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Technical Translator

Community
Mehdibargach
technical-translator

Translate a technical document (API spec, architecture doc, RFC, eng proposal) into PM-friendly language. What it means for the product and users.

Overview

PublisherMehdibargach
Repositoryclaude-code-pm-skills
Skill nametechnical-translator
Stars
97
Forks
38
Bundled files
Instructions only
Links
Skill sourceRepository
  • 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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Technical Translator 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/Mehdibargach/claude-code-pm-skills/tree/main/skills/technical-translator
  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/Mehdibargach/claude-code-pm-skills.git /tmp/claude-code-pm-skills
mkdir -p .claude/skills
cp -r /tmp/claude-code-pm-skills/skills/technical-translator .claude/skills/technical-translator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Technical Translator 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 Technical Translator 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 Technical Translator 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.

Technical Translator

Read a technical document and translate it into language a PM can act on. Not dumbed down — translated. The goal is to preserve nuance while making it actionable for product decisions.

Process

  1. Read the technical document at the provided file path.
  2. Identify the core technical concepts, decisions, and trade-offs.
  3. Translate each into product and user impact.
  4. Flag what the PM must understand versus what is nice-to-know context.
  5. Surface decisions the PM needs to make or influence.
  6. List questions the PM should bring to engineering.
  7. Save the output to a file in the current working directory as translated-[original-filename].md.

Output Format

TL;DR

1-2 sentences. What is this document about and why should the PM care?

What This Means for the Product

  • How does this change what we can build?
  • Does this open new capabilities or close off options?
  • Does this affect our roadmap or timelines?
  • Any impact on product architecture or platform strategy?

What This Means for Users

  • Will users notice anything? (Performance, UI changes, new features, breaking changes)
  • Does this affect specific user segments differently?
  • Any migration, downtime, or behavior changes users need to know about?
  • Impact on user data, privacy, or security?

Key Decisions the PM Needs to Make

For each decision:

  • What is the decision?
  • What are the options?
  • What are the trade-offs (in product terms, not technical terms)?
  • What is the engineering team's recommendation and why?

Questions to Ask Engineering

  • Clarifying questions the PM should bring to the next sync
  • "What happens if..." scenarios that aren't addressed in the doc
  • Timeline and resource implications to validate

Risks

  • Technical risks translated to product impact ("If the migration fails, users will experience X")
  • Timeline risks
  • Dependency risks
  • Things the doc assumes that might not be true

Glossary (if needed)

  • Technical terms used in the document with plain-language definitions
  • Only include terms the PM will encounter again — skip one-off jargon

Rules

  • Do not dumb down. Translate. A PM reading this should understand the nuance, not a watered-down version.
  • Clearly separate what the PM MUST understand (affects decisions) from nice-to-know context.
  • When translating trade-offs, always frame them in terms of user impact, timeline impact, or cost impact.
  • If the technical document is ambiguous, flag the ambiguity rather than guessing.
  • Preserve the original document's structure references so the PM can go back to the source for specific sections.
  • If the document contains code, don't reproduce the code — explain what it does and why it matters.
  • Never invent product implications that aren't supported by the technical content.

Frequently asked questions

What does the Technical Translator AI skill do?

Translate a technical document (API spec, architecture doc, RFC, eng proposal) into PM-friendly language. What it means for the product and users.

Why use Technical Translator on TypingMind?

Because you install it once and use it with any model. Technical Translator 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 Technical Translator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Mehdibargach/claude-code-pm-skills/tree/main/skills/technical-translator. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Technical Translator?

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 Technical Translator?

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

Is the Technical Translator AI skill free?

It is published on GitHub by Mehdibargach. 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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