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Productionize App With Services

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Dimon94
productionize-app-with-services

Productize a working prototype by adding a bounded set of explicitly requested operational or product services. Use only when the user asks for a broad productization pass or names product-service gaps such as permissions, API access, audit history, admin operations, or production operability as the primary task. Do not trigger for ordinary feature work, a single service integration, generic "make this production ready" phrasing without concrete scope, or routine security, observability, testing, and release tasks covered by narrower skills.

Overview

PublisherDimon94
Repositoryskills
Skill nameproductionize-app-with-services
Stars
107
Forks
21
Bundled files
4
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.

  • 4 bundled files

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

  • Open source

    Published by Dimon94 on GitHub. Read the source before you install it.

Installation

Install the Productionize App With Services 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/Dimon94/skills/tree/main/skills/productionize-app-with-services
  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/Dimon94/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/productionize-app-with-services .claude/skills/productionize-app-with-services
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Productionize App With Services 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 Productionize App With Services 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 Productionize App With Services 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.

Productionize App With Services

Use this skill when a working prototype needs a specific, user-selected set of product services. Productization is not a checklist of everything a mature SaaS might contain.

Use this skill to coordinate several named product-service gaps. For one narrow capability, use ordinary implementation or the corresponding narrower skill.

Counterweight: earn every service

  • Start from users, callers, incidents, and operating constraints. Name the concrete need before adding a service or control.
  • Prefer the repository's existing stack and provider capabilities. Do not introduce Postgres, hand-rolled auth, PostHog, OpenAPI, API keys, feature flags, audit logs, admin UI, dashboards, or a public agent guide by default.
  • Add the smallest service that closes the named gap. Avoid building extension points, bulk APIs, role systems, or management surfaces for hypothetical consumers.
  • Use one authoritative identity and action path where practical, but do not create a shared action layer merely to satisfy the pattern.
  • Prefer deletion, configuration, or a provider-native feature over a new subsystem.
  • Do not treat security, observability, auditability, API access, and agent friendliness as inseparable. Select only the requested dimensions.
  • Do not create synthetic users, records, migrations, PRs, deployments, or audit microsites solely to prove productization machinery.
  • Production mutation, commits, external services, and subagents require the active request to include them or clearly authorize them.
  • Stop when the selected product need is met and proportionally verified; report deferred dimensions without implementing them.

Stack selection

Prefer the repo's existing stack and managed provider capabilities. When a selected capability requires a choice, compare the smallest viable options against current scale, ownership, portability, cost, and migration burden. Do not introduce a preferred database, auth model, analytics vendor, schema library, or documentation format without evidence from the task.

Operating Principles

  • Ground everything in repo facts before planning: current scripts, deployment topology, database schema, auth model, API routes, frontend state model, tests, recent commits, and dirty worktree.
  • Preserve working product behavior unless the user explicitly asks for breaking changes.
  • Keep existing UI and API architecture unless a selected caller exposes a concrete mismatch.
  • Consolidate a product action only when it is already duplicated across meaningful callers.
  • Add user or admin visibility only for state someone must actually operate.
  • Make agent-facing surfaces only for named agent consumers.

Workflow

1. Discover The Product Boundary

Run a non-mutating discovery pass:

  • Current git status and only the entrypoints, data, providers, tests, and operations relevant to the selected services.
  • Named users, operators, or programmatic callers and the workflows they need.
  • Existing framework or provider capabilities that may already satisfy the need.
  • Product behavior and compatibility that must remain intact.

Read only the relevant sections of product-principles.md for capabilities the user selected.

2. Select Productization Slices

Write a plan the user can modify before or during execution. Include:

  • Product goal, explicit non-goals, assumptions, and risk posture.
  • Current-state findings with file/schema references.
  • Requested workstreams and explicit non-goals; omit unrelated maturity dimensions.
  • A concurrency map only when the user requested parallel work.
  • API plan only when named programmatic clients require one.
  • Proportional validation for the selected slices.
  • Rollback and compatibility plan for migrations, env changes, provider changes, and user-visible workflow changes.

The plan should be complete, but not frozen. Update it as facts change.

3. Build The Safety Net

Before broad edits:

  • Run focused existing checks or record why they cannot run.
  • Add a characterization test only when fragile behavior cannot otherwise be preserved confidently.
  • Add runtime schemas only at changed untrusted boundaries.
  • Add health checks only when deployment or operations are selected slices and existing signals are insufficient.
  • Capture screenshots only for changed visual behavior.

4. Execute Only Selected Slices

Implement only the selected capabilities. Typical slices are a data or permission contract, one shared operation used by named callers, its required UI or API adapter, and focused verification. Security, observability, audit, admin UX, documentation, and deployment are separate slices only when explicitly in scope.

For detailed execution rules and worker prompts, read execution-playbook.md.

5. Coordinate Large Work When Requested

Use subagents only when explicitly requested and the selected slices are independent.

Do not let multiple workers edit the same hot files. Keep one integration owner responsible for proportional verification and any explicitly requested merge or deploy.

6. Validate Proportionally

At the end of each slice:

  • Run narrow tests for touched code.
  • Add focused tests when they prove a plausible defect in a changed contract.
  • Expand to broad checks only when shared boundaries or repository policy require them.
  • Run e2e/browser/screenshot checks for changed user/admin flows.
  • Deploy only when the active request includes deployment.
  • If deployment is explicitly requested and smoke data materially reduces risk, use clearly temporary records and clean them up.
  • Check only logs or dashboards relevant to selected and deployed capabilities.

7. Report The Result

Summarize selected capabilities, evidence, deletions, remaining risks, and deliberately omitted maturity dimensions. Create a static review microsite only when explicitly requested; then read audit-microsite.md.

Quality Bar

A successful run satisfies the named users or operators, reuses existing product and provider capabilities, introduces no hypothetical services, keeps one clear source of truth, and provides proportional verification.

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 Productionize App With Services AI skill do?

Productize a working prototype by adding a bounded set of explicitly requested operational or product services. Use only when the user asks for a broad productization pass or names product-service gaps such as permissions, API access, audit history, admin operations, or production operability as the primary task. Do not trigger for ordinary feature work, a single service integration, generic "make this production ready" phrasing without concrete scope, or routine security, observability, testing, and release tasks covered by narrower skills.

Why use Productionize App With Services on TypingMind?

Because you install it once and use it with any model. Productionize App With Services 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 Productionize App With Services in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Dimon94/skills/tree/main/skills/productionize-app-with-services. 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 Productionize App With Services?

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 Productionize App With Services?

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

Is the Productionize App With Services AI skill free?

It is published on GitHub by Dimon94. Check the repository for licensing terms. 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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