Prd V01 Problem Framing logo

Prd V01 Problem Framing

Community
mattgierhart
prd-v01-problem-framing

Transform vague product ideas into evidence-anchored problem statements for PRD v0.1 Spark. Triggers on starting new products/features, validating market opportunities, drafting PRD Why sections, or requests like "frame the problem", "define pain points", "write problem statement", "start v0.1", "what problem are we solving". Outputs structured problem tables with CFD evidence IDs.

Overview

Publishermattgierhart
RepositoryPRD-driven-context-engineering
Skill nameprd-v01-problem-framing
Stars
179
Forks
11
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the Prd V01 Problem Framing 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/mattgierhart/PRD-driven-context-engineering.git /tmp/PRD-driven-context-engineering
mkdir -p .claude/skills
cp -r /tmp/PRD-driven-context-engineering/plugins/prd-ce/skills/prd-v01-problem-framing .claude/skills/prd-v01-problem-framing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Prd V01 Problem Framing 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 Prd V01 Problem Framing 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 Prd V01 Problem Framing 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.

Problem Framing Skill

Transform market signals into evidence-anchored problem statements.

Consumes

This skill assumes you have zero prior research. It is the starting point.

  • No prior CFD- entries needed
  • No prior PR D required
  • Assumes: Founder/PM has observed market signals but hasn't validated them

Produces

This skill creates/updates:

  • CFD-* entries (customer feedback) — 1-5 per problem dimension, with confidence scoring (see PRINCIPLES.md)
  • PRD.md Why section — Evidence-anchored problem statement table
  • MVP scope signal — Identifies which problem dimensions will drive MVP feature scope (handed to v0.3)

All CFD- entries should include:

  • confidence: 1-3/5 (pre-product research, no usage data)
  • Evidence source (competitive analysis, interviews, workarounds, etc.)
  • Forward target: "Would move to 4/5 if we observe 10+ paying customers with this pain"

Workflow Overview

  1. Assess gaps → Identify what evidence is missing before you can confidently state a problem
  2. Anchor evidence → Create CFD- entries for each pain point dimension with confidence scoring
  3. Extract dimensions → Pull multiple distinct problems from each source
  4. Quantify costs → Add time/money/risk numbers to make pain concrete
  5. Draft statement → Populate the problem table, tied to CFD- entries

Core Output Template

Populate this table for every problem statement:

ElementDefinitionEvidence
Who is hurting?Specific, findable, countable personaSegment size
What pain exists?Observable behavior or workflow frictionCFD-ID
Cost of problemTime, money, or opportunity lostQuantified
Why now?Market trigger creating urgencyTrend/event
What's impossible?Opportunity cost—what can't they doUser quote

See assets/problem-statement.md for copy-paste template.

Step 1: Gap Assessment

Before drafting, create this status table:

ElementStatusSource
Who is hurting?⚠️ Hypothesis / ✅ Validated / ❌ Missing
What pain exists?⚠️ / ✅ / ❌
Cost of problem⚠️ / ✅ / ❌
Why now?⚠️ / ✅ / ❌
What's impossible?⚠️ / ✅ / ❌

Gate: Require ≥2 elements ✅ Validated before drafting. If ≥3 elements ❌ Missing, run deep research first. See references/research-prompts.md for research templates.

Step 2: Evidence Anchoring

Create CFD entries for each pain point with confidence scoring:

CFD-###: [Pain Point Name]
Source: [Where this evidence came from]
Tier: [1-5 evidence quality]
Confidence: [1-5]/5 (pre-product research)
Quote: "[Verbatim from source]"
Dimensions: [List distinct problems extracted from this source]
Next Target: "Would move to 3/5 if we interview X more customers"

Evidence Tier Hierarchy (strength of observation):

  • Tier 1: Buying behavior (invoices, subscriptions, job budgets) — users spend money to solve this
  • Tier 2: Active workarounds (spreadsheets, hired help, manual processes) — users invest labor
  • Tier 3: Complaints with cost ("costs me X hours/week") — users quantify the pain
  • Tier 4: General complaints ("this is annoying") — users acknowledge it but haven't quantified
  • Tier 5: Speculation — REJECT ("users probably want...")

