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-dlq-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-dlq-review)<a href="https://agentmods.dev/skills/lensesio/agentic-engineering-for-apache-kafka/kafka-dlq-review"><img src="https://agentmods.dev/badge/skills/lensesio/agentic-engineering-for-apache-kafka/kafka-dlq-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-dlq-review"><img src="https://agentmods.dev/badge/skills/lensesio/agentic-engineering-for-apache-kafka/kafka-dlq-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 | $0.00093 | $0.02240 |
| Opus 5.5 | $0.00037 | $0.00896 |
| Sonnet 5.5 | $0.00019 | $0.00448 |
| Haiku 4.5 | $0.00009 | $0.00224 |
Grade A, and why
kafka-dlq-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 — 232 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kafka Dead Letter Queue Review
Reviews dead letter queue implementations for completeness and best practices. DLQs are a cross-cutting concern that every Kafka consumer eventually needs. Most teams implement them as an afterthought and forget to monitor them.
Target environment: $ARGUMENTS
Workflow
Copy this checklist and track your progress:
DLQ Review Progress:
- [ ] Step 1: Discover DLQ topics
- [ ] Step 2: Check DLQ topic configuration
- [ ] Step 3: Verify DLQ monitoring
- [ ] Step 4: Sample DLQ messages
- [ ] Step 5: Audit connector DLQ configs
- [ ] Step 6: Scan codebase for error handling
- [ ] Step 7: Generate report with maturity assessment
- Discover DLQ topics in the cluster
- Check DLQ topic configuration (retention, partitions)
- Verify DLQ monitoring (consumers, alerting)
- Sample DLQ messages for metadata completeness
- Audit connector DLQ configs
- Scan codebase for error handling patterns
- Report findings with maturity assessment
Step 1: Discover DLQ Topics
Use the Lenses MCP list_topics tool and filter for DLQ topics by naming convention:
*.dlq*.dead-letter*.error*.errors*.retry*-dlq*-dead-letter
Also check connector configurations for DLQ topic names using list_kafka_connectors.
Expected output: List of DLQ topics discovered by naming convention and connector config.
Validation: If no DLQ topics are found, search the codebase for error topic configuration before concluding that DLQs are not implemented.
Step 2: Check DLQ Topic Configuration
For each discovered DLQ topic, use get_topic to verify:
- Warning: Retention too short (DLQ messages should be kept longer than source topics for investigation)
- Warning: Only 1 partition (may bottleneck if DLQ volume spikes)
- Warning: Replication factor < source topic (DLQ data is often more critical to preserve)
- Suggestion: Consider compaction for DLQ topics where reprocessing overwrites errors
What ships with it
1 file 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 · 232 lines · 93 tokens per session scan A 3a128f87c2fb
kafka-dlq-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 93 tokens to every session and 2,240 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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