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/code-review/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/code-review)<a href="https://agentmods.dev/skills/pipecat-ai/pipecat/code-review"><img src="https://agentmods.dev/badge/skills/pipecat-ai/pipecat/code-review/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/code-review"><img src="https://agentmods.dev/badge/skills/pipecat-ai/pipecat/code-review.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 | $0.00014 | $0.01581 |
| Opus 5.5 | $0.00006 | $0.00632 |
| Sonnet 5.5 | $0.00003 | $0.00316 |
| Haiku 4.5 | $0.00001 | $0.00158 |
Grade A, and why
code-review 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Provide a code review for the given pull request.
Agent assumptions (applies to all agents and subagents):
- All tools are functional and will work without error. Do not test tools or make exploratory calls. Make sure this is clear to every subagent that is launched.
- Only call a tool if it is required to complete the task. Every tool call should have a clear purpose.
To do this, follow these steps precisely:
-
Launch a haiku agent to check if any of the following are true:
- The pull request is closed
- The pull request is a draft
- The pull request does not need code review (e.g. automated PR, trivial change that is obviously correct)
- Claude has already commented on this PR (check
gh pr view <PR> --commentsfor comments left by claude)
If any condition is true, stop and do not proceed.
Note: Still review Claude generated PR's.
-
Launch a haiku agent to return a list of file paths (not their contents) for all relevant CLAUDE.md files including:
- The root CLAUDE.md file, if it exists
- Any CLAUDE.md files in directories containing files modified by the pull request
-
Launch a sonnet agent to view the pull request and return a summary of the changes
-
Launch 4 agents in parallel to independently review the changes. Each agent should return the list of issues, where each issue includes a description and the reason it was flagged (e.g. "CLAUDE.md adherence", "bug"). The agents should do the following:
Agents 1 + 2: CLAUDE.md compliance sonnet agents Audit changes for CLAUDE.md compliance in parallel. Note: When evaluating CLAUDE.md compliance for a file, you should only consider CLAUDE.md files that share a file path with the file or parents.
Agent 3: Opus bug agent (parallel subagent with agent 4) Scan for obvious bugs. Focus only on the diff itself without reading extra context. Flag only significant bugs; ignore nitpicks and likely false positives. Do not flag issues that you cannot validate without looking at context outside of the git diff.
Agent 4: Opus bug agent (parallel subagent with agent 3) Look for problems that exist in the introduced code. This could be security issues, incorrect logic, etc. Only look for issues that fall within the changed code.
CRITICAL: We only want HIGH SIGNAL issues. Flag issues where:
- The code will fail to compile or parse (syntax errors, type errors, missing imports, unresolved references)
- The code will definitely produce wrong results regardless of inputs (clear logic errors)
- Clear, unambiguous CLAUDE.md violations where you can quote the exact rule being broken
Do NOT flag:
- Code style or quality concerns
- Potential issues that depend on specific inputs or state
- Subjective suggestions or improvements
If you are not certain an issue is real, do not flag it. False positives erode trust and waste reviewer time.
In addition to the above, each subagent should be told the PR title and description. This will help provide context regarding the author's intent.
-
For each issue found in the previous step by agents 3 and 4, launch parallel subagents to validate the issue. These subagents should get the PR title and description along with a description of the issue. The agent's job is to review the issue to validate that the stated issue is truly an issue with high confidence. For example, if an issue such as "variable is not defined" was flagged, the subagent's job would be to validate that is actually true in the code. Another example would be CLAUDE.md issues. The agent should validate that the CLAUDE.md rule that was violated is scoped for this file and is actually violated. Use Opus subagents for bugs and logic issues, and sonnet agents for CLAUDE.md violations.
-
Filter out any issues that were not validated in step 5. This step will give us our list of high signal issues for our review.
-
If issues were found, skip to step 8 to post comments.
If NO issues were found, post a summary comment using
gh pr comment(if--commentargument is provided): "No issues found. Checked for bugs and CLAUDE.md compliance."
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 · 108 lines · 14 tokens per session scan A 3870871c3922
code-review is a skill published in the GitHub repository pipecat-ai/pipecat (16,618 stars, last pushed today), licensed BSD-2-Clause. It adds 14 tokens to every session and 1,581 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.