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Plan Interrogate

CommunityPopular
rohitg00
plan-interrogate

Stress-test a plan by walking its decision tree one question at a time. Use when the user wants to pressure-test a design before implementation.

Overview

Publisherrohitg00
Repositorypro-workflow
Skill nameplan-interrogate
Stars
2.9K
Forks
286
Bundled files
Instructions only
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 rohitg00 on GitHub. Read the source before you install it.

Installation

Install the Plan Interrogate 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/rohitg00/pro-workflow.git /tmp/pro-workflow
mkdir -p .claude/skills
cp -r /tmp/pro-workflow/skills/plan-interrogate .claude/skills/plan-interrogate
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Plan Interrogate 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 Plan Interrogate 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 Plan Interrogate 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.

plan-interrogate

Drive a plan from sketch to commitment by resolving every open decision before any code is written.

Method

  1. Restate the plan in one paragraph. Confirm with the user that this is the plan being interrogated. Do not proceed on a mis-restatement.
  2. Extract the decision tree. Every branch point becomes a node. Mark each node as open (undecided) or resolved. A resolved node carries a source tag: user (the user answered), inferred (the codebase or an existing constraint settled it).
  3. Resolve in dependency order. A node is ready when every node it depends on is resolved.
  4. For each ready open node, ask exactly one question. Keep the question tight and binary or small-multiple-choice when possible.
  5. Pair every question with a recommended answer and one sentence of reasoning. The user can confirm, pick a different option, or push back.
  6. Before asking, check whether the answer already lives in the codebase, prior commits, or an existing doc. If so, skip the question and mark the node resolved with source inferred: <path>.
  7. Exit only when zero nodes are open. Print the resolved tree as a flat list: Decision - Choice - Source (user | inferred: <path>).

Anti-patterns

  • Asking multiple questions at once. The user loses context and you lose the ability to react to each answer individually.
  • Asking before exploring. If a fifteen-second read would answer the question, read first.
  • Asking without a recommendation. A question without a stance is a survey; it offloads design onto the user.
  • Rolling past an unresolved node. If a dependency is not pinned, the downstream question is premature.

Outputs

The interrogation produces three artifacts, not just answers. Offer to write each; do not force it.

  1. Decision ledger (always). The resolved tree as a flat list: Decision - Choice - Source (user | inferred: <path>).

  2. CONTEXT.md (when the interrogation surfaced project-specific terms). A short shared-language file: every domain term you and the user had to pin down, with a one-line definition in the project's own words. This is what stops the agent from using twenty words where one will do next session, and keeps names in code consistent. One term per line: term - what it means here. Point future sessions at it. On re-run, merge new terms in place rather than overwriting existing ones.

  3. Decision records (for contested or hard-to-reverse nodes only). One short record per decision that a future reader would question: the context, the choice, the alternatives rejected, and why. Keep them in docs/decisions/NNNN-slug.md. Read the directory first and number from the highest existing record so two records never collide. Skip the obvious ones - a record for a trivial choice is noise.

Output contract

The decision ledger the user can paste into the plan doc. No prose summary. No hedging. If the user declines to decide a node, mark it DEFERRED with the reason the user gave - this is not the same as open. When you write CONTEXT.md or a decision record, keep it in the project's language, not a generic template.

Frequently asked questions

What does the Plan Interrogate AI skill do?

Stress-test a plan by walking its decision tree one question at a time. Use when the user wants to pressure-test a design before implementation.

Why use Plan Interrogate on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rohitg00/pro-workflow/tree/main/skills/plan-interrogate. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Plan Interrogate?

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 Plan Interrogate?

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

Is the Plan Interrogate AI skill free?

It is published on GitHub by rohitg00. 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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