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.
git clone --depth 1 https://github.com/dotclaude/marketplaceWrote 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/commands/dotclaude/marketplace/etl)<a href="https://agentmods.dev/commands/dotclaude/marketplace/etl"><img src="https://agentmods.dev/badge/commands/dotclaude/marketplace/etl/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/commands/dotclaude/marketplace/etl"><img src="https://agentmods.dev/badge/commands/dotclaude/marketplace/etl.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00009 | $0.00118 |
| Opus 5.5 | $0.00004 | $0.00047 |
| Sonnet 5.5 | $0.00002 | $0.00024 |
| Haiku 4.5 | $0.00001 | $0.00012 |
Grade A, and why
etl 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.
What it actually says
Etl Command
ETL/ELT pipeline design and implementation
Arguments
$1 (Required): requirement
$2 (Optional): pattern
Examples
/etl "Design data ingestion pipeline" batch
/etl "Build real-time processing" streaming
Invoke the streaming-architect agent with: $ARGUMENTS
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 · 26 lines · 9 tokens per session scan A 056e42b77bbb
etl is a command published in the GitHub repository dotclaude/marketplace (43 stars, last pushed 6mo ago), licensed MIT. It adds 9 tokens to every session and 118 once invoked, about $0.0000 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 commands, from other repositories
suggest
Suggest the matching Convex component when the user hand-rolls a pattern it already solves (crons, sharded-counter, rate-limiter, storage, search, presence, workflow, RAG, prosemirror-sync). Passive — suggest after the task, never interrupt. Never install without consent.
migrate-airbyte-connector
Migrate an Airbyte source connector to Connector SDK.
ds-package
Package a model as a servable unit and prove prediction parity.
abap2ui5-add-ai
Add an AI / LLM feature to an existing abap2UI5 app - a chat or a summarize-this-data panel, on a local provider first, then on a real model through the samples-stack LLM classes, with secrets kept out of the code.
dare-llm-integration
Integração segura e eficiente com LLMs (Gemini, Claude, OpenAI, Ollama) em projetos DARE.
llm-api-scaffold
Generate production-ready LLM API integration boilerplate.