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Dev Implementation

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codeaholicguy
dev-implementation

AI DevKit · Implementation phase guidance for executing feature plans and checking implementation against design. Use when the user wants to implement planned tasks, update implementation docs, verify code matches design, or run dev-lifecycle phases 5 and 7.

Overview

Publishercodeaholicguy
Repositoryai-devkit
Skill namedev-implementation
Stars
1.6K
Forks
252
Bundled files
1
LicenseApache-2.0
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Dev Implementation 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.
  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/codeaholicguy/ai-devkit.git /tmp/ai-devkit
mkdir -p .claude/skills
cp -r /tmp/ai-devkit/skills/dev-implementation .claude/skills/dev-implementation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dev Implementation 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 Dev Implementation 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 Dev Implementation 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.

Dev Implementation

Run implementation work for configured AI docs features. Before changing docs or code, propose the concrete plan for this phase and wait for user approval unless the user already approved the exact phase plan.

Phase Contract

  1. Run npx ai-devkit@latest lint before phase work.
  2. If working on a named feature, run npx ai-devkit@latest lint --feature <name>.
  3. Read requirements, design, planning, implementation, and testing docs before changes.
  4. Use the tdd skill while executing implementation tasks: write a failing test before production code, then make it pass.
  5. Apply the verify skill before completing tasks or making implementation alignment claims.
  6. Keep testing and implementation docs in lockstep with code. Do not defer all doc updates to final verification.
  7. If parent dev-lifecycle established usable task tracing, emit phase, progress, next-step, blocker, and evidence events per task.

Execute Plan

Use for Phase 5.

  1. Run npx ai-devkit@latest lint --feature <name> and work through the planning doc path it validates. If manual path resolution is unavoidable, first resolve .ai-devkit.json paths.docs, falling back to docs/ai.
  2. Gather context: feature name, planning doc path, supporting docs, current branch, and current diff.
  3. Parse task lists and build an ordered queue by section.
  4. Present the task queue with status: todo, in-progress, done, blocked.
  5. For each task, show context, suggest relevant docs, and outline sub-steps from the design doc when useful.
  6. If task tracing is available, record current task progress and immediate next action per task.
  7. Reuse before writing: grep for existing utilities/functions before adding new ones. Reuse only if it fits cleanly.
  8. Subtract before adding: delete dead paths, redundant guards, stale tests, and one-caller wrappers when they are in task scope.
  9. Handle breaking changes carefully: update all in-repo callers atomically and delete the old internal API; for external/public/cross-service callers, add a new function and deprecate the old one.
  10. Model the domain when implementation friction repeats: replace synchronized flags, branch growth, or duplicated shape assumptions with the smallest domain structure that removes them.
  11. Generate a markdown tracking snippet after each status change.
  12. After each task, update the testing doc with completed scenarios, newly discovered scenarios, and invalidated scenarios. Update the implementation doc with changed files, decisions, design deviations, and edge cases handled.
  13. After each section, ask if new tasks were discovered.
  14. Summarize completed, in-progress, blocked, skipped, new tasks, task-tracing events emitted or why tracing was unavailable, and doc deltas.

Next: after completing any task, run dev-planning Phase 6. When all tasks are done, run Check Implementation, then dev-testing and dev-review.

Check Implementation

Use for Phase 7.

  1. Compare implementation against the configured design and requirements docs validated by npx ai-devkit@latest lint --feature <name>.
  2. Gather context: feature description, modified files, relevant design/requirements docs, constraints.
  3. Summarize design: key decisions, components, interfaces, data flows.
  4. Review file by file: verify design intent, note deviations, flag logic gaps, edge cases, security issues, and missing tests or doc updates.
  5. Treat repeated implementation friction as a redesign signal, not another patch, and return to design when the model no longer fits.
  6. Finalize the implementation doc. Verify it captures what shipped, fill gaps, and record follow-ups.
  7. Summarize alignment status, deviations with severity, missing pieces, concerns, and next steps.

Next: dev-testing, then dev-review. If major deviations exist, return to dev-design if design is wrong or Execute Plan if implementation is wrong.

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 Dev Implementation AI skill do?

AI DevKit · Implementation phase guidance for executing feature plans and checking implementation against design. Use when the user wants to implement planned tasks, update implementation docs, verify code matches design, or run dev-lifecycle phases 5 and 7.

Why use Dev Implementation on TypingMind?

Because you install it once and use it with any model. Dev Implementation 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 Dev Implementation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/codeaholicguy/ai-devkit/tree/main/skills/dev-implementation. 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 Dev Implementation?

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 Dev Implementation?

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

Is the Dev Implementation AI skill free?

Yes. It is published on GitHub by codeaholicguy under the Apache-2.0 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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