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 lensesio/agentic-engineering-for-apache-kafka --skill kafka-perf-reviewgit clone --depth 1 https://github.com/lensesio/agentic-engineering-for-apache-kafkaWrote 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/lensesio/agentic-engineering-for-apache-kafka/kafka-perf-review)<a href="https://agentmods.dev/skills/lensesio/agentic-engineering-for-apache-kafka/kafka-perf-review"><img src="https://agentmods.dev/badge/skills/lensesio/agentic-engineering-for-apache-kafka/kafka-perf-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/lensesio/agentic-engineering-for-apache-kafka/kafka-perf-review"><img src="https://agentmods.dev/badge/skills/lensesio/agentic-engineering-for-apache-kafka/kafka-perf-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 | $0.00082 | $0.01591 |
| Opus 5 | $0.00041 | $0.00796 |
| Sonnet 5 | $0.00016 | $0.00318 |
| Haiku 4.5 | $0.00008 | $0.00159 |
Grade A, and why
kafka-perf-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 12d 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 — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kafka Performance Configuration Review
Reviews producer and consumer configurations in both the live cluster and the codebase for performance anti-patterns. These settings are the same across all Kafka client libraries (they're Kafka protocol properties).
Target environment and path: $ARGUMENTS (defaults to src/ for codebase scan if path not specified)
Workflow
Copy this checklist and track your progress:
Performance Review Progress:
- [ ] Step 1: Inspect live cluster configs
- [ ] Step 2: Scan codebase for producer/consumer configs
- [ ] Step 3: Audit producer configs
- [ ] Step 4: Audit consumer configs
- [ ] Step 5: Cross-reference cluster and code configs
- [ ] Step 6: Generate report
- Inspect live cluster configs via Lenses MCP
- Scan codebase for producer/consumer config properties (see
references/producer-defaults.mdandreferences/consumer-defaults.md) - Audit producer configs against recommended values
- Audit consumer configs against recommended values
- Cross-reference cluster and code configs
- Report findings with current values, recommended values and trade-off explanations
Step 1: Live Cluster Inspection
Use Lenses MCP tools to check cluster-side performance configs:
get_topic- topic-level configs affecting performance (min.insync.replicas,compression.type,max.message.bytes)get_topic_broker_configs- broker-level configs (message.max.bytes,replica.fetch.max.bytes,num.io.threads)get_topic_partitions- message distribution across partitions (detect skew where one partition has significantly more bytes than others)get_dataset_message_metrics- message throughput over time to identify bottlenecks or capacity headroom
Expected output: Topic-level performance configs, partition distribution and throughput metrics.
Validation: If MCP calls fail, proceed with codebase-only analysis and note the limitation in the report.
Step 2: Codebase Inspection
Search the codebase for Kafka producer and consumer configuration properties. Consult references/producer-defaults.md for the full list of producer properties and references/consumer-defaults.md for consumer properties.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 159 lines · 82 tokens per session scan A 432560b50b22
kafka-perf-review is a skill published in the GitHub repository lensesio/agentic-engineering-for-apache-kafka (57 stars, last pushed 21d ago), licensed MIT. It adds 82 tokens to every session and 1,591 once invoked, about $0.0004 per session on Opus 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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