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Agent skill

Genkit Production Expert

by jeremylongshore in jeremylongshore/tons-of-skills-marketplace

Build production Firebase Genkit applications including RAG systems, multi-step flows, and tool calling for Node.js/Python/Go.

MITAuto-check passedAI & LLM Engineering

Install Genkit Production Expert

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill genkit-production-expert -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace genkit-production-expert --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/genkit-production-expert .claude/skills/genkit-production-expert && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
genkit-production-expert
GitHub stars
2.8k
Token cost
~1.5k tokens
SKILL.md length
648 words
Files
9 (incl. scripts, references)
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Build production Firebase Genkit applications including RAG systems, multi-step flows, and tool calling for Node.js/Python/Go.

  • Works in 10 steps: Analyze the requirements to determine… → Initialize the project structure with… → Install Genkit core, provider plugins,… → …
  • Asked to create genkit flow
  • SKILL.md covers Overview, Prerequisites, Instructions and Output, plus 3 more sections
  • Runs Shell scripts from its folder; calls npm

What it does

Genkit Production Expert is an agent skill from jeremylongshore/tons-of-skills-marketplace. Build production Firebase Genkit applications including RAG systems, multi-step flows, and tool calling for Node.js/Python/Go. Deploy to Firebase Functions or Cloud Run with AI monitoring. Use when asked to "create genkit flow" or "implement RAG". Trigger with relevant phrases based on skill purpose.

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including scripts and reference files (for example `references/ARD.md`, `references/PRD.md` and `references/errors.md`). Compatibility notes: Designed for Claude Code

It sits in AI & LLM Engineering, covering Structured output and tool calling and Retrieval-augmented generation. It works with Firebase, Python, Node.js and Cloud Run. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.

When your agent uses it

  • Asked to create genkit flow
  • With relevant phrases based on skill purpose

Example prompts

  • “create genkit flow”
  • “implement RAG”
  • “/genkit-production-expert”

Requirements

  • Python 3
  • Node.js
  • A Bash shell
  • Compatibility (from SKILL.md): Designed for Claude Code
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Grep, Glob, Bash(cmd:*)

Workflow steps

10 steps, taken from the first numbered list in SKILL.md.

  1. Analyze the requirements to determine target language, flow complexity (simple, multi-step, or RAG), model selection (Gemini 2.5 Flash vs…
  2. Initialize the project structure with appropriate config files (tsconfig.json, genkit.config.ts, or equivalent)
  3. Install Genkit core, provider plugins, and schema validation dependencies
  4. Define input/output schemas using Zod, Pydantic, or Go structs to enforce type safety at runtime
  5. Implement the Genkit flow using ai.defineFlow() with model configuration, temperature tuning, and token limits
  6. Add tool definitions using ai.defineTool() with scoped schemas for each external capability the flow requires
  7. For RAG flows: implement a retriever using ai.defineRetriever() with embedding generation (text-embedding-gecko) and vector database…
  8. Configure error handling for safety blocks (SAFETY_BLOCK), quota exceeded (QUOTA_EXCEEDED), and provider timeouts
  9. Enable OpenTelemetry tracing with custom span attributes for cost and latency tracking
  10. Test locally using the Genkit Developer UI, then deploy to Firebase Functions or Cloud Run with auto-scaling configuration

What it can do on your machine

Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Grep
    • Glob
    • Bash(cmd:*)

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Shell), which the agent can run.

    Shell commands in SKILL.md call:

    • npm

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • firebase.google.com
    • github.com
    • zod.dev
    • opentelemetry.io

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Designed for Claude Code

    From compatibility in the SKILL.md frontmatter.

Context cost

Genkit Production Expert loads about 1.5k tokens when it runs, and up to ~9.7k if it reads all its reference files. Until then it costs about 82 tokens; SKILL.md has 648 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~82
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~9.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 648 words, ~1,506 tokens.

