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Product Discovery

Community
nahid-sparktales
product-discovery

Find the real problem behind a feature request before anyone scopes a solution — whose job is stuck, what evidence exists already, and what observation would prove the assumption wrong. Use when a request arrives phrased as a solution ("add a dashboard", "we need notifications"), when a team is about to build on a belief nobody has checked, or when asked whether something is worth building at all. Not for writing the spec once the problem is settled, not for ranking work that is already understood, and never a licence to contact users without permission.

Overview

Publishernahid-sparktales
Repositoryagent-dispatcher
Skill nameproduct-discovery
Stars
58
Forks
5
Bundled files
1
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.

  • 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 nahid-sparktales on GitHub. Read the source before you install it.

Installation

Install the Product Discovery 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/nahid-sparktales/agent-dispatcher/tree/main/skills/product/product-discovery
  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/nahid-sparktales/agent-dispatcher.git /tmp/agent-dispatcher
mkdir -p .claude/skills
cp -r /tmp/agent-dispatcher/skills/product/product-discovery .claude/skills/product-discovery
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Product Discovery 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 Product Discovery 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 Product Discovery 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.

Product discovery

A feature request is an answer. Discovery is recovering the question it answers, and finding out whether the question is real. Most of the evidence you need already exists and nobody has read it.

When this fires

A request names a solution and not a problem; a plan rests on a belief about users that has never been checked; someone asks whether a thing is worth building. It does not fire when the problem and its evidence are already established and the job is to write the scope — that is prd-and-stories.

Procedure

  1. Split the request into solution and problem. Write the request verbatim, then write the sentence under it: who is trying to do what, and what happens today instead. If you cannot write that second sentence from what you were given, that gap is the finding — say so before going further.
  2. Name the person and the job. A named role doing a specific task on a specific occasion ("a support agent closing a ticket after a refund"), not a segment. Ask who is not affected too — a problem everyone allegedly has usually belongs to nobody in particular.
  3. Read what the product already does. Find the current path through the code, the existing screens, the config, the feature flags. Half of all requests are for something that exists, is disabled, or is two clicks away from where the user looked. Report that outcome as a result, not as a failure to find work.
  4. Harvest evidence already in reach before asking for any. Issue tracker and its duplicates, support tickets, changelog and past attempts at this, analytics and logs already collected, prior docs and decision records. Quote what you found with its source; a claim you cannot attribute is an assumption.
  5. Grade every piece of evidence. Three grades, and keep them apart: observed (behaviour in logs, tickets, recordings, a failing workflow you traced), reported (a person says so — including the requester and the loudest stakeholder), assumed (nobody has checked). Say the grade next to the claim. Most "user needs" collapse a grade when written down honestly.
  6. Write the load-bearing assumption as one falsifiable sentence. The one that, if wrong, makes the whole idea pointless. Phrase it so it can fail: "agents abandon refunds because the audit log is on another screen" — not "agents want a better experience".
  7. Say what would falsify it, and what it would cost to look. Name the cheapest honest check that could return "no": a query against data you already have, a count of tickets matching a pattern, tracing five real cases end to end, watching the current workflow being used. Prefer a check you can run over one that needs someone else's calendar.
  8. Run the checks you are permitted to run. Reading existing data and code is yours to do. Anything that reaches a person — a survey, an interview request, a message to a customer, a posted question, an experiment served to live traffic — is outward-facing: draft it, then stop and ask. Discovery never sends on its own initiative.
  9. Report the decision the evidence supports, not the one that was hoped for. Three honest endings: the problem is real and here is what we know; the problem is real but different from the request; there is no evidence yet and here is the cheapest way to get some. "Proceed anyway" is a legitimate call for someone else to make — say what is being bet on.

Checklist

  • The request is written down separately from the problem it claims to solve
  • A specific person and a specific occasion are named, not a segment
  • Current product behaviour was actually inspected, not assumed absent
  • Existing evidence was searched before any new research was proposed
  • Every claim carries a grade: observed, reported, or assumed
  • One load-bearing assumption is written so that it could fail
  • A named check that could falsify it, with its cost
  • Anything that would contact a user was drafted and paused for approval, not sent
  • What is still unknown is listed, not smoothed over

Failure handling

  • No evidence exists anywhere. That is a finding, and a common one. Say the belief is unvalidated, give the cheapest check, and do not launder the requester's confidence into "user research shows".
  • The only evidence is one loud stakeholder. Record it as reported, with the name. It may still be right; it is not yet observed.
  • Analytics disagree with what people say. Report both and say which question each answers. Logs show what happened; people explain why. Neither overrides the other by default.
  • The problem is real but the requested solution does not address it. Say so plainly, keep the problem, and hand the solution question on. Do not quietly redesign the request.
  • Discovery would need to contact users and you have no approval. Stop. Prepare the questions and say what you would learn. An unapproved outreach is worse than an unanswered question.
  • The pressure is to confirm. If every check you propose can only return "yes", you have designed a formality. Rewrite one that can return "no".

Evidence to report

The request as received and the problem statement derived from it; the person and job named; what the product does today, with file or screen references; every source you read, quoted, with its grade; the load-bearing assumption in falsifiable form; the checks run and what they returned, distinguished from the checks proposed but not run; and the list of open unknowns. "We validated the need" with no quoted source, no grade and no check is not evidence.

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 Product Discovery AI skill do?

Find the real problem behind a feature request before anyone scopes a solution — whose job is stuck, what evidence exists already, and what observation would prove the assumption wrong. Use when a request arrives phrased as a solution ("add a dashboard", "we need notifications"), when a team is about to build on a belief nobody has checked, or when asked whether something is worth building at all. Not for writing the spec once the problem is settled, not for ranking work that is already understood, and never a licence to contact users without permission.

Why use Product Discovery on TypingMind?

Because you install it once and use it with any model. Product Discovery 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 Product Discovery in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nahid-sparktales/agent-dispatcher/tree/main/skills/product/product-discovery. 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 Product Discovery?

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 Product Discovery?

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

Is the Product Discovery AI skill free?

Yes. It is published on GitHub by nahid-sparktales 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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