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/gemini-cli-extensions/data-agent-kit-starter-pack/building-data-appsnpx skills add gemini-cli-extensions/data-agent-kit-starter-pack --skill building-data-appsgit clone --depth 1 https://github.com/gemini-cli-extensions/data-agent-kit-starter-packWrote 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/gemini-cli-extensions/data-agent-kit-starter-pack/building-data-apps)<a href="https://agentmods.dev/skills/gemini-cli-extensions/data-agent-kit-starter-pack/building-data-apps"><img src="https://agentmods.dev/badge/skills/gemini-cli-extensions/data-agent-kit-starter-pack/building-data-apps.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 | $0.00218 | $0.01347 |
| Opus 5 | $0.00109 | $0.00674 |
| Sonnet 5 | $0.00044 | $0.00269 |
| Haiku 4.5 | $0.00022 | $0.00135 |
Grade A, and why
building-data-apps 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 4d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- building-data-apps — 94% identical, 8 lines differ
How it starts
The opening of the file, as written. The whole thing — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Building Data Applications
Architect high-quality data dashboards and interactive reports. You MUST select the appropriate framework before implementation.
Step 0: Framework Selection
You MUST select the framework based on the user's maintenance requirements and data ecosystem.
Choice: Streamlit
- User Profile: Data Scientists / Python users.
- Logic Complexity: High Python dependency (Pandas, NumPy, local data processing).
- Deployment: Single-file Python script.
- Customization: Standard layout (fast boilerplate).
Choice: React + Vite
- User Profile: Web Developers / Full-stack teams.
- Logic Complexity: High UI and Interactivity requirements (e.g., drag-and-drop, interactive maps).
- Deployment: Standalone Frontend + Backend API.
- Customization: Infinite (Custom CSS, specialized JS libraries).
Guidance:
- Check for existing stack first: ALWAYS prefer the framework the user is
already using in their project (e.g., if you see a
package.jsonwith React dependencies, use React; if you see existing Streamlit files, use Streamlit). - Default to React + Vite for production-grade applications that require complex client-side state, custom branding, or integration into a larger web ecosystem.
- Default to Streamlit if the user specifically mentions "Python dashboard", needs to iterate on complex local Python data processing, or requires a single-script deployment.
Step 1: Implementation Plan
You MUST propose a plan to the user that specifies the chosen framework and justifies the choice based on the criteria above.
Shared Design Standards
Regardless of framework, you MUST follow the principles in
references/shared_design_system.md.
- Visual Style: Minimal chrome, zinc color palette, and card-based layouts.
- Typography:
DM Sansfor content,JetBrains Monofor data.
What ships with it
7 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.
- 4d ago First seen · 158 lines · 218 tokens per session scan A a9153d270f9d
building-data-apps is a skill published in the GitHub repository gemini-cli-extensions/data-agent-kit-starter-pack (178 stars, last pushed today), licensed Apache-2.0. It adds 218 tokens to every session and 1,347 once invoked, about $0.0011 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 skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…