Agent skill

Product Manager Toolkit

by borghei in borghei/Claude-Skills

Product manager toolkit covering RICE prioritization, customer interview analysis, PRDs, and discovery frameworks.

MITAuto-check passedProduct & Project Management

Install Product Manager Toolkit

skills CLI
$ npx skills add borghei/Claude-Skills --skill product-manager-toolkit -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install borghei/Claude-Skills product-manager-toolkit --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/product-team/product-manager-toolkit .claude/skills/product-manager-toolkit && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
product-manager-toolkit
GitHub stars
886
Token cost
~5.8k tokens
SKILL.md length
1,666 words
Files
5 (incl. scripts, references)
Skills in repo
354
Repo updated
First seen
Licence
MIT

At a glance

Product manager toolkit covering RICE prioritization, customer interview analysis, PRDs, and discovery frameworks.

  • Works in 12 steps: Gather Feature Requests → Score with RICE → Analyze Portfolio → …
  • Feature prioritization
  • SKILL.md covers Table of Contents, Clarify First, Quick Start and Core Workflows, plus 11 more sections
  • Runs Python scripts from its folder; calls python and just

What it does

Product Manager Toolkit is an agent skill from borghei/Claude-Skills. Product manager toolkit covering RICE prioritization, customer interview analysis, PRDs, and discovery frameworks. Use for feature prioritization, user research synthesis, requirement documentation, or product strategy.

Its SKILL.md is about 5.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/frameworks.md`, `references/prd_templates.md` and `scripts/customer_interview_analyzer.py`).

It sits in Product & Project Management, covering Prioritization frameworks, User research and PRD writing. The repository describes itself as: 385 AI skills, 77 expert agents, and 900 stdlib Python tools for every team: engineering, PM, marketing, C-level, compliance, business ops, research, and a LinkedIn toolkit… The licence is MIT.

When your agent uses it

  • Feature prioritization
  • User research synthesis
  • Requirement documentation
  • Product strategy

Example prompts

  • “/product-manager-toolkit”

Requirements

  • Python 3

Workflow steps

12 steps, taken from the step headings in SKILL.md.

  1. Gather Feature Requests
  2. Score with RICE
  3. Analyze Portfolio
  4. Generate Roadmap
  5. Validate Results
  6. Execute and Iterate
  7. Plan Research
  8. Recruit Participants
  9. Conduct Interviews
  10. Analyze Insights
  11. Synthesize Findings
  12. Validate Solutions

What it can do on your machine

Read from SKILL.md and the folder at commit 4a698e8. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python
    • just

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Product Manager Toolkit loads about 5.8k tokens when it runs, and up to ~12k if it reads all its reference files. Until then it costs about 61 tokens; SKILL.md has 1,666 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~61
When it runs · the whole SKILL.md, loaded when a task matches
~5.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~12k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from borghei/Claude-Skills at commit 4a698e8, republished under its MIT licence (© borghei). 1,666 words, ~5,750 tokens.

Download SKILL.mdSave it as .claude/skills/product-manager-toolkit/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
product-manager-toolkit
description
Product manager toolkit covering RICE prioritization, customer interview analysis, PRDs, and discovery frameworks. Use for feature prioritization, user research synthesis, requirement documentation, or product strategy.
license
MIT + Commons Clause
metadata.version
1.0.0
metadata.author
borghei
metadata.category
product
metadata.domain
product-management
metadata.updated
2026-03-31
metadata.tags
product-management, rice, okr, roadmap, prioritization

Product Manager Toolkit

Essential tools and frameworks for modern product management, from discovery to delivery.


Table of Contents


Clarify First

Before generating, confirm these inputs. If any is unknown or vague, ASK — do not assume:

  • Which deliverable — RICE prioritization, interview synthesis, PRD, or positioning statement (sets which workflow, template, and inputs apply)
  • The core problem and who has it — one sentence in the user's words (drives the PRD problem statement and JTBD)
  • Success metric — the measurable outcome that defines "it worked" (drives PRD success metrics and RICE impact)
  • Scope boundary — what is explicitly out (drives RICE effort estimates and PRD out-of-scope)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Quick Start

For Feature Prioritization
bash
# Create sample data file
python scripts/rice_prioritizer.py sample

# Run prioritization with team capacity
python scripts/rice_prioritizer.py sample_features.csv --capacity 15
For Interview Analysis
bash
python scripts/customer_interview_analyzer.py interview_transcript.txt
For PRD Creation
  1. Choose template from references/prd_templates.md
  2. Fill sections based on discovery work
  3. Review with engineering for feasibility
  4. Version control in project management tool

Core Workflows

Feature Prioritization Process
Gather → Score → Analyze → Plan → Validate → Execute
Step 1: Gather Feature Requests
  • Customer feedback (support tickets, interviews)
  • Sales requests (CRM pipeline blockers)
  • Technical debt (engineering input)
  • Strategic initiatives (leadership goals)
Step 2: Score with RICE
bash
# Input: CSV with features
python scripts/rice_prioritizer.py features.csv --capacity 20

See references/frameworks.md for RICE formula and scoring guidelines.

Step 3: Analyze Portfolio

Review the tool output for:

  • Quick wins vs big bets distribution
  • Effort concentration (avoid all XL projects)
  • Strategic alignment gaps
Step 4: Generate Roadmap
  • Quarterly capacity allocation
  • Dependency identification
  • Stakeholder communication plan
Step 5: Validate Results

Before finalizing the roadmap:

  • Compare top priorities against strategic goals
  • Run sensitivity analysis (what if estimates are wrong by 2x?)
  • Review with key stakeholders for blind spots
  • Check for missing dependencies between features
  • Validate effort estimates with engineering
Step 6: Execute and Iterate
  • Share roadmap with team
  • Track actual vs estimated effort
  • Revisit priorities quarterly
  • Update RICE inputs based on learnings

Customer Discovery Process
Plan → Recruit → Interview → Analyze → Synthesize → Validate
Step 1: Plan Research
  • Define research questions
  • Identify target segments
  • Create interview script (see references/frameworks.md)
Step 2: Recruit Participants
  • 5-8 interviews per segment
  • Mix of power users and churned users
  • Incentivize appropriately
Step 3: Conduct Interviews
  • Use semi-structured format
  • Focus on problems, not solutions
  • Record with permission
  • Take minimal notes during interview
Step 4: Analyze Insights
bash
python scripts/customer_interview_analyzer.py transcript.txt

Extracts:

  • Pain points with severity
  • Feature requests with priority
  • Jobs to be done patterns
  • Sentiment and key themes
  • Notable quotes
Step 5: Synthesize Findings
  • Group similar pain points across interviews
  • Identify patterns (3+ mentions = pattern)
  • Map to opportunity areas using Opportunity Solution Tree
  • Prioritize opportunities by frequency and severity
Step 6: Validate Solutions

