building-data-apps
Build modern data apps, dashboards, and interactive reports using either React + Vite or Streamlit.
Skill by jerrylin96
Chalk Labs
Need help deploying building-data-apps?
Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/jerrylin96-building-data-apps
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
Get the weekly diff
The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.
About
Use cases: design, frontend, backend, cloud
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
- Runs as
- Python package
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/building-data-apps
What's inside
- Skill name
building-data-apps- Description
- Build modern data apps, dashboards, and interactive reports using either React + Vite or Streamlit. Includes optional Gemini Data Analytics chat integration for an AI powered "chat with your data" experience. Relevant when any of the following conditions are true: 1. User explicitly requests to build a data dashboard, data application, or visualization UI, and the UI pulls data from a GCP database (defaulting to BigQuery unless otherwise specified). 2. You need to generate a frontend web application to interact with, query, and visualize data from GCP data sources. 3. User wants to build a "chat with your data" experience or integrate the Gemini Data Analytics chat API into a web interface. Do NOT use when any of the following conditions are true: 1. The request is for building backend-only services. 2. The request is for simple CLI scripts or command-line applications. 3. The web application is not data-centric or does not involve visualizing/querying data from GCP sources.
- License
- Apache-2.0
- Metadata
- version: v1 · publisher: google
- Instructions
- 137 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
Agent Skills spec issues
- files: referenced but missing: references/shared_design_system.md, references/streamlit_framework.md, references/react_framework.md, references/chat_integration.md
Files (8)
- SKILL.md
- express_chat.ts · examples/
- fastapi_chat.py · examples/
- react_chat_panel.jsx · examples/
- chat_integration.md · resources/
- react_framework.md · resources/
- shared_design_system.md · resources/
- streamlit_framework.md · resources/
SKILL.md (first lines)
# 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.json` with 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 Sans` for content, `JetBrains Mono` for data.
--------------------------------------------------------------------------------
## Framework Implementation
### If using Streamlit:
1. Read `references/streamlit_framework.md` for detailed CSS and component
patterns.
2. Follow the "Checklist for New Dashboards" in that file.
### If using React + Vite:
1. Read `references/react_framework.md` for Tailwind and ECharts setup.
2. Follow the detailed component guidelines for KPI cards, Tables, and Panels.
--------------------------------------------------------------------------------
## AI Chat Interface (Optional Feature)
```
> [!IMPORTANT]
>
> If the user does not explicitly request a chat interface, you SHOULD
> proactively ask them: "Would you like to include a Gemini-powered chat
> interface to enable natural language queries against your data?" OR if
> there is an implementation plan: "Would you like to include a
> Gemini-powered chat interface to enable natural language queries against
> your data? Let me know and I'll update the plan!".
```
If the user requests or agrees to the chat interface:
```
> [!CAUTION]
>
> Adding the chat interface is a significant change. Implicit approval of
> the implementation plan for including the chat interface MUST never be
> assumed.
```
1. **Gather Technical Details**: You MUST read `references/chat_integration.md`
for the technical requirements.
2. **Update the implementation plan**: If and only if there is an
implementation plan, you MUST update the implementation plan. This is a
significant change so the user must explicitly approve the updated plan.
3. **Verify Prerequisites**: Ensure the user has the Gemini Data Analytics API
enabled and data exists in BigQuery.
4. **Reference Examples**: Adapt the patterns in
`examples/react_chat_panel.jsx` and either `examples/fastapi_chat.py` or
`examples/express_chat.ts`.
## Acceptance Criteria
> [!CAUTION]
>
> If available, you MUST use browser testing capabilities (such as
> `browser_subagent`, Puppeteer, Playwright, or an equivalent available tool) to
> visually verify the frontend application is working correctly *before*
> notifying the user that the task is complete.
> [!IMPORTANT]
>
> The following checklist represents the strict requirements for this task. You
> must include these items in whatever format you use to track your work (e.g.,From jerrylin96/dotgemini, Apache-2.0.
More from jerrylin96/dotgemini
- 0.585score
codebase-audit
jerrylin96
Multi-agent adversarial codebase review and deep audit across functional clusters with cross-boundary contract validation and blast-radius scorecards.
SkillBFreshness: B★ 9Claude Code +9 - 0.585score
codebase-map
jerrylin96
Multi-agent codebase exploration and architectural mapping engine across functional clusters with polyglot entrypoint discovery and intent-guided scoping.
