Pipecat is an open-source Python framework for building real-time conversational agents that work with voice, video, images, and other media. It is for creating voice assistants, companions, business agents, and multi-agent systems from modular conversation pipelines.
Borrowing it
Nothing to install: this file belongs to pipecat-ai/pipecat. 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/pipecat-ai/pipecat/main/.claude/skills/cleanup/SKILL.mdgit clone --depth 1 https://github.com/pipecat-ai/pipecatWrote 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/pipecat-ai/pipecat/cleanup)<a href="https://agentmods.dev/skills/pipecat-ai/pipecat/cleanup"><img src="https://agentmods.dev/badge/skills/pipecat-ai/pipecat/cleanup/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/pipecat-ai/pipecat/cleanup"><img src="https://agentmods.dev/badge/skills/pipecat-ai/pipecat/cleanup.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00017 | $0.01737 |
| Opus 5.5 | $0.00007 | $0.00695 |
| Sonnet 5.5 | $0.00003 | $0.00347 |
| Haiku 4.5 | $0.00002 | $0.00174 |
Grade A, and why
cleanup 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 1mo 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 — 326 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Cleanup Skill
The Code Cleanup Skill reviews, refactors, and documents code changes in your current branch, ensuring alignment with Pipecat's architecture, coding standards, and example patterns. It focuses on readability, correctness, performance, and consistency, while avoiding breaking changes.
Skill Overview
This skill analyzes all changes introduced in your branch and performs the following actions:
- Analyze Branch Changes
- Review uncommitted changes and outgoing commits
- Refactor for Readability
- Improve clarity, naming, structure, and modern Python usage
- Enhance Performance
- Identify safe, conservative optimization opportunities
- Add Documentation
- Apply Pipecat-style, Google-format docstrings
- Ensure Pattern Consistency
- Match existing Pipecat services, pipelines, and examples
- Validate Examples
- Ensure examples follow foundational patterns (e.g.
07-interruptible.py)
- Ensure examples follow foundational patterns (e.g.
Usage
Invoke the skill using any of the following commands:
- "Clean up my branch code"
- "Refactor the changes in my branch"
- "Review and improve my branch code"
/cleanup
What This Skill Does
1. Analyze Branch Changes
The skill retrieves all uncommitted changes and outgoing commits to understand:
- New files added
- Modified files
- Code additions and deletions
- Overall scope and intent of changes
2. Code Refactoring
Readability Improvements
- Replace tuples with named classes or dataclasses
- Improve variable, method, and class naming
- Extract complex logic into well-named helper methods
- Add missing type hints
- Simplify nested or complex conditionals
- Replace deprecated methods and features
- Normalize formatting to match Pipecat style
Performance Enhancements
- Identify inefficient loops or repeated work
- Suggest appropriate data structures
- Optimize async workflows and I/O
- Remove redundant operations
Performance changes are conservative and non-breaking.
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.
- 1mo ago First seen · 326 lines · 17 tokens per session scan A 4f419ba49000
cleanup is a skill published in the GitHub repository pipecat-ai/pipecat (16,618 stars, last pushed today), licensed BSD-2-Clause. It adds 17 tokens to every session and 1,737 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
review-fix-signoff-loop
Use when writing Agent Relay or Ricky workflows that must loop review, fix, and validation with fresh agent context until independent signoff agents, typically Claude and Codex, both agree the work is comprehensively complete. Covers fresh-context iterations, repairable gates, dual reviewer verdict contracts…
factory-rereview
Re-review a pull request after a push — reconcile the previous review against the new commits, look for new defects the push introduced, then a fresh pass over the whole PR, and finish with a verdict on the PR.
understand-pr
Review a PR the way a seasoned maintainer would — build an independent model before seeing the diff, investigate the implementation and history, verify material claims, and return a concise briefing. Use for any PR review, self-review, or re-review.
factory-gitlab-review
Review a GitLab merge request for a Factory work item using brokered source-control tools.
factory-gitlab-rereview
Re-review a GitLab merge request after new commits and reconcile the previous review.
factory-self-review
Review and test a Factory work item's pushed branch before any pull or merge request exists, then record the verdict that sends it back to Building or on to open the change request.