Confidence Scoring (pre-product, see PRINCIPLES.md):

  • 1/5: PM assumption or single data point
  • 2/5: Secondary research (competitive analysis, market reports)
  • 3/5: Pre-product interviews (3-5 user conversations)
  • 4/5: Beta cohort validation (observed behavior, not questions)
  • 5/5: Production usage (reserved for post-launch)

Example entry with confidence:

CFD-001: Sales teams waste 5+ hours/week on spreadsheet workflow

Source: 3 customer interviews (SaaS sales director, SMB sales rep, enterprise sales manager)
Tier: 2-3 (workaround + cost quantification)
Confidence: 3/5 (source: 3-customer-interviews-jan-2026)
Quote: "I spend 5 hours every Friday reconciling our pipeline with the actual numbers in our CRM"
Dimensions:
  - Manual data reconciliation between systems (workaround)
  - Inventory work (scheduling impact)
  - Single source of truth fragmentation (data quality risk)
Next Target: "Would move to 4/5 if we validate with 5 more sales leaders or observe workflows directly"

Step 3: Pain Dimension Extraction

Extract multiple problems from each source. One quote often contains 3-4 distinct pain dimensions.

Example: "USB sticks removed for every update, no scheduling, screens don't communicate, priced for 100+ displays" → Sneakernet workflow, No dynamic scheduling, No centralization, Price mismatch

Step 4: Cost Quantification

Every problem needs a number:

TypeCalculation
TimeHours/week × hourly rate
MoneyCurrent spend on workaround
OpportunityRevenue/outcomes missed
RiskPenalty × probability

Step 5: Draft Problem Statement

Use the core output template. Reference CFD-IDs for every claim.

See references/examples.md for good/bad examples with explanations.

Quality Gates

Pass Checklist

  • ≥1 Tier 1-2 evidence item
  • Cost quantified (time, money, or risk)
  • "Who" specific enough to build prospect list
  • "Why now" has at least Tier 3 hypothesis

Testability Check

  • Can find 10 people with this problem in 48 hours?
  • Can observe the pain behavior?
  • Can quantify cost without leading questions?

Anti-Patterns

PatternExampleFix
Vague "Who""Small businesses"→ "SMBs with 1-10 screens"
Feature-as-problem"Need a dashboard"→ "Can't see status"
Solution creep"MVP must solve X"→ Stay on problem (v0.4)
Missing cost"This is annoying"→ "Costs X hrs/week"
Speculation"Users might want"→ Find evidence or reject

Bundled Resources

  • references/research-prompts.md — Deep research templates by gap type. Use when gap assessment shows ≥3 missing elements.
  • references/examples.md — Good/bad problem statement examples with explanations.
  • assets/problem-statement.md — Copy-paste template for problem tables and CFD entries.

Handoff

Problem statement complete when quality gates pass. Next: v0.2 Market Definition (segments, sizing, ICP).

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 Prd V01 Problem Framing AI skill do?

Transform vague product ideas into evidence-anchored problem statements for PRD v0.1 Spark. Triggers on starting new products/features, validating market opportunities, drafting PRD Why sections, or requests like "frame the problem", "define pain points", "write problem statement", "start v0.1", "what problem are we solving". Outputs structured problem tables with CFD evidence IDs.

Why use Prd V01 Problem Framing on TypingMind?

Because you install it once and use it with any model. Prd V01 Problem Framing 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 Prd V01 Problem Framing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mattgierhart/PRD-driven-context-engineering/tree/main/plugins/prd-ce/skills/prd-v01-problem-framing. 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 Prd V01 Problem Framing?

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 Prd V01 Problem Framing?

As many as you like. As long as a model supports skills, you can use Prd V01 Problem Framing with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Prd V01 Problem Framing AI skill free?

Yes. It is published on GitHub by mattgierhart 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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