ID,--model-strong=,--model-fast=,--no-commit,--allow-dirty,--allow-untested-commit,--policy-rollout=, retention controls, budget caps, and--modeitself — pass whatever the user supplied.A resume invocation must repeat the original task and effective options. The engine proceeds only if its atomic checkpoint and all bound engine, configuration, Git, worktree, verification-policy, ledger, attempt, and artifact hashes still match.
This is long-running (it spawns up to 13 provider subprocesses, several minutes, real API spend). Launch it in the background and monitor, or tell the user it will take a while before starting.
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.
- 11d ago First seen · 97 lines · 0 tokens per session scan A 36a4580c9f0e
auto-pipeline is a command published in the GitHub repository TheAstrelo/Claude-Pipeline (45 stars, last pushed 6d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,234 tokens. 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 2026-08-30.
Other commands, from other repositories
deploy
Deploy application with pre/post-deploy checks.
status
Summarise what Fabrik is doing right now — board state, in-flight workers, worktrees with uncommitted changes.
happy-start-cd.template
This prompt was authored for Claude-style slash workflows. In Codex runtime, adapt tool calls as follows.
devops
Author CI/CD, containers, or IaC following the repo's platform — deploy and secrets flagged for human approval, never executed.
setup-forge
First-run onboarding — asks name/goal/project-type/language, then beginner-safe API-key setup, then scaffolds the per-project Forge system. User-invoked only. Run once per project. Subcommands: doctor · keys · reset.
workflow-automation
Integrate AI swarms with GitHub Actions to create intelligent, self-organizing CI/CD pipelines that adapt to your codebase.