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 5.5 | $0.00033 | $0.00636 |
| Sonnet 5.5 | $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 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 — 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.
- 1mo 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 (65 stars, last pushed 2mo ago), licensed MIT. It adds 82 tokens to every session and 1,591 once invoked, about $0.0003 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
engineering-skills
23 skills de engenharia prontas para produção para Claude Code. Arquitetura, frontend, backend, QA, DevOps, segurança, IA/ML, engenharia de dados, Playwright, Stripe, AWS, MS365. ferramentas Node.js, TypeScript e scripts utilitários.
review-api-compat
Use when the user asks for an API compatibility review, breaking change review, proto breaking change review, buf breaking review, OpenAPI compatibility check, gRPC backwards compatibility audit, "are these API changes breaking", "did we break the wire", contract evolution review, or backwards-compatibility audit on…
review-reliability
Use when the user asks for a reliability review, production readiness assessment, stability analysis, graceful shutdown audit, drain or rollout review, timeout review, timeout budget check, deadline propagation review, circuit breaker or bulkhead review, retry and backoff review, goroutine leak or resource leak…
api-design-reviewer
Review REST API designs for quality, consistency, and breaking changes. Lints OpenAPI specs, generates scorecards, and detects breaking changes between versions. Use when designing APIs, reviewing contracts, or managing API versioning.
graphql-contract-review
Review a GraphQL schema and a Backend-for-Frontend (BFF) contract BEFORE implementation against a GraphQL checklist (nullability, N+1, cost limits, federation, BFF thinness). Use when designing or modifying a GraphQL schema, a federated subgraph, or a BFF contract. Do not use for a REST contract (use…
review-code
Use when the user asks for a code review, architecture review, Go review, Go best practices review, protobuf review, proto or API design review, SOLID review, design pattern review, package design review, coupling or cohesion review, testability review, "review this code", "is this well designed", "is this idiomatic…