Confluent MCP Server is an open-source Model Context Protocol server that lets AI assistants interact with Confluent Cloud, Confluent Platform, and standalone Apache Kafka through natural language. Its tools cover Kafka, Flink SQL, Schema Registry, connectors, and Tableflow. The catalogue add-ons help agents operate and configure the server.
Borrowing it
Nothing to install: this file belongs to confluentinc/mcp-confluent. 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/confluentinc/mcp-confluent/main/.claude/skills/pr-review/SKILL.mdgit clone --depth 1 https://github.com/confluentinc/mcp-confluentWrote 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/confluentinc/mcp-confluent/pr-review)<a href="https://agentmods.dev/skills/confluentinc/mcp-confluent/pr-review"><img src="https://agentmods.dev/badge/skills/confluentinc/mcp-confluent/pr-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/confluentinc/mcp-confluent/pr-review"><img src="https://agentmods.dev/badge/skills/confluentinc/mcp-confluent/pr-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 | $0.00088 | $0.04333 |
| Opus 5.5 | $0.00035 | $0.01733 |
| Sonnet 5.5 | $0.00018 | $0.00867 |
| Haiku 4.5 | $0.00009 | $0.00433 |
Grade A, and why
pr-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 — 426 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PR Review Skill
Reviews pull requests for the Confluent MCP (Model Context Protocol) server, focusing on project-specific patterns and the failure modes most likely to slip past TypeScript and lint.
Two Review Modes
The mode is selected by the invocation context, not by the user. If the user supplies a PR number/URL or asks about someone else's PR, run Formal Review Mode. Otherwise (no PR number, working from a local branch, phrases like "self-review" or "check my PR") run Self-Review Mode. When ambiguous, ask which mode to use.
Self-Review Mode (for PR authors)
Use when: the author wants to check their own changes before sharing with the team. Typically used on a draft PR or against local changes before pushing.
Goals:
- Catch issues early, before formal review
- Verify MCP tool wiring is complete (enum + handler + registry + predicate)
- Ensure new external I/O is stubbable
- Confirm OpenAPI spec and generated types stay in sync
Formal Review Mode (for reviewers)
Use when: a reviewer needs to evaluate a PR from another team member.
Goals:
- Quickly understand the scope and purpose of changes
- Identify potential issues or concerns
- Provide constructive feedback grounded in MCP/Confluent conventions
- Verify the PR template checklist is honored
Review Process
Step 1: Gather Information
For local changes (self-review):
# files changed since divergence from main
git diff main --name-only
# overview and full diff
git diff main --stat
git diff main
For GitHub PRs:
# PR metadata
gh pr view <PR_NUMBER> --json number,title,body,author,baseRefName,headRefName,additions,deletions,changedFiles,state,reviewDecision
# if no PR number is given, try the current branch
gh pr view --json number,title,body,author,baseRefName,headRefName,additions,deletions,changedFiles,state,reviewDecision
# diff
gh pr diff <PR_NUMBER>
# existing reviews and inline comments
gh pr view <PR_NUMBER> --json reviews,comments
# referenced issues
gh issue view <ISSUE_NUMBER> --json body,comments
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 · 426 lines · 88 tokens per session scan A f107b0e41905
pr-review is a skill published in the GitHub repository confluentinc/mcp-confluent (168 stars, last pushed yesterday), licensed MIT. It adds 88 tokens to every session and 4,333 once invoked, about $0.0004 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.
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