Omh Product Discovery Validation logo

Omh Product Discovery Validation

CommunityPopular
rlaope
omh-product-discovery-validation

[omh] Unvalidated early product idea: test whether a customer problem, segment, and business hypothesis deserve product investment, ending in kill, pivot, persevere, or inconclusive before any PRD. Use when the user says: product-discovery-validation, product discovery validation, product discovery, customer discovery, customer discovery plan, zero to one validation, validate the problem before building, problem solution interview.

Overview

Publisherrlaope
Repositoryoh-my-hermes
Skill nameomh-product-discovery-validation
Stars
3.6K
Forks
269
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 rlaope on GitHub. Read the source before you install it.

Installation

Install the Omh Product Discovery Validation 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/rlaope/oh-my-hermes/tree/main/agent-skills/omh-product-discovery-validation
  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/rlaope/oh-my-hermes.git /tmp/oh-my-hermes
mkdir -p .claude/skills
cp -r /tmp/oh-my-hermes/agent-skills/omh-product-discovery-validation .claude/skills/omh-product-discovery-validation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Omh Product Discovery Validation 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 Omh Product Discovery Validation 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 Omh Product Discovery Validation 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 Validation

This is an OMH product-discovery-validation workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).

Why This Exists

product-discovery-validation gives an early idea a bounded, evidence-typed path to a kill, pivot, persevere, or inconclusive decision so product-brief consumes validated inputs instead of judging raw discovery itself.

Do Not Use When

  • The user needs market, competitor, pricing, or customer research on named sources without a discovery decision to make; use omh-research-brief.
  • The user is clarifying their own request, requirements, or preferences rather than testing a customer problem with external people; use ulw-interview.
  • The user needs a company or product strategy decision across existing options with evidence already in hand; use omh-decide.
  • The problem, segment, and evidence are already validated and accepted and the user wants a PRD or prioritization; use omh-product-brief.
  • An accepted product brief and plan exist and the user wants implementation, QA, and release gates; use omh-idea-to-deploy.
  • The user has one falsifiable technical or empirical question a disposable prototype can answer; use omh-decision-prototype.

Examples

Good example:

  • Prompt: I think freelance designers struggle to chase late invoices. Before we write a PRD, help me test whether this is worth building.
  • Expected behavior: Frame the decision and kill criteria, classify the existing evidence, plan past-behavior customer interviews, rank the riskiest assumptions with precommitted tests, and stop at an explicit kill, pivot, persevere, or inconclusive receipt.
  • Why: The request is a pre-PRD discovery decision about a customer problem and segment, not research on named sources, requester clarification, or a PRD.

Bad example:

  • Prompt: Write the PRD for our invoice-chasing feature; the interviews already confirmed the problem.
  • Expected behavior: Route to product-brief and ask for the accepted discovery receipt or evidence rather than rerunning discovery.
  • Why: Validated, accepted evidence with a PRD request belongs to the PRD owner, not to discovery.

Completion Checklist

  • The problem gate state is recorded as validated, refuted, or inconclusive with the external-human or behavioral-data refs that decided it, all observed in the framed target segment.
  • The target segment is explicit enough to recruit or tied to observed behavioral data before any solution, PRD, prototype, or coding output leaves this workflow.
  • Every assumption test in the portfolio carries its precommitted success, failure, inconclusive, segment, deadline, cost, owner, and evidence re-entry fields.
  • The receipt names kill, pivot, persevere, or inconclusive, preserves rejected paths, and routes to product-brief only from an accepted persevere.
  • Every artifact is reported as prepared; interviews, tests, and prototypes stay not_observed until re-entered evidence exists.

Recovery Notes

  • If external-human or behavioral-data evidence is absent, hold the problem gate at inconclusive and hand the customer discovery plan to a human owner instead of filling the gap with personas.
  • If a test passes its deadline or budget without meeting a precommitted condition, record inconclusive with the residual risk and let the decision owner choose a new budget or a kill.
  • If the target segment is unknown, synthetic-only, or non-recruitable, keep the discovery frame and customer discovery plan and report defining a recruitable or behaviorally observed audience as the next evidence task.
  • If a pivot changes the problem or segment, open a new decision frame and carry the falsified hypotheses forward as rejected paths.

When to Use

Use when a founder or product owner brings an early idea and needs the problem, segment, value proposition, and business hypothesis framed, evidence-typed, and tested to an explicit discovery decision before a PRD, prototype, or delivery plan exists.

Strong routing signals: `product-discovery-validation`, `product discovery validation`, `product discovery`, `customer discovery`, `customer discovery plan`, `zero to one validation`, `validate the problem before building`, `problem solution interview`, `customer interview guide`, `riskiest assumption test`, `assumption test portfolio`, `kill pivot persevere`, `kill or pivot decision`, `willingness to pay test`, `business hypothesis validation`, `is this idea worth building`, `고객 발견 검증`, `가정 검증 테스트`, `킬 피벗 지속 결정`

Catalog Metadata

Category: planning Phase: product-discovery-validation Quality tier: decision-gated Reasoning demand: standard

Quality bar:

  • Separate the decision frame, typed evidence, customer re-entry plan, ranked assumptions, and the receipt so each can be reviewed alone.
  • Keep every kill, pivot, persevere, or inconclusive claim tied to precommitted criteria and observed evidence.

Required inputs:

  • problem hypothesis
  • target segment
  • known evidence and current alternatives
  • decision owner
  • learning budget and deadline
  • success, failure, and stop criteria

Expert clarification questions:

  • problem hypothesis
    • English: Which customer problem or opportunity do you believe exists, for whom, and what would you expect to observe if it were false?
    • Korean: 어떤 고객 문제 또는 기회가 존재한다고 보시며, 누구에게 해당하고, 그 가설이 틀렸다면 무엇이 관찰될 것으로 예상하시나요?

Expected outputs:

  • discovery_decision_frame/v1
  • discovery_evidence_ledger/v1
  • customer_discovery_plan/v1
  • assumption_test_portfolio/v1
  • discovery_decision_receipt/v1
  • initial_gtm_hypothesis/v1

Artifact expectations:

  • prepared discovery decision frame, evidence ledger, customer discovery plan, assumption test portfolio, decision receipt, and GTM hypothesis when a wrapper captures them

Safety rules:

  • Do not recruit or contact participants, record interviews, run surveys, scrape communities, buy ads, launch fake doors, accept payments, build a prototype, write a PRD, write code, or deploy anything from this workflow.
  • Synthetic personas, model-generated interview answers, secondary summaries, prototypes without representative-user observation, and unsupported market-size figures cannot satisfy a customer-validation gate.
  • Interview praise, stated purchase intent, a waitlist signup, a finished prototype, or one passed experiment is not product-market fit; state what each signal can and cannot establish.
  • Founder-market fit and strategic preference may inform the decision but never substitute for target-customer evidence.
  • An unknown, synthetic-only, or non-recruitable target segment blocks a solution, PRD, prototype-as-validation, or coding handoff; discovery framing, the customer discovery plan, and evidence work continue while it does.

Procedure: load references/procedure.md.

Runtime Evidence

Use the current host's own tools and subagent/task mechanism when available; otherwise run the same lanes sequentially or name the unavailable capability. A prepared plan, handoff, checklist, or skill installation is not execution, review, CI, merge-readiness, or merge evidence. Record actual tool results, or not_observed / not_available, in the record; never invent dispatch or host accounting. Treat supplied context as advisory, not proof of hidden memory reads or writes. State scope, constraints, verification, and the stop condition before work. Reply in the user's own words and the host's own voice: the host's persona owns reply language, tone, speech level, and sentence endings, progress updates included (where it sets no language, use the one the user wrote in), and OMH shapes structure and content only; OMH's record terms (surface, lane, wrapper, handoff, evidence boundary, not_observed) stay in records and tool calls, never in the sentence the user reads unless they ask about one; and when a real unresolved user decision or a useful briefing discussion remains, offer one specific, content-linked question. Completed work needs no question; omit unrelated non-action footers and generic offers to continue. Supporting paths are relative to this skill directory; sibling skill paths are relative to its parent. Resolve them from the host-provided skill base directory ({baseDir} on hosts that provide it), never a hardcoded install location. A named workflow not installed here is unavailable, not permission to emulate its host-specific capabilities. Verify through the real surface before done.

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 Omh Product Discovery Validation AI skill do?

[omh] Unvalidated early product idea: test whether a customer problem, segment, and business hypothesis deserve product investment, ending in kill, pivot, persevere, or inconclusive before any PRD. Use when the user says: product-discovery-validation, product discovery validation, product discovery, customer discovery, customer discovery plan, zero to one validation, validate the problem before building, problem solution interview.

Why use Omh Product Discovery Validation on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rlaope/oh-my-hermes/tree/main/agent-skills/omh-product-discovery-validation. 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 Omh Product Discovery Validation?

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 Omh Product Discovery Validation?

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

Is the Omh Product Discovery Validation AI skill free?

Yes. It is published on GitHub by rlaope 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