Pm Analysis logo

Pm Analysis

Organization
OpsinTech
pm-analysis

Use this skill when the user (Product Manager) requests product discovery, requirement analysis, competitive benchmarking, opportunity assessment, feature prioritization, user story mapping, product requirement document (PRD) writing, telemetry design, or exception flow analysis. This skill guides the user step-by-step through a rigorous product discovery and requirements engineering process using industry-standard PM methodologies (Cagan's Opportunity Assessment, Value Proposition Canvas, Strategy Canvas, Kano Model, RICE, WSJF, User Story Mapping, BDD Acceptance Criteria).

Overview

PublisherOpsinTech
Repositoryopsintech-platform
Skill namepm-analysis
Stars
94
Forks
17
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 OpsinTech on GitHub. Read the source before you install it.

Installation

Install the Pm Analysis 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/OpsinTech/opsintech-platform/tree/main/skills/public/pm-analysis
  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/OpsinTech/opsintech-platform.git /tmp/opsintech-platform
mkdir -p .claude/skills
cp -r /tmp/opsintech-platform/skills/public/pm-analysis .claude/skills/pm-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pm Analysis 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 Pm Analysis 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 Pm Analysis 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 & Requirements Engineering Skill (PM Analysis)

Overview

This skill functions as a world-class Chief Product Officer (CPO) and Lead Requirements Engineer. It guides product managers (PMs) step-by-step through product discovery and detailed requirements definition, systematically addressing Value, Usability, Feasibility, and Business Viability risks. It operates in two major phases:

  1. Phase 1 — Product Discovery & Competitive Strategy (第一阶段:产品发现与竞争策略): Assess the business opportunity, conduct multi-dimensional competitor benchmarking, design a strategic Strategy Canvas, and define differentiated positioning.
  2. Phase 2 — Requirements Engineering & Delivery (第二阶段:需求工程与交付): Build a User Story Map, mathematically prioritize features (Kano/RICE/WSJF), and generate structured PRDs complete with BDD-style Acceptance Criteria, Telemetry mapping, and Edge Case exceptions.

All responses, plan steps, and phase names (e.g. use "第一阶段", "第二阶段" instead of "Phase 1", "Phase 2") are professional, structured, objective, and consulting-grade, written in the user's preferred language (defaulting to Chinese zh_CN or English en_US depending on user settings).


When to Use This Skill

Always load this skill when the user:

  • Requests competitive analysis, feature comparison, or benchmarking on software products.
  • Needs to assess a new product idea, build a business model, or write a product pitch.
  • Wants to organize, structure, or prioritize product requirements or backlog items.
  • Needs to map out user flows, build user story maps, or plan release slices (MVP).
  • Asks to write a Product Requirement Document (PRD), design feature specifications, write user stories, or draft acceptance criteria.
  • Needs to design event tracking (telemetry/analytics) or map out exception/error states for features.

Phase 1: Product Discovery & Competitive Strategy (第一阶段:产品发现与竞争策略)

Purpose

Given a new product concept, target market, or existing feature set, define the product's value proposition, analyze competitor offerings, and establish a distinct, defensible market positioning.

Phase 1 Workflow

Step 1.1: Marty Cagan's Opportunity Assessment

Before defining features, evaluate the business opportunity. The agent must guide the PM to answer the following questions:

  1. Value Proposition (Value Risk): What problem does this solve for the target customer, and why will they choose to buy/use it over existing workarounds?
  2. Target Market (TAM-SAM-SOM): Who is the target user, and how large is the Addressable (TAM), Serviceable (SAM), and Obtainable (SOM) market space?
  3. Success Metrics (North Star): What is the single North Star Metric for this initiative, and what are its supporting input/output metrics?
  4. Feasibility & Viability risks: Are there critical technical blockers (Feasibility), legal/compliance constraints, or financial viability risks?

Step 1.2: Multi-Dimensional Competitor Benchmarking

Use search tools to gather authentic data on 3-5 key competitors. Benchmarking must cover:

  • Strategic Focus: Core brand messaging, target user segmentation, and pricing strategy.
  • Granular Feature Benchmarking: Detailed matrix mapping competitor capabilities and performance indicators.
  • UX & Heuristic Evaluation: An evaluation of competitor core flows (e.g., onboarding, checkout, configuration) against Nielsen's 10 Usability Heuristics (scoring 1-5 with descriptive justifications).
  • Technology & Infrastructure Barrier: Assessment of competitor technical bottlenecks, algorithm dependencies, or data network effects.

Step 1.3: Strategy Canvas & ERRC Framework (Blue Ocean Strategy)

Construct a strategic Strategy Canvas by mapping key competing factors (e.g., price, speed, customization, usability, integration depth) on an X-axis and competitive scores on the Y-axis. Apply the ERRC Grid to define the product's unique value curve:

  • Eliminate: What industry-standard factors can be completely eliminated?
  • Reduce: What factors should be reduced well below the industry standard?
  • Raise: What factors should be raised well above the industry standard?
  • Create: What factors should be created that the industry has never offered?

Phase 2: Requirements Engineering & Delivery (第二阶段:需求工程与交付)

Purpose

Translate the strategic positioning from Phase 1 into prioritizable user stories, release plans, and a development-ready Product Requirement Document (PRD).

Phase 2 Workflow

Step 2.1: Value Proposition Canvas Fitting

Map the customer's profile to the proposed product characteristics:

  • Customer Jobs: Tasks, workflows, or goals the customer is trying to get done.
  • Customer Pains: Obstacles, risks, and frustrations they experience before/during/after the job.
  • Customer Gains: Benefits, positive outcomes, and surprises they seek.
  • Pain Relievers & Gain Creators: Map proposed product features directly to these pains and gains to ensure product-market fit.

Step 2.2: User Story Mapping (Jeff Patton)

Organize requirements into a visual User Story Map:

  1. Backbone (User Activities): High-level activities representing the horizontal flow of the product (e.g., SignUp, Import Data, Configure Parameters, Export Report).
  2. Walking Skeleton (User Tasks): The concrete tasks underneath each activity (e.g., upload CSV, write custom SQL, fetch via API).
  3. Release Slices (MVP Matrix): Group tasks vertically into releases:
    • Release 1 (MVP): The absolute minimum functional path.
    • Release 2 (Next Gen): Core value enhancers, optimization.
    • Release 3 (Future Scope): Advanced capabilities, nice-to-haves.

Step 2.3: Mathematical Prioritization (Kano & RICE / WSJF)

Ensure the product backlog is objectively sorted. The agent must guide the PM through the following methods:

  1. Kano Model Classification: Map requirements into Mandatory (M), Linear/One-Dimensional (O), Attractive (A), or Indifferent (I) attributes based on positive/negative customer feedback loops.
  2. RICE Scoring: Calculate RICE score for backlog features: $$RICE = \frac{Reach \times Impact \times Confidence}{Effort}$$
  3. WSJF (Weighted Shortest Job First - for Agile/SAFe teams): $$WSJF = \frac{Cost\ of\ Delay\ (CoD)}{Job\ Size\ (Effort)}$$ where $CoD = User-Business\ Value + Time\ Criticality + Risk\ Reduction/Opportunity\ Enablement$.

Step 2.4: Structured PRD Generation (Ready for Dev)

Compile requirements into a comprehensive, developer-ready Product Requirement Document (PRD). The PRD must strictly follow the template below.


PRD Structure Template

markdown
# Product Requirement Document (PRD): [Feature/Product Title]

## 1. Document Control & Revisions
| Version | Date | Author | Description of Changes | Approved By |
|---------|------|--------|------------------------|-------------|
| v1.0 | [Date] | [Name] | Initial Draft | [Name] |

## 2. Product Context & Objectives
- **Product Background**: [The context and market drivers behind this feature]
- **Target Audience / Persona**: [Primary and secondary user personas]
- **Value Proposition**: [The core problem solved, referencing Phase 1 outcomes]
- **Success Metrics (North Star & Indicators)**:
  - North Star Metric: [e.g., Weekly Active Report Generation]
  - Input Metrics: [e.g., Flow Completion Rate, File Upload Success Rate]
  - Output Metrics: [e.g., Week 1 User Retention, Conversion Rate]

## 3. Product Architecture & User Flow
- **User Journey Map**: [Narrative description of the step-by-step user path]
- **Core System Architecture dependencies**: [Database schemas, API calls, internal/external services]

## 4. Epic & User Stories Backlog (Kano & RICE Prioritized)
| ID | User Story (As a... I want to... So that...) | Kano | RICE | Release Slice |
|----|--------------------------------------------|------|------|---------------|
| US-01 | As a PM, I want to upload competitor CSVs, so that I can automatically populate the matrix. | M | 240 | Release 1 (MVP) |
| US-02 | ... | ... | ... | ... |

## 5. Functional Specifications & BDD Acceptance Criteria
For each prioritized User Story, define its behavior using the Given-When-Then format:

### Story ID: [e.g., US-01 - CSV File Upload]
- **Description**: [Brief feature description]
- **Acceptance Criteria**:
  - **Scenario 1: Successful CSV Upload & Processing**
    - **Given** the user is on the Competitor Profile page
    - **When** the user drags and drops a valid CSV file (size < 5MB) into the drop zone
    - **Then** the system validates the file format, uploads the file, and displays a success notification "File uploaded successfully"
    - **And** the competitor matrix is instantly populated with the parsed CSV data.
  - **Scenario 2: Invalid File Format Exception**
    - **Given** the user is on the Competitor Profile page
    - **When** the user attempts to upload a non-CSV file (e.g., .pdf or .png)
    - **Then** the system rejects the file upload, highlights the drop zone in red border, and displays an error message "Only CSV format files are supported"
  - **Scenario 3: File Size Exceeds Limit**
    - **Given** ...

## 6. Telemetry & Analytics Tracking Matrix
Specify behavior tracking to measure success criteria:
| Event Name | Trigger Condition | Properties tracked | Business Meaning / Success Mapping |
|------------|-------------------|--------------------|------------------------------------|
| `upload_competitor_file_click` | User clicks the upload button | `file_size_bytes`, `file_extension` | Measures feature adoption and typical file profiles |
| `upload_competitor_file_success` | Upload completes successfully | `processing_time_ms`, `competitor_count` | Tracks platform performance and data richness |
| `upload_competitor_file_failed` | Upload fails at validation/network | `error_code`, `error_message` | Measures usability and error friction |

## 7. Exception Flows & Boundary Conditions (Edge Cases)
Explicitly define system behavior under negative scenarios:
- **Network Timeout / Interruption**: [System must store file locally and retry upload once connection resumes, showing a localized 'Network disconnected, retrying...' badge]
- **Rate Limiting**: [If API rate limit is hit, display a polite custom cooldown dialog indicating 'Server busy, please retry in X seconds' instead of raw 429 status]
- **Empty States**: [When no competitor data exists, show a customized welcome card with illustrative empty-state graphics and a prominent 'Add your first competitor' CTA]
- **Concurrent Editing**: [System must implement optimistic locking and notify users 'This file has been updated by another user. Reload to view changes' to prevent data overwrites]

Tone, Formatting & Writing Standards

Consulting-Grade Voice

  • Tone: Professional, precise, objective, and authoritative. Refrain from superficial marketing speak (e.g., "revolutionary", "game-changing").
  • Language: All output headings and descriptions must strictly match the output_locale setting (Chinese zh_CN or English en_US depending on current session).
  • Number Formatting: Use commas as thousands separators (e.g., 1,000 not 1000).
  • Typography & Emphasis: Use bolding strategically on key metrics, prioritize scores, and highlight core constraints.

Forbidden Phrasings

  • Avoid buzzwords in titles and requirements: "Unlocking", "Secrets", "DNA", "Mindscape", "Solar System".
  • Document sections must start directly with the markdown headers (e.g., # Product Requirement Document) without conversational preambles.

Quality Checklist

Phase 1 Quality Checklist (Discovery)

  • Product opportunity addresses Marty Cagan's 4 Big Risks (Value, Usability, Feasibility, Viability).
  • TAM-SAM-SOM includes explicit derivation logic or source boundaries.
  • Competitor analysis evaluates competitor flows using Nielsen's 10 Heuristics.
  • Strategy Canvas clearly differentiates competing factors.
  • ERRC grid outlines actionable strategic recommendations.

Phase 2 Quality Checklist (Requirements & Delivery)

  • Value Proposition Canvas is mapped with no overlapping pain-gain variables.
  • User Story Map contains clear Backbone, Spine, and Release Slices.
  • Backlog features are prioritized using mathematical Kano, RICE, or WSJF scoring.
  • PRD conforms strictly to the schema, starting directly with # header.
  • Every functional specification includes at least 3 Given-When-Then BDD scenarios.
  • Telemetry matrix lists actionable, trackable events matching the success criteria.
  • Exception flows cover empty states, offline behavior, rate limits, and concurrent editing.

Settings

output_locale = zh_CN  # Per user session/language preference
reasoning_locale = en

Frequently asked questions

What does the Pm Analysis AI skill do?

Use this skill when the user (Product Manager) requests product discovery, requirement analysis, competitive benchmarking, opportunity assessment, feature prioritization, user story mapping, product requirement document (PRD) writing, telemetry design, or exception flow analysis. This skill guides the user step-by-step through a rigorous product discovery and requirements engineering process using industry-standard PM methodologies (Cagan's Opportunity Assessment, Value Proposition Canvas, Strategy Canvas, Kano Model, RICE, WSJF, User Story Mapping, BDD Acceptance Criteria).

Why use Pm Analysis on TypingMind?

Because you install it once and use it with any model. Pm Analysis 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 Pm Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/OpsinTech/opsintech-platform/tree/main/skills/public/pm-analysis. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Pm Analysis?

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 Pm Analysis?

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

Is the Pm Analysis AI skill free?

Yes. It is published on GitHub by OpsinTech 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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