Prd V02 Competitive Landscape Mapping logo

Prd V02 Competitive Landscape Mapping

Community
mattgierhart
prd-v02-competitive-landscape-mapping

Map the competitive landscape before positioning your product for PRD v0.2 Market Definition. Triggers on completing v0.1 Spark, analyzing competitors, researching market, or requests like "competitive analysis", "who else solves this", "market landscape", "what alternatives exist", "competitor research", "feature comparison". Outputs CFD- entries for competitive intelligence and BR- entries for positioning rules.

Overview

Publishermattgierhart
RepositoryPRD-driven-context-engineering
Skill nameprd-v02-competitive-landscape-mapping
Stars
179
Forks
11
Bundled files
4
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.

  • 4 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 V02 Competitive Landscape Mapping 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-v02-competitive-landscape-mapping .claude/skills/prd-v02-competitive-landscape-mapping
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Prd V02 Competitive Landscape Mapping 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 V02 Competitive Landscape Mapping 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 V02 Competitive Landscape Mapping 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.

Competitive Landscape Mapping

Understand market reality before defining your position.

Workflow Position

v0.1 Spark (Problem + Value) → Competitive Landscape Mapping → Product Type Classification
       (what hurts)                 (who else solves it)           (how we compete)

Consumes

This skill requires prior work from v0.1:

  • **CFD-* entries (problem statements, from Problem Framing) — Evidence for what users need/want
  • **CFD-* entries (value hypotheses, from User Value Articulation) — Desired outcomes users are seeking
  • PRD.md Why section — Problem framing and market signals

This skill assumes v0.1 Spark is complete (both problem and value).

Produces

This skill creates/updates:

  • CFD-* entries (competitive intelligence) — Analysis of direct competitors, adjacent solutions, workarounds
  • BR-* entries (positioning rules) — Constraints derived from landscape analysis
  • Landscape map artifact — Current behavior documentation, feature matrix, 1% better hypothesis

All CFD competitive intelligence entries should include:

  • confidence: 2-3/5 (based on evidence tier from public sources + user validation)
  • Evidence source (G2 reviews, pricing pages, user interviews, etc.)
  • Forward target: "Would move to 4/5 if we validate gap with 5+ target users"

Example competitive intelligence entry:

markdown
CFD-042: Competitive Intelligence — Competitor Landscape Analysis

Type: Competitive Intelligence
Date: 2026-02-01
Confidence: 3/5 (source: public-research + 3-customer-interviews)
Competitors Analyzed: 4 direct + 2 adjacent
Primary Gap: All competitors require enterprise licensing; SMB segment underserved

Feature Matrix: [Link to matrix]
1% Hypothesis: "SMB sales teams can get 80% of [Competitor A] features for 40% of price"
Evidence:
  - CFD-001: 3 SMB teams paying $500/mo but using only 5 of 20 features
  - CFD-015: Value hypothesis shows $12,500/year need for core 5 features only
Next Target: "Would move to 4/5 if we validate with 5+ SMB prospects willing to pay $200/mo"

Workflow Overview

  1. Document current behavior → What users do TODAY (before competitor search)
  2. Discover alternatives → Direct, adjacent, workarounds, "do nothing"
  3. Analyze gaps → Industry/geography gaps, underserved segments
  4. Compare features → Build comparison matrix
  5. Form hypothesis → 1% better hypothesis with evidence

Core Output Template

ElementDefinitionEvidence
Current BehaviorHow users solve this todayObserved workflow
Direct CompetitorsProducts solving same problemRevenue/funding proof
Adjacent SolutionsProducts solving related problemsUser overlap
WorkaroundsDIY solutions (spreadsheets, manual)Forum/reddit mentions
Feature MatrixSide-by-side capability comparisonProduct documentation
Gap AnalysisWhere competition is weakReviews, complaints
1% HypothesisHow we winEvidence-anchored

See assets/landscape.md for copy-paste template.

Step 1: Document Current Behavior

Before searching competitors, document what target users do TODAY.

Capture Format

Current Behavior: [What they do]
Tools Used: [Existing tools, if any]
Time Investment: [Hours/week on workaround]
Pain Points: [From v0.1 CFD-IDs]

Why First?

  • Prevents solution bias from competitor features
  • Reveals workarounds competitors might miss
  • Establishes true baseline for improvement claims

Step 2: Competitor Discovery

Discovery Categories

CategoryDefinitionSearch Strategy
DirectSame problem, same segment"[problem] software"
AdjacentRelated problem, potential pivot"[related workflow] tool"
WorkaroundsDIY solutionsReddit: "how I [task]"
Do NothingAccept status quoWhy hasn't this been solved?

Minimum Discovery Checklist

  • 3+ direct competitors (or document why fewer exist)
  • 2+ adjacent solutions
  • 1+ workaround documented
  • "Do nothing" cost quantified

Create CFD Entry Per Competitor

CFD-###: Competitor — [Name]
Type: Competitive Intelligence
Source: [Website, G2, Crunchbase]
Date: YYYY-MM-DD

Overview: [1-2 sentences]
Target Segment: [Who they serve]
Pricing: [Model and range]
Revenue/Funding: [If available]
Key Differentiator: [Their claim]
Weakness Signals: [Reviews, complaints]

Step 3: Gap Analysis

Industry/Geography Gap Table

IndustryCompetitors ServingGap Level
[Industry 1]X of YNone / Small / Large
[Industry 2]X of YNone / Small / Large

Segment Gap Table

SegmentServed ByUnderserved Signal
Enterprise[List][Signal or "Well served"]
Mid-Market[List][Signal or "Well served"]
SMB[List][Signal or "Well served"]
Prosumer[List][Signal or "Well served"]

Underserved Signals

  • Tier 1: Users paying but complaining (G2 reviews)
  • Tier 2: Users building workarounds (Reddit, forums)
  • Tier 3: Users asking for solutions (community posts)
  • Tier 4: No apparent demand (caution)

Step 4: Feature Comparison Matrix

Build side-by-side comparison:

FeatureUs (Planned)Competitor ACompetitor BGap
[Feature 1]✅/❌/🔄✅/❌✅/❌[Our advantage]
[Feature 2]✅/❌/🔄✅/❌✅/❌[Our advantage]

Legend: ✅ = Has | ❌ = Missing | 🔄 = Planned

Matrix Requirements

  • Include all "table stakes" features (what everyone has)
  • Identify 1-3 differentiating features
  • Note pricing tier where features unlock
  • Flag features competitors are building (roadmap signals)

Step 5: 1% Better Hypothesis

Template

We can be 1% better than [Competitor X] by [specific improvement] for [specific segment].

Evidence:
- [CFD-ID]: [Supporting evidence]
- [CFD-ID]: [Supporting evidence]

Why This Matters:
- [Segment] cares about this because [reason]
- Current solutions fail at this because [reason]

Risk:
- [What could invalidate this hypothesis]

Hypothesis Quality Check

  • "1% better" is specific and measurable
  • References CFD-IDs for evidence
  • Targets a defined segment
  • Explains WHY this gap exists
  • Acknowledges risks

Quality Gates

Pass Checklist

  • ≥3 competitors documented with CFD-IDs
  • Feature matrix with ≥5 compared features
  • ≥1 gap identified with Tier 1-2 evidence
  • 1% better hypothesis formed
  • Current behavior documented FIRST

Testability Check

  • Can validate 1% hypothesis in <30 days?
  • Can find 10 people in target segment?
  • Gap evidence is from users, not assumptions?

Anti-Patterns

PatternSignalFix
Competitor-first thinkingStarted with competitor featuresDocument current behavior first
False uniqueness"No competitors" claimInclude workarounds and adjacent
Feature bloatMatrix has 20+ featuresFocus on differentiators
Vague gaps"Better UX" without evidenceAdd specific user complaint
10x claims"10x better than X"Start with 1% provable claim
Ignored workaroundsOnly listed software competitorsInclude spreadsheets, manual

CFD/BR Output Format

CFD Entry (Competitive Intelligence)

CFD-###: Competitive Intelligence — [Market/Segment]
Type: Competitive Intelligence
Date: YYYY-MM-DD

Competitors Analyzed: [Count]
Primary Gap: [Description]
Evidence Tier: [1-5]

Feature Matrix: [Link or inline]
1% Hypothesis: [Statement]

BR Entry (Positioning Rule)

BR-###: Positioning Rule — [Title]
Type: Business Rule
Source: CFD-###
Date: YYYY-MM-DD

Rule: [Specific constraint derived from landscape]
Rationale: [Why this matters]
Applies To: [Scope]

Bundled Resources

  • references/research-prompts.md — Deep research templates for competitor discovery and gap analysis.
  • references/examples.md — Good/bad competitive analysis examples.
  • assets/landscape.md — Copy-paste template for landscape mapping.
  • assets/feature-matrix.md — Feature comparison matrix template.

Handoff

Competitive landscape complete when quality gates pass. Landscape map informs:

  • Product Type Classification (next skill) — What type are we? Clone, Slice, etc.
  • v0.3 Pricing — Competitive pricing anchors
  • v0.3 Moat — Where competitors are weak

Next: Product Type Classification (How should we compete based on landscape?)

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 V02 Competitive Landscape Mapping AI skill do?

Map the competitive landscape before positioning your product for PRD v0.2 Market Definition. Triggers on completing v0.1 Spark, analyzing competitors, researching market, or requests like "competitive analysis", "who else solves this", "market landscape", "what alternatives exist", "competitor research", "feature comparison". Outputs CFD- entries for competitive intelligence and BR- entries for positioning rules.

Why use Prd V02 Competitive Landscape Mapping on TypingMind?

Because you install it once and use it with any model. Prd V02 Competitive Landscape Mapping 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 V02 Competitive Landscape Mapping 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-v02-competitive-landscape-mapping. 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 V02 Competitive Landscape Mapping?

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 V02 Competitive Landscape Mapping?

As many as you like. As long as a model supports skills, you can use Prd V02 Competitive Landscape Mapping with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Prd V02 Competitive Landscape Mapping 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.

View all

Set up your own AI workspace now

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