SkillBFreshness: B★ 9Claude Code +9 - 0.581score
adversarial-review
jerrylin96
Multi-stage adversarial review of specifications, plans, RED tests, and code worktrees to catch edge cases, non-negotiables, and bugs across the software…
SkillBFreshness: B★ 9Claude Code +9 - 0.580score
explain-diff
jerrylin96
Interactive, neutral diff explanation walkthrough (overall summary, topic-by-topic, per-hunk, and commit-by-commit walkthroughs, Q&A).
SkillBFreshness: B★ 9Claude Code +9 - 0.543score
bigquery-data-transfer-service
jerrylin96
Discovers and inspects BigQuery Data Transfer Service (DTS) configurations.
SkillCFreshness: C★ 9Claude Code +9 - 0.540score
data-autocleaning
jerrylin96
Automated data quality and transformation capabilities for Dataform/dbt/BigQuery pipelines.
SkillCFreshness: C★ 9Claude Code +9 - 0.538score
gcp-data-pipelines
jerrylin96
Primary entry point for building, managing, and orchestrating data pipelines on Google Cloud.
SkillCFreshness: C★ 9Claude Code +9 - 0.538score
dataform-bigquery
jerrylin96
Expertise in generating clean, correct, and efficient Dataform pipeline code for BigQuery ELT.
SkillCFreshness: C★ 9Claude Code +9 - 0.538score
dbt-bigquery
jerrylin96
Expert guidance for creating, modifying, and optimizing dbt pipelines for BigQuery.
SkillCFreshness: C★ 9Claude Code +9 - 0.538score
discovering-gcp-data-assets
jerrylin96
Finds and inspects data assets within Google Cloud.
SkillCFreshness: C★ 9Claude Code +9 - 0.538score
federate-lakehouse-catalog
jerrylin96
Sets up Google Cloud Lakehouse federated catalogs to remote Iceberg REST Catalogs.
SkillCFreshness: C★ 9Claude Code +9 - 0.538score
gcp-composer-troubleshooting
jerrylin96
Provides expert guidance for troubleshooting Cloud Composer (Apache Airflow) and Orchestration pipelines.
SkillCFreshness: C★ 9Claude Code +9
Signals
- Stars
- 9
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Jul 9, 2026 (3 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- Version
- v1
- License
- Apache-2.0
- Repository
- github.com/jerrylin96/dotgemini
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
Same skill elsewhere
Copies of this SKILL.md published by others; search shows only one of them.
More from jerrylin96
- 0.585score
codebase-audit
jerrylin96
Multi-agent adversarial codebase review and deep audit across functional clusters with cross-boundary contract validation and blast-radius scorecards.
SkillBFreshness: B★ 9Claude Code +9 - 0.585score
codebase-map
jerrylin96
Multi-agent codebase exploration and architectural mapping engine across functional clusters with polyglot entrypoint discovery and intent-guided scoping.
SkillBFreshness: B★ 9Claude Code +9 - 0.581score
adversarial-review
jerrylin96
Multi-stage adversarial review of specifications, plans, RED tests, and code worktrees to catch edge cases, non-negotiables, and bugs across the software…
SkillBFreshness: B★ 9Claude Code +9 - 0.580score
explain-diff
jerrylin96
Interactive, neutral diff explanation walkthrough (overall summary, topic-by-topic, per-hunk, and commit-by-commit walkthroughs, Q&A).
SkillBFreshness: B★ 9Claude Code +9 - 0.543score
bigquery-data-transfer-service
jerrylin96
Discovers and inspects BigQuery Data Transfer Service (DTS) configurations.
SkillCFreshness: C★ 9Claude Code +9 - 0.540score
data-autocleaning
jerrylin96
Automated data quality and transformation capabilities for Dataform/dbt/BigQuery pipelines.
SkillCFreshness: C★ 9Claude Code +9
Done for you
Rather not set it up yourself?
Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.
See how we build AI agents →Read the case studies
Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.
- launches shipped
- 50+
- launches shipped
- media placements
- 500+
- media placements
- founders on every account
- 2
- founders on every account
Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block
Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
Get building-data-apps running in your business
Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.
Cite this page
building-data-apps skill. Agent Mart. https://agentmart.dev/skills/jerrylin96-building-data-apps (last updated 2026-10-24)