Download SKILL.mdSave it as .claude/skills/genkit-production-expert/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.
name
genkit-production-expert
description
Build production Firebase Genkit applications including RAG systems, multi-step flows, and tool calling for Node.js/Python/Go. Deploy to Firebase Functions or Cloud Run with AI monitoring. Use when asked to "create genkit flow" or "implement RAG". Trigger with relevant phrases based on skill purpose.
allowed-tools
Read, Write, Edit, Grep, Glob, Bash(cmd:*)
compatibility
Designed for Claude Code
version
2.26.0
author
Jeremy Longshore <jeremy@intentsolutions.io>
license
MIT
effort
medium
tags
ai, deployment, monitoring, python

Genkit Production Expert

Overview

Build production-grade Firebase Genkit applications including RAG systems, multi-step flows, and tool-calling agents for Node.js, Python, and Go. This skill covers the full lifecycle from project scaffolding and schema validation through flow implementation, local testing with the Genkit Developer UI, and deployment to Firebase Functions or Cloud Run with AI monitoring and OpenTelemetry tracing.

Prerequisites

  • Node.js 18+ (TypeScript), Python 3.10+ (Python), or Go 1.21+ (Go) runtime
  • Genkit CLI and core packages (npm install genkit @genkit-ai/googleai for TypeScript)
  • Google Cloud project with Vertex AI API enabled for Gemini model access
  • Firebase CLI for Firebase Functions deployments (npm install -g firebase-tools)
  • Zod (TypeScript), Pydantic (Python), or Go structs for input/output schema validation
  • Environment variables configured for API keys (never hardcoded; use Secret Manager)

Instructions

  1. Analyze the requirements to determine target language, flow complexity (simple, multi-step, or RAG), model selection (Gemini 2.5 Flash vs Pro), and deployment target
  2. Initialize the project structure with appropriate config files (tsconfig.json, genkit.config.ts, or equivalent)
  3. Install Genkit core, provider plugins, and schema validation dependencies
  4. Define input/output schemas using Zod, Pydantic, or Go structs to enforce type safety at runtime
  5. Implement the Genkit flow using ai.defineFlow() with model configuration, temperature tuning, and token limits
  6. Add tool definitions using ai.defineTool() with scoped schemas for each external capability the flow requires
  7. For RAG flows: implement a retriever using ai.defineRetriever() with embedding generation (text-embedding-gecko) and vector database integration
  8. Configure error handling for safety blocks (SAFETY_BLOCK), quota exceeded (QUOTA_EXCEEDED), and provider timeouts
  9. Enable OpenTelemetry tracing with custom span attributes for cost and latency tracking
  10. Test locally using the Genkit Developer UI, then deploy to Firebase Functions or Cloud Run with auto-scaling configuration

See ${CLAUDE_SKILL_DIR}/references/how-it-works.md for the phased workflow and ${CLAUDE_SKILL_DIR}/references/production-best-practices-applied.md for the production checklist.

Output

  • Complete Genkit flow implementation with typed schemas and model bindings
  • Tool definitions with Zod/Pydantic-validated inputs and outputs
  • Retriever configuration for RAG flows (embeddings, vector search, context injection)
  • Deployment configuration: Firebase Functions (firebase.json) or Cloud Run service YAML
  • Monitoring setup: OpenTelemetry tracing, Firebase Console integration, alert policies
  • Cost optimization report: model selection rationale, token usage estimates, caching strategy
Show full SKILL.md (295 more words)Show less

Error Handling

ErrorCauseSolution
SAFETY_BLOCK responseModel safety filters triggered on input or outputReview prompt content; adjust safety settings; add input sanitization before generation
QUOTA_EXCEEDEDAPI rate limit or daily token quota reachedImplement exponential backoff with jitter; request quota increase; cache repeated prompts
Schema validation failureRuntime input does not match Zod/Pydantic schemaAdd descriptive error messages to schema; validate inputs before calling ai.generate()
Retriever returns empty resultsVector database query found no matches above similarity thresholdLower similarity threshold; verify embeddings are indexed; check embedding model version match
Deployment timeoutCold start exceeds Firebase Functions 60s limitIncrease memory allocation; use Cloud Run for long-running flows; enable min instances > 0

See ${CLAUDE_SKILL_DIR}/references/errors.md for additional error scenarios.

Examples

Scenario 1: Question-Answering Flow -- Create a Genkit flow using Gemini 2.5 Flash with Zod input/output schemas. Set temperature to 0.3 for factual responses. Deploy to Firebase Functions with token usage monitoring. Expected latency: under 2 seconds per query.

