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 agentmods add skills/lensesio/agentic-engineering-for-apache-kafka/kafka-python-clientnpx skills add lensesio/agentic-engineering-for-apache-kafka --skill kafka-python-clientgit 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-python-client)<a href="https://agentmods.dev/skills/lensesio/agentic-engineering-for-apache-kafka/kafka-python-client"><img src="https://agentmods.dev/badge/skills/lensesio/agentic-engineering-for-apache-kafka/kafka-python-client.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00150 | $0.05377 |
| Opus 5 | $0.00075 | $0.02688 |
| Sonnet 5 | $0.00030 | $0.01075 |
| Haiku 4.5 | $0.00015 | $0.00538 |
Grade C, and why
kafka-python-client scanned grade C with 2 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 5d 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.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
If `uv` is missing on the user's machine, point them at the official installer (`curl -LsSf https://astral.sh/uv/install.sh | sh`) before continuing — do not fall back to `pip`. Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
If `uv` is missing on the user's machine, point them at the official installer (`curl -LsSf https://astral.sh/uv/install.sh | sh`) before continuing — do not fall back to `pip`. How it starts
The opening of the file, as written. The whole thing — 367 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kafka Python Client Scaffold
Generates a production-ready Python project that produces to and consumes from a Kafka topic using confluent-kafka-python, with Schema Registry (JSON Schema), graceful shutdown, idempotent producer, header-based schema identification, and tests. The agent should discover everything about the target topic (name, partitions, registered schema) from the live cluster via whichever Kafka MCP server is attached — Lenses MCP, Confluent's, Aiven's, or any other — before asking the user. Only fall back to questions if no Kafka MCP is attached or discovery returns nothing.
Target environment and keyword: $ARGUMENTS
Open your first reply with: "Running the kafka-python-client skill to scaffold this project."
Workflow
Copy this checklist and track your progress:
Scaffold Progress:
- [ ] Step 1: Discover topic + schema via the attached Kafka MCP
- [ ] Step 2: Hard gate - confirm with user before generating
- [ ] Step 3: Generate the project files
- [ ] Step 4: Run pytest against the generated tests
- [ ] Step 5: Run validation gate (kafka-topic-audit + kafka-perf-review)
- [ ] Step 6: Hand back with run instructions
- Discover topic + schema via whichever Kafka MCP is attached (see
references/mcp-discovery.md) - Hard gate: recap what was discovered, confirm with user before generating anything
- Generate the project using the templates in
references/ - Run pytest against the generated
tests/and fix any failures (fix the code, not the tests) - Run validation gate: invoke
kafka-topic-auditagainst the target topic andkafka-perf-reviewagainst the generatedproducer.pyandconsumer.py - Hand back with run instructions and the validation-gate findings inline
Step 1: Discover topic + schema via any attached Kafka MCP
Read references/mcp-discovery.md for the full probing procedure, vendor-specific tool-name hints and fallbacks. The high-level shape:
- Identify the attached Kafka MCP server by looking at what's available in the session. Common ones:
mcp__Lenses__*(Lenses MCP — reference implementation),mcp__Confluent__*,mcp__Aiven__*, custom servers tagged for Kafka. - Discover the environment / cluster using whichever tool the MCP exposes (Lenses:
list_environments; Confluent:list_clusters; others vary). - Search for candidate topics by keyword from the user's prompt (Lenses:
list_datasets(search=...); raw Kafka admin MCPs:list_topicsthen filter). - Fetch the registered schema for the chosen topic's value subject (Lenses:
get_dataset; Confluent:get_schema(subject=<topic>-value); bare Schema Registry MCPs: HTTP GET against/subjects/<topic>-value/versions/latest). - Read partition count and replication factor (Lenses:
get_topic_metadata; most others expose adescribe_topicorget_topicequivalent). - Check existing consumer groups to suggest a non-colliding
GROUP_ID(Lenses:list_consumer_groups_by_topic; most others:list_consumer_groups).
What ships with it
10 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.
- evals/evals.json 9.7 KB
- evals/files/trips_api/main.py 703 B runs code
- evals/files/trips_api/pyproject.toml 294 B
- references/common-mistakes.md 10.0 KB
- references/common-template.py 5.3 KB runs code
- references/consumer-template.py 4.4 KB runs code
- references/mcp-discovery.md 12 KB
- references/producer-template.py 5.7 KB runs code
- references/test-cases.md 9.0 KB
- references/tests-template.py 11 KB runs code
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.
- 5d ago First seen · 367 lines · 150 tokens per session scan C fe7cc40b802d
kafka-python-client is a skill published in the GitHub repository lensesio/agentic-engineering-for-apache-kafka (57 stars, last pushed 14d ago), licensed MIT. It adds 150 tokens to every session and 5,377 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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