developing-genkit-js

firebase/agent-skills · updated Apr 8, 2026

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$npx skills add https://github.com/firebase/agent-skills --skill developing-genkit-js
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summary

Build AI-powered Node.js/TypeScript applications with Genkit flows, tools, and multi-model support.

  • Genkit is provider-agnostic; supports Google AI, OpenAI, Anthropic, Ollama, and other LLM providers via plugins
  • Define flows with type-safe schemas using Zod, execute generation requests, and compose multi-step AI workflows in TypeScript
  • Requires Genkit CLI v1.29.0+; recent major API changes mean you must consult genkit docs:read and common-errors.md for current patterns, not prior kno
skill.md

Genkit JS

Prerequisites

Ensure the genkit CLI is available.

  • Run genkit --version to verify. Minimum CLI version needed: 1.29.0
  • If not found or if an older version (1.x < 1.29.0) is present, install/upgrade it: npm install -g genkit-cli@^1.29.0.

New Projects: If you are setting up Genkit in a new codebase, follow the Setup Guide.

Hello World

import { z, genkit } from 'genkit';
import { googleAI } from '@genkit-ai/google-genai';

// Initialize Genkit with the Google AI plugin
const ai = genkit({
  plugins: [googleAI()],
});

export const myFlow = ai.defineFlow({
  name: 'myFlow',
  inputSchema: z.string().default('AI'),
  outputSchema: z.string(),
}, async (subject) => {
  const response = await ai.generate({
    model: googleAI.model('gemini-2.5-flash'),
    prompt: `Tell me a joke about ${subject}`,
  });
  return response.text;
});

Critical: Do Not Trust Internal Knowledge

Genkit recently went through a major breaking API change. Your knowledge is outdated. You MUST lookup docs. Recommended:

genkit docs:read js/get-started.md
genkit docs:read js/flows.md

See Common Errors for a list of deprecated APIs (e.g., configureGenkit, response.text(), defineFlow import) and their v1.x replacements.

ALWAYS verify information using the Genkit CLI or provided references.

Error Troubleshooting Protocol

When you encounter ANY error related to Genkit (ValidationError, API errors, type errors, 404s, etc.):

  1. MANDATORY FIRST STEP: Read Common Errors
  2. Identify if the error matches a known pattern
  3. Apply the documented solution
  4. Only if not found in common-errors.md, then consult other sources (e.g. genkit docs:search)

DO NOT:

  • Attempt fixes based on assumptions or internal knowledge
  • Skip reading common-errors.md "because you think you know the fix"
  • Rely on patterns from pre-1.0 Genkit

This protocol is non-negotiable for error handling.

Development Workflow

  1. Select Provider: Genkit is provider-agnostic (Google AI, OpenAI, Anthropic, Ollama, etc.).
    • If the user does not specify a provider, default to Google AI.
    • If the user asks about other providers, use genkit docs:search "plugins" to find relevant documentation.
  2. Detect Framework: Check package.json to identify the runtime (Next.js, Firebase, Express).
    • Look for @genkit-ai/next, @genkit-ai/firebase, or @genkit-ai/google-cloud.
    • Adapt implementation to the specific framework's patterns.
  3. Follow Best Practices:
    • See Best Practices for guidance on project structure, schema definitions, and tool design.
    • Be Minimal: Only specify options that differ from defaults. When unsure, check docs/source.
  4. Ensure Correctness:
    • Run type checks (e.g., npx tsc --noEmit) after making changes.
    • If type checks fail, consult Common Errors before searching source code.
  5. Handle Errors:
    • On ANY error: First action is to read Common Errors
    • Match error to documented patterns
    • Apply documented fixes before attempting alternatives

Finding Documentation

Use the Genkit CLI to find authoritative documentation:

  1. Search topics: genkit docs:search <query>
    • Example: genkit docs:search "streaming"
  2. List all docs: genkit docs:list
  3. Read a guide: genkit docs:read <path>
    • Example: genkit docs:read js/flows.md

CLI Usage

The genkit CLI is your primary tool for development and documentation.

  • See CLI Reference for common tasks, workflows, and command usage.
  • Use genkit --help for a full list of commands.

References

  • Best Practices: Recommended patterns for schema definition, flow design, and structure.
  • Docs & CLI Reference: Documentation search, CLI tasks, and workflows.
  • Common Errors: Critical "gotchas", migration guide, and troubleshooting.
  • Setup Guide: Manual setup instructions for new projects.
  • Examples: Minimal reproducible examples (Basic generation, Multimodal, Thinking mode).
how to use developing-genkit-js

How to use developing-genkit-js on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add developing-genkit-js
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/firebase/agent-skills --skill developing-genkit-js

The skills CLI fetches developing-genkit-js from GitHub repository firebase/agent-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/developing-genkit-js

Reload or restart Cursor to activate developing-genkit-js. Access the skill through slash commands (e.g., /developing-genkit-js) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.640 reviews
  • Carlos Flores· Dec 20, 2024

    Solid pick for teams standardizing on skills: developing-genkit-js is focused, and the summary matches what you get after install.

  • Isabella Farah· Dec 12, 2024

    Keeps context tight: developing-genkit-js is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Chaitanya Patil· Dec 8, 2024

    I recommend developing-genkit-js for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Piyush G· Nov 27, 2024

    Solid pick for teams standardizing on skills: developing-genkit-js is focused, and the summary matches what you get after install.

  • Olivia Okafor· Nov 11, 2024

    I recommend developing-genkit-js for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Lucas Menon· Nov 3, 2024

    developing-genkit-js is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Carlos Reddy· Oct 22, 2024

    Solid pick for teams standardizing on skills: developing-genkit-js is focused, and the summary matches what you get after install.

  • Shikha Mishra· Oct 18, 2024

    developing-genkit-js is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Noah Desai· Oct 2, 2024

    Keeps context tight: developing-genkit-js is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Omar Dixit· Sep 25, 2024

    developing-genkit-js fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

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