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 excalimate/excalimate --skill data-pipelinesgit clone --depth 1 https://github.com/excalimate/excalimateWrote 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/excalimate/excalimate/data-pipelines)<a href="https://agentmods.dev/skills/excalimate/excalimate/data-pipelines"><img src="https://agentmods.dev/badge/skills/excalimate/excalimate/data-pipelines/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/excalimate/excalimate/data-pipelines"><img src="https://agentmods.dev/badge/skills/excalimate/excalimate/data-pipelines.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 | $0.00076 | $0.02669 |
| Opus 5.5 | $0.00030 | $0.01068 |
| Sonnet 5.5 | $0.00015 | $0.00534 |
| Haiku 4.5 | $0.00008 | $0.00267 |
Grade A, and why
data-pipelines 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 — 311 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Pipelines Agent Skill
This skill specializes in creating animated data pipeline and ETL flow diagrams using the Excalimate MCP server.
When to Use
Use this skill when users ask to visualize:
- Data pipelines and ETL/ELT processes
- Streaming architectures (Kafka, RabbitMQ, SQS)
- Data warehousing flows
- ML pipelines and data science workflows
- Event streaming topologies
- Data transformation workflows
- Any scenario involving "data flow" or "how the data moves"
Workflow
- Identify Components: Sources, transforms, sinks, queues, routers
- Choose Topology: Linear pipeline, fan-out, fan-in, diamond, or streaming
- Layout Left-to-Right: Sources → Processing → Sinks with 350px stage separation
- Color-Code by Role: Green sources, blue transforms, purple sinks, etc.
- Animate Left-to-Right: Stage-by-stage reveal from sources to sinks
Components
All components use consistent sizing and include boundElements for labels:
Data Source (Green)
{
"type": "rectangle",
"width": 160,
"height": 70,
"strokeColor": "#2f9e44",
"backgroundColor": "#b2f2bb",
"boundElements": [{"id": "label-id", "type": "text"}]
}
Transform (Blue)
{
"type": "rectangle",
"width": 180,
"height": 80,
"strokeColor": "#1971c2",
"backgroundColor": "#a5d8ff",
"boundElements": [{"id": "label-id", "type": "text"}]
}
Sink (Purple)
{
"type": "rectangle",
"width": 160,
"height": 70,
"strokeColor": "#6741d9",
"backgroundColor": "#d0bfff",
"boundElements": [{"id": "label-id", "type": "text"}]
}
Queue/Stream (Orange)
{
"type": "rectangle",
"width": 200,
"height": 50,
"strokeColor": "#f08c00",
"backgroundColor": "#ffec99",
"boundElements": [{"id": "label-id", "type": "text"}]
}
Router (Teal Diamond)
{
"type": "diamond",
"width": 140,
"height": 120,
"strokeColor": "#0c8599",
"backgroundColor": "#99e9f2",
"boundElements": [{"id": "label-id", "type": "text"}]
}
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 · 311 lines · 76 tokens per session scan A b79bc3e38138
data-pipelines is a skill published in the GitHub repository excalimate/excalimate (95 stars, last pushed yesterday), licensed MIT. It adds 76 tokens to every session and 2,669 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
archscribe
A tool for turning technical descriptions, articles, processes, or reference images into hand-drawn-style diagrams. It can produce editable Excalidraw diagrams, image and video previews, and interactive HTML.
animated-sketch-diagram
A skill for generating animated technical diagrams in a hand-drawn sketch style. It produces a self-contained HTML file with an SVG diagram, animated connections and icons, and an option to export a looping GIF.
open-source
Documentation reference for writing Python code using the browser-use open-source library. Use this skill whenever the user needs help with Agent, Browser, or Tools configuration, is writing code that imports from browseruse, asks about @sandbox deployment, supported LLM models, Actor API, custom tools, lifecycle…
omh-model-optimization
This is a Hermes-native model-optimization workflow skill.
pinme-llm
Use this skill when a PinMe project (Worker TypeScript) needs to call OpenRouter-backed LLM APIs, including models, chat/completions, streaming, or OpenRouter web search. Guides AI to generate correct Worker TS code.
recommendation-algorithm-design
Complete mastery guide for designing social media recommendation algorithms — understanding candidate generation, ranking models, engagement prediction, content graph modeling, collaborative filtering, embedding spaces, real-time personalization, exploration vs exploitation tradeoffs, feedback loops, filter bubble…