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/mrlm-xyz/demo-claude-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/mrlm-xyz/demo-claude-marketplace/ingest)<a href="https://agentmods.dev/commands/mrlm-xyz/demo-claude-marketplace/ingest"><img src="https://agentmods.dev/badge/commands/mrlm-xyz/demo-claude-marketplace/ingest/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/mrlm-xyz/demo-claude-marketplace/ingest"><img src="https://agentmods.dev/badge/commands/mrlm-xyz/demo-claude-marketplace/ingest.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 | $0.00015 | $0.00240 |
| Opus 5 | $0.00008 | $0.00120 |
| Sonnet 5 | $0.00003 | $0.00048 |
| Haiku 4.5 | $0.00002 | $0.00024 |
Grade A, and why
ingest 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 13d 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
Ingest Command
Build a complete data ingestion pipeline with extraction, validation, transformation, and loading stages.
Scope
This command creates production-ready ingestion code that:
- Connects to data sources (databases, APIs, files)
- Validates data quality and schema compliance
- Transforms data to target schema
- Loads data into destination with error handling
- Logs metrics and errors for monitoring
Execution Flow
- Analyze source schema and data characteristics
- Design target schema and transformation logic
- Implement extraction with retry and timeout handling
- Add validation rules for data quality
- Build transformation pipeline with error handling
- Implement loading with transaction management
- Add comprehensive logging and metrics
- Create tests with realistic data samples
Constraints
- Use existing data-toolkit skills for implementation
- Follow idempotent design patterns
- Include data quality checks at each stage
- Add monitoring hooks for observability
- Document data lineage and assumptions
- Handle partial failures gracefully
- Provide rollback capabilities
- Test with production-scale data samples
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.
- 13d ago First seen · 39 lines · 15 tokens per session scan A 5552f0910691
ingest is a command published in the GitHub repository mrlm-xyz/demo-claude-marketplace (34 stars, last pushed 10d ago), licensed Apache-2.0. It adds 15 tokens to every session and 240 once invoked, about $0.0001 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.
Other commands, from other repositories
music-suno-prompt
Grounded Suno prompt synthesis from local knowledge corpus + persona canon + label canon. No vibes-prompting.
laravel-ai-sdk
Build AI features with the first-party Laravel AI SDK (Laravel 13+); use the laravel:ai-sdk skill exactly as written.
audit-prompt
Evaluate an existing prompt for clarity, effectiveness, and edge cases.
develop-image-prompt.eval
Generates a detailed image generation prompt from a document or content description. Good output: a prompt that is specific, visual, non-abstract, includes style/composition/lighting guidance, and is calibrated to the specified dimensions and style options.
dare-llm-integration
Integração segura e eficiente com LLMs (Gemini, Claude, OpenAI, Ollama) em projetos DARE.
prompt-create
Create a new prompt following ground rules.