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Ambiguity Resolver

CommunityPopular
mohitagw15856
ambiguity-resolver

Structure vague opportunities and unclear briefs into actionable one-page problem statements. Use when asked to clarify a vague brief, frame an undefined problem, make sense of an unclear opportunity, or when the user says 'we need to figure out what to do about X' or 'I've been asked to look into Y'. Produces a structured problem brief with reframed questions, scoped boundaries, and a minimum viable research plan.

Overview

Publishermohitagw15856
Repositorypm-claude-skills
Skill nameambiguity-resolver
Stars
1.4K
Forks
240
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 mohitagw15856 on GitHub. Read the source before you install it.

Installation

Install the Ambiguity Resolver 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/mohitagw15856/pm-claude-skills.git /tmp/pm-claude-skills
mkdir -p .claude/skills
cp -r /tmp/pm-claude-skills/exports/openclaw/ambiguity-resolver .claude/skills/ambiguity-resolver
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ambiguity Resolver 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 Ambiguity Resolver 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 Ambiguity Resolver 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.

Ambiguity Resolver Skill

Turn vague briefs and half-formed opportunities into structured, actionable problem statements — so you can reply with clarity instead of asking for three more meetings.

Required Inputs

Ask the user for these if not provided:

  • The vague brief or opportunity description (even a single sentence is enough)
  • Who asked for this (stakeholder context shapes the framing)
  • Known constraints (timeline, budget, team size — if any are known)

Three-Stage Process

Stage 1: Reframe

  • Restate the vague input as 3-5 explicit questions that need answering
  • Identify the unstated assumptions hidden in the brief
  • Surface the real decision this feeds into (what will someone do differently once this is resolved?)

Stage 2: Scope

  • Define what is explicitly IN scope
  • Define what is explicitly OUT of scope (equally important)
  • Identify the deadline pressure: is this urgent/important, important/not urgent, or unclear?
  • Name who owns the final decision and who needs to be consulted

Stage 3: Action

  • Define the minimum viable research: 2-3 activities maximum that would give enough signal to move forward with confidence
  • Time estimate for each activity
  • What each activity would tell you (and what it wouldn't)
  • Proposed check-in point: when to regroup before committing to more

Validate — Confirm every reframed question maps to at least one research activity. Verify scope boundaries are specific enough to say "no" to something concrete.

Output Structure

Problem Brief: [Opportunity Area]

Restated as questions:

  1. [Question 1]
  2. [Question 2]
  3. [Question 3]

Unstated assumptions we should surface:

  • [Assumption 1]
  • [Assumption 2]

In scope: [Clear boundary] Out of scope: [Clear boundary] Decision owner: [Name/role] Timeline: [Real deadline if known, or "unclear — recommend setting one"]

Minimum viable research:

ActivityTime requiredWhat it tells usWhat it won't tell us
[activity][time][insight][limitation]

Proposed check-in: After [activity], regroup to decide whether to proceed or pivot.

Example (Partial)

Input: "We need to figure out what to do about our enterprise customers."

Restated as questions:

  1. Are enterprise customers churning, underperforming on expansion, or both?
  2. Is this a product gap, a support/service gap, or a pricing/packaging issue?
  3. What does "do something" look like — a new initiative, a policy change, or a resource shift?

In scope: Enterprise accounts ($50K+ ARR) showing declining health scores in the last two quarters Out of scope: SMB segment, new enterprise acquisition strategy

Anti-Patterns

  • Do not reframe the brief into questions that are still too broad to research — each reframed question must be answerable by a specific activity
  • Do not list a research activity without stating what it would tell you and what it would NOT tell you
  • Do not leave the decision owner as "leadership" or "the team" — name a specific person or role
  • Do not omit an explicit out-of-scope boundary — without it, scope will expand organically and the brief becomes meaningless

Quality Checks

  • Every reframed question is specific enough to research (not "how do we improve things?")
  • Scope boundaries name something concrete that is excluded
  • Research activities are achievable within the stated timeline
  • Decision owner is identified (not "leadership" — a specific person or role)

Frequently asked questions

What does the Ambiguity Resolver AI skill do?

Structure vague opportunities and unclear briefs into actionable one-page problem statements. Use when asked to clarify a vague brief, frame an undefined problem, make sense of an unclear opportunity, or when the user says 'we need to figure out what to do about X' or 'I've been asked to look into Y'. Produces a structured problem brief with reframed questions, scoped boundaries, and a minimum viable research plan.

Why use Ambiguity Resolver on TypingMind?

Because you install it once and use it with any model. Ambiguity Resolver 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 Ambiguity Resolver in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mohitagw15856/pm-claude-skills/tree/main/exports/openclaw/ambiguity-resolver. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ambiguity Resolver?

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 Ambiguity Resolver?

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

Is the Ambiguity Resolver AI skill free?

Yes. It is published on GitHub by mohitagw15856 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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