Scenario 2: RAG Document Search -- Implement a retriever with text-embedding-gecko embeddings connected to Firestore vector search. Build a RAG flow that retrieves top-5 relevant documents, injects them as context, and generates grounded answers with source citations. Include context caching for repeated queries.

Scenario 3: Multi-Tool Agent -- Define weather and calendar tools with typed schemas. Create an agent flow that routes user queries to appropriate tools, handles multi-turn conversations, and traces each tool execution for debugging. Deploy to Cloud Run with auto-scaling (2-10 instances).

See ${CLAUDE_SKILL_DIR}/references/workflow-examples.md for complete code examples.

Resources

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 8 other files (scripts, references) in skills/.curated/genkit-production-expert of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • references/ARD.md
  • references/PRD.md
  • references/errors.md
  • references/examples.md
  • references/how-it-works.md
  • references/production-best-practices-applied.md
  • references/workflow-examples.md
  • scripts/init-genkit.sh

Open the folder on GitHubat commit cfae287

Compare with similar skills

Genkit Production Expert next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Genkit Production Expert compared with similar skills
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Genkit Production Expert this skilljeremylongshore/tons-of-skills-marketplace2.8k—~1.5kAutomated safety check: PassMIT
Claude Cookbooks Reference2025Emma/vibe-coding-cn23k1 repos~2.2kAutomated safety check: PassMIT
Gemini API Devgoogle-gemini/gemini-skills4.3k—~5.1kAutomated safety check: PassApache-2.0
Agent Squad for TypeScript2FastLabs/agent-squad7.8k—~4.3kAutomated safety check: PassApache-2.0
Retail Product Search Agentgoogle/adk-recipes10k—~3kAutomated safety check: PassApache-2.0
Azure Openai To Responsesmicrosoft/ai-agents-for-beginners77k—~6kAutomated safety check: NotesMIT

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Works with

Categories

Questions about Genkit Production Expert

What does Genkit Production Expert do?

Build production Firebase Genkit applications including RAG systems, multi-step flows, and tool calling for Node.js/Python/Go. Genkit Production Expert is an agent skill from jeremylongshore/tons-of-skills-marketplace.js/Python/Go.

When should I use Genkit Production Expert?

Genkit Production Expert fits situations like: asked to create genkit flow; with relevant phrases based on skill purpose.

How do I install Genkit Production Expert in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill genkit-production-expert -a claude-code`. Or copy the skill folder (skills/.curated/genkit-production-expert in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/genkit-production-expert in your project. Claude Code loads it when a task matches its description.

How do I install Genkit Production Expert in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill genkit-production-expert -a codex`. Or copy the skill folder (skills/.curated/genkit-production-expert in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/genkit-production-expert in your project. Codex loads it when a task matches its description.

Can I use Genkit Production Expert in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill genkit-production-expert -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/genkit-production-expert, .gemini/skills/genkit-production-expert, .github/skills/genkit-production-expert and .opencode/skills/genkit-production-expert in your project.

What does Genkit Production Expert need to run?

Going by SKILL.md and its folder, Genkit Production Expert needs a shell for the scripts in its folder and the command-line tools its instructions call (npm). Our summary lists: Python 3; Node.js; A Bash shell. Its frontmatter pre-approves these tools: Read, Write, Edit, Grep, Glob, Bash(cmd:*). Compatibility (from SKILL.md): Designed for Claude Code.

Does Genkit Production Expert access the network?

SKILL.md names 4 domains. As links in the text: firebase.google.com, github.com, zod.dev and opentelemetry.io. This is read from the text; nothing was executed.

Is Genkit Production Expert safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Genkit Production Expert use?

Genkit Production Expert is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Genkit Production Expert use?

About 1.5k tokens (SKILL.md is roughly 6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 8.2k tokens, read only when the agent opens those files.

What are the alternatives to Genkit Production Expert?

Skills that share tags, products or a category with Genkit Production Expert: Claude Cookbooks Reference (2025Emma/vibe-coding-cn, 23k stars), Gemini API Dev (google-gemini/gemini-skills, 4.3k stars), Agent Squad for TypeScript (2FastLabs/agent-squad, 7.8k stars) and Retail Product Search Agent (google/adk-recipes, 10k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Genkit Production Expert?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 2026.

Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.