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Implement Plan

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tobihagemann
implement-plan

Execute an implementation plan file produced by /draft-plan or /turboplan. Runs pre-implementation prep, then runs /implement to execute the steps and finalize once they are all done. Use when the user asks to "implement plan", "implement the plan", "execute the plan", "run the plan", "implement plans/<slug>.md", "start implementing the plan", or starts a fresh session to implement a previously drafted plan.

Overview

Publishertobihagemann
Repositoryturbo
Skill nameimplement-plan
Stars
417
Forks
32
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

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

Installation

Install the Implement Plan 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/tobihagemann/turbo/tree/main/claude/skills/implement-plan
  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/tobihagemann/turbo.git /tmp/turbo
mkdir -p .claude/skills
cp -r /tmp/turbo/claude/skills/implement-plan .claude/skills/implement-plan
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Implement Plan 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 Implement Plan 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 Implement Plan 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.

Implement Plan

Execute an implementation plan file.

Task Tracking

At the start, use TaskCreate to create a task for each step:

  1. Resolve and read the plan file
  2. Read context files
  3. Run /implement skill
  4. Update plan status

Step 1: Resolve and Read the Plan File

Determine which plan file to implement using these rules in order:

  1. Explicit path — If an absolute or relative path was passed and that file exists, use it
  2. Explicit slug — If a slug was passed (e.g., add-image-cache), resolve to .turbo/plans/<slug>.md if that file exists
  3. Single file — Glob .turbo/plans/*.md. If exactly one plan exists, use it
  4. Most recent — If multiple plans exist, use the most recently modified
  5. Legacy fallback — If .turbo/plans/ does not exist but .turbo/plan.md exists, use it
  6. Nothing found — If no rule above resolved to an existing file, tell the user to run /turboplan and stop. When a slug or path was passed but no file matched it, say which one was tried

If multiple plans exist and the most-recent choice is non-obvious (e.g., several plans were modified within the same minute), use AskUserQuestion to let the user pick from the candidates.

State the resolved plan path before continuing, then read the file.

Unless an explicit path or slug was passed, confirm the resolved plan still describes work that remains to be done:

  • Already implemented — the frontmatter status: is done
  • Waiting on a gate — the plan's Context states a gate that must pass before its work can start

When you check whether a gate has passed, check each condition it names at its source of record, such as the remote's tags for another repository's release, rather than a local copy.

When a signal fires, output it as text. Then use AskUserQuestion to offer:

  • Implement anyway — the marker is stale, or the gate has passed
  • Pick another plan — resolve to a different file under .turbo/plans/, then confirm that plan against these same signals
  • Leave it unimplemented — the plan needs revising first, or its gate has not passed

On Leave it unimplemented, tell the user to bring the plan current with /refine-plan and run this skill again, or, for a plan whose gate has not passed, to run this skill again once it has. Mark the remaining implement steps deleted, then use the TaskList tool and proceed to any remaining task.

Step 2: Read Context Files

The read list, every file to read in full:

  • Every file listed in the plan's Context Files section
  • Files the user referenced in their original request (if any)
  • Every file path the plan references in the Context, Pattern Survey, and Implementation Steps sections

Project the read's share of the context window before the bulk of it. With one Bash call, get the size in bytes of each read-list file that exists, and their total. Call the mcp__context-level__read tool, then read the plan's Context Files. Once those reads have returned, call the tool again. While the reading has dropped fewer than five percentage points and read-list files remain unread, read a further batch and call the tool again. The projection is the drop from the first reading to the last, multiplied by the total bytes over the bytes read so far.

When the projection is above 25%, output as text that reading the plan's files is projected to take that share of the context window, with the number of files still unread. Then use AskUserQuestion to ask how to implement the plan:

  • Implement in this session — this session reads the remaining files and implements every step itself, and will likely need to compact before QA. Mark it recommended when the projection is 50% or less.
  • One subagent per step — the remaining files stay unread here, and each Implementation Step goes to a fresh subagent that reads what its step names. Every subagent gathers its own context, so the run takes longer and uses more tokens overall. Mark it recommended when the projection is above 50%.

Read the remaining read-list files unless the answer was One subagent per step.

Step 3: Run /implement Skill

Run the /implement skill. The plan file, its file references, and its Verification section are already in conversation context from Step 1. When Step 2 settled on one subagent per step, pass that choice along.

Step 4: Update Plan Status

After /implement completes, set the plan's frontmatter status: to done with the Edit tool. If the plan is the legacy .turbo/plan.md without frontmatter, skip the status update.

When that edit is refused while this session runs inside a linked worktree, report that the status update was not applied and name the edit to make from a session that can write to the main checkout: status: done in the resolved plan path.

When the plan's Context names a larger source it implements part of (an assessment, a backlog, an issue), report the plan's completion separately from that source's: name the source's items the plan left out, and say whether another plan under .turbo/plans/ covers them.

Rules

  • The plan file is read-only during execution. If revisions are needed, run /refine-plan or /draft-plan separately.
  • Never skip Step 2.
  • Never enumerate or execute the plan's Implementation Steps inline. The work runs through /implement. Restating steps as a turn-level narration counts as inline execution and bypasses the delegation.
  • If the plan's Implementation Steps or Verification include git commit, git push, or PR creation, halt before Step 3 and ask the user to remove them via /refine-plan. A Verification item that observes the result of a push without instructing one is not grounds to halt.

Frequently asked questions

What does the Implement Plan AI skill do?

Execute an implementation plan file produced by /draft-plan or /turboplan. Runs pre-implementation prep, then runs /implement to execute the steps and finalize once they are all done. Use when the user asks to "implement plan", "implement the plan", "execute the plan", "run the plan", "implement plans/<slug>.md", "start implementing the plan", or starts a fresh session to implement a previously drafted plan.

Why use Implement Plan on TypingMind?

Because you install it once and use it with any model. Implement Plan 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 Implement Plan in TypingMind?

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

Which AI models can use Implement Plan?

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 Implement Plan?

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

Is the Implement Plan AI skill free?

Yes. It is published on GitHub by tobihagemann 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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