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Ask Why

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maxkura
Ask_Why

Use this skill for vague, high-rework planning work that needs iterative human decisions, such as product design, research planning, technical strategy, PRDs, workflow design, agent design, or skill design. Run concise P2A decision rounds, challenge assumptions early, record decisions in PLAN.md, DECISIONS.md, and OPEN_QUESTIONS.md, then produce FINAL_PLAN.md when converged. Do not use for simple factual Q&A, routine coding fixes, direct execution, translation, writing polish, or one-shot requests.

Overview

Publishermaxkura
RepositoryAsk_Why
Skill nameAsk_Why
Stars
117
Forks
9
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Ask Why 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/maxkura/Ask_Why/tree/main/Ask_Why
  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/maxkura/Ask_Why.git /tmp/Ask_Why
mkdir -p .claude/skills
cp -r /tmp/Ask_Why/Ask_Why .claude/skills/Ask_Why
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ask Why 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 Ask Why 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 Ask Why 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.

Ask Why

Use this skill to turn an ambiguous request into a traceable planning conversation. Keep the user in charge of direction-changing choices, make tradeoffs explicit, and preserve the reasoning in durable state files.

Cache Strategy

  • Keep SKILL.md as the stable dispatcher. Long structural templates live in references/formats.md.
  • Read references/formats.md only after the Entry Gate passes or when artifact formats are needed.
  • Use the same localized labels, heading order, and file names within each session.
  • Put stable content before volatile content in artifacts: goal, boundaries, decisions, then open questions.
  • Prefer short state summaries over pasting full prior plans, transcripts, or file contents into chat.
  • Append decisions; do not rewrite confirmed history unless correcting an error.
  • Avoid timestamps, environment dumps, long examples, and local file listings unless they change the plan.

Entry Gate

Enter Ask Why mode only when all are true:

  • The task is vague, open-ended, or complex.
  • User preference will materially change the result.
  • Multiple reasonable directions exist.
  • Early wrong choices would cause substantial rework.
  • The user appears willing to co-design through questions and decisions.

If any condition is false, briefly say why the workflow is not needed and handle the request directly.

Primary Language Contract

  • At the moment this skill is invoked, determine the primary language of the user's triggering prompt before producing Ask Why output.
  • Use that initial primary language for every user-facing interaction and maintained artifact in the session, including round prefaces, decision questions, option labels, recommendations, summaries, PLAN.md, DECISIONS.md, OPEN_QUESTIONS.md, and FINAL_PLAN.md.
  • If the triggering prompt mixes languages, choose the dominant natural language by meaningful user-written content and intent-bearing phrasing. If no dominant language can be determined, ask one brief language-selection question before continuing.
  • Keep filenames, code identifiers, API names, product names, quoted user text, and other proper nouns in their original form when translation would reduce precision.
  • Do not switch the session language just because the user writes a later message in another language. Switch only when the user explicitly asks to change the session language.
  • When using templates from references/formats.md, translate or localize all headings, bracketed labels, field labels, option labels, helper text, and prose into the session's primary language while preserving the template structure and semantics.

Operating Rules

  • Follow the Primary Language Contract for all interaction, state files, draft plans, and final plans.
  • Treat the user as the decision maker. Recommend, challenge, and explain tradeoffs, but do not silently decide key unknowns.
  • Ask 3-5 focused questions per round; each question must change the plan, confirm a key assumption, or choose a real tradeoff.
  • If files can be written, maintain state in p2a-session/ unless the user specifies another path.
  • If writing is unavailable, maintain the same state inline using the same headings.

Required State

Maintain these files:

  • PLAN.md: current version, goal, non-goals, mechanism, workflow, outputs, risks, next phase.
  • DECISIONS.md: append-only confirmed choices with rationale and plan impact.
  • OPEN_QUESTIONS.md: unresolved P0, P1, and P2 questions.
  • FINAL_PLAN.md: final deliverable once the direction has converged.

Use the localized structural templates from references/formats.md when creating or repairing these files.

Runbook

  1. Read references/formats.md.
  2. Start with understanding, not a final plan.
  3. Create or update the state files.
  4. Build an ambiguity map and rank unknowns as P0, P1, or P2.
  5. Run decision rounds with 3-5 focused questions.
  6. Update state files after every user answer before asking the next round.
  7. Challenge strongly in rounds 1-3; converge from round 4 onward.
  8. Summarize every 2-3 rounds.
  9. Produce a draft plan when all P0 and most P1 questions are resolved.
  10. Produce FINAL_PLAN.md when remaining uncertainty is explicit and non-blocking.

Ambiguity Map

Classify unknowns with these categories:

  • Goal ambiguity
  • User or audience ambiguity
  • Use-case ambiguity
  • Interaction granularity ambiguity
  • Platform or environment ambiguity
  • Output format ambiguity
  • Success criteria ambiguity
  • Risk tolerance ambiguity
  • Boundary or non-goal ambiguity
  • Follow-up execution ambiguity

Use these priorities:

  • P0: blocks further design.
  • P1: changes the core plan.
  • P2: can be assumed temporarily and confirmed later.

Rounds 1-3

  • Include current plan, current ambiguities, and round goal.
  • Name at least 2 credible weaknesses in the current framing.
  • Offer at least 2 expansion or reframing directions.
  • Recommend one option for every key question.
  • Do not assume the user's initial framing is automatically correct.
  • Do not make final decisions for the user.
  • Use the localized structural round and question formats from references/formats.md.

Rounds 4+

  • Reduce scope expansion unless a blocking contradiction appears.
  • Prioritize unresolved P0 and P1 questions.
  • Keep asking 3-5 questions per round while questions remain useful.
  • If the user introduces no major new direction for 2 consecutive rounds, prepare the draft plan.
  • Ask only for decisions that materially change the plan once convergence starts.

Draft And Final Plans

  • Draft plan: produce when all P0 and most P1 questions are resolved.
  • Final plan: produce when remaining uncertainty is explicit and non-blocking.
  • Use the localized draft, final, and stage summary formats from references/formats.md.

The final plan must include:

  • Task definition and boundaries
  • Goals and non-goals
  • Key decisions and why they hold
  • Constraints and risks
  • Execution path
  • Non-blocking open questions
  • Next actions, including who decides, who executes, and what happens first

Definition Of Done

The Ask Why session is complete only when:

  • The task boundary is clear.
  • Direction-changing decisions are resolved and recorded.
  • Remaining uncertainty is explicit and non-blocking.
  • The final plan is concrete enough for discussion, task breakdown, implementation, or continued collaboration.
  • DECISIONS.md and OPEN_QUESTIONS.md trace the main reasoning.

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 Ask Why AI skill do?

Use this skill for vague, high-rework planning work that needs iterative human decisions, such as product design, research planning, technical strategy, PRDs, workflow design, agent design, or skill design. Run concise P2A decision rounds, challenge assumptions early, record decisions in PLAN.md, DECISIONS.md, and OPEN_QUESTIONS.md, then produce FINAL_PLAN.md when converged. Do not use for simple factual Q&A, routine coding fixes, direct execution, translation, writing polish, or one-shot requests.

Why use Ask Why on TypingMind?

Because you install it once and use it with any model. Ask Why 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 Ask Why in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/maxkura/Ask_Why/tree/main/Ask_Why. 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 Ask Why?

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 Ask Why?

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

Is the Ask Why AI skill free?

Yes. It is published on GitHub by maxkura 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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