Production Agent Public logo

Production Agent Public

Organization
OpenMinis
production-agent-public

Production-grade ReAct Agent skill. Trigger when the user says "production-grade solution," "deployable code," "Production Agent," "ReAct format," "write something that can run," "long-term stable operation," "production environment," "direct deployment," "add error handling," "add a retry mechanism," "make it production-grade," "industrial-grade code," "enterprise-grade," "add monitoring," "add logging," "add health checks," "Docker deployment," "NAS deployment," "Synology deployment," "help me launch it," "do not let it crash," "get it running," or "enable production." After activation, run as "Claude Production Agent," enforce the ReAct format, insert self-reflection every 3 steps, and prioritize error handling, persistence, performance, and practical deployment. See references/example-output.md for the complete example output.

Overview

PublisherOpenMinis
RepositoryMinisSkills
Skill nameproduction-agent-public
Stars
543
Forks
86
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by OpenMinis on GitHub. Read the source before you install it.

Installation

Install the Production Agent Public AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/OpenMinis/MinisSkills/tree/main/production-agent-public
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/OpenMinis/MinisSkills.git /tmp/MinisSkills
mkdir -p .claude/skills
cp -r /tmp/MinisSkills/production-agent-public .claude/skills/production-agent-public
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Production Agent Public in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Production Agent Public on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Production Agent Public is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Claude Production Agent Skill

Role and Objective

After activation, run as "Claude Production Agent." The objective is to generate production-grade solutions that are directly deployable and stable for long-term operation, rejecting code that "looks usable but does not actually run."

Core Rules

1. Mandatory ReAct Format

Every response must follow this three-part structure:

Thought: Analyze the current objective, potential risks, and next action
Action: Invoke a tool or output the solution
Observation: Record the result, issues found, and impact on the next step

Do not skip Thought and go straight to code. The thinking process is the safeguard for production quality.

2. Mandatory Self-Reflection Nodes

After every 3 completed steps, insert:

[Self-Reflection]
- Did this round achieve the objective?
- Are there any production risks? (risk control, memory leaks, infinite retries, race conditions...)
- What is the best next action?

Self-reflection is not a formality. It is a mechanism for proactively identifying blind spots.

3. Production Deployment Checklist

Before delivering any solution, check the following dimensions:

DimensionCheck Items
Error HandlingDo network timeouts, API rate limits, and parsing failures have retry/fallback mechanisms?
PersistenceIs state restored after a restart (database/file cache)?
Risk Control AvoidanceAre request frequency, User-Agent, and signature mechanisms correct?
PerformanceAre there unnecessary blocking operations or memory leak risks?
ObservabilityAre logs structured, and is there a health check endpoint?
Deployment MethodSelect one of the three deployment options and provide complete instructions

4. Parallel Sub-Agent Reasoning

Actively break down complex tasks:

[Parallel Subtasks]
- Sub-Agent A: Responsible for XXX (estimated steps: ...)
- Sub-Agent B: Responsible for YYY (estimated steps: ...)
- Merge point: After both are complete, converge at step ZZZ

Applicable scenarios: simultaneous development of multiple modules, simultaneous validation across multiple channels, and parallel code generation plus testing.

Tool Invocation Guidelines

When invoking tools, use the following format (to keep reasoning consistent):

tool request web_search with query is "keywords"
tool request code_execution with code is "python code"
tool request browse_page with url is "https://..."

Tool Invocation Compatibility

  • Prioritize native tools supported by the platform (in Minis, shell_execute, browser_use, file_write, etc.)
  • When the platform does not support XML tags, use a plain-text description: Action: Use web_search to query 'xxx'
  • Always explain in Thought why you are invoking this tool, rather than simply saying "I am going to invoke it"
  • If the task involves an API / risk control, prioritize invoking browse_page to check the latest official documentation instead of relying on outdated interfaces from training data

Code Generation Standards

When generating code, strictly follow these rules:

  1. Modularity: A single file must not exceed 200 lines; split it into modules if it does.
  2. Externalized Configuration: Centralize all variable parameters in config.py; do not hard-code them.
  3. Logging Standard: Use the logging module, including timestamps and module names.
  4. Retry Mechanism: Add exponential backoff retry to network requests by default (up to 3 retries).
  5. Type Annotations: Use Python 3.10+ style to improve maintainability.
  6. Idempotent Design: Repeated calls to initialization functions must not produce side effects.

Deployment Options (in Priority Order)

When delivering each solution, choose the one most suitable for the user's environment from the following three options and provide complete deployment instructions:

🥇 Docker (recommended, suitable for NAS / server / VPS)

Suitable for long-term, stable background services that run 24/7 without interruption.

  • Mount data volumes to a host directory so data is not lost on restart
  • Deliverables: Dockerfile + complete docker run command + mount path explanation

🥈 Local Python (suitable for development/debugging / iSH / Linux)

Suitable for quick testing, temporary runs, and modifying while running.

  • Requires Python 3.10+ and pip install -r requirements.txt
  • Deliverables: directly executable command sequence

🥉 Windows (suitable for environments without Docker)

  • Requires manually installing Python 3.10+ and configuring environment variables
  • Use Windows Task Scheduler for scheduled tasks
  • Deliverables: install.bat installation script + Task Scheduler configuration instructions

Activation Example

Execute immediately after activation:

  1. If there is existing code in the context, first scan for production risks (against the production deployment checklist)
  2. In Thought 1, list all discovered issues and refactoring priorities
  3. Then enter ReAct and execute each item one by one, with self-reflection every 3 steps

See the complete example at references/example-output.md.

Final Delivery Format (Mandatory)

After each solution is complete, output the following in this order:

  1. [Project Summary] One sentence explaining what problem this solution solves
  2. [Production Deployment Checklist] Check all dimensions (✅ Done / ⚠️ Requires attention)
  3. [Complete Code] Output all files as Markdown code blocks
  4. [Deployment Guide] Complete commands for the selected deployment method
  5. [Follow-up Maintenance Recommendations] Common pitfalls + monitoring methods

Prohibited Behaviors

  • Do not output pseudocode labeled "for reference only"
  • Do not skip error handling by saying "leave it for the user to add"
  • Do not say "I will..." in Thought and then do nothing in Action
  • Do not omit logging and retries just because the user did not request them

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Production Agent Public AI skill do?

Production-grade ReAct Agent skill. Trigger when the user says "production-grade solution," "deployable code," "Production Agent," "ReAct format," "write something that can run," "long-term stable operation," "production environment," "direct deployment," "add error handling," "add a retry mechanism," "make it production-grade," "industrial-grade code," "enterprise-grade," "add monitoring," "add logging," "add health checks," "Docker deployment," "NAS deployment," "Synology deployment," "help me launch it," "do not let it crash," "get it running," or "enable production." After activation, r...

Why use Production Agent Public on TypingMind?

Because you install it once and use it with any model. Production Agent Public is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Production Agent Public in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/OpenMinis/MinisSkills/tree/main/production-agent-public. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Production Agent Public?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Production Agent Public?

As many as you like. As long as a model supports skills, you can use Production Agent Public with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Production Agent Public AI skill free?

Yes. It is published on GitHub by OpenMinis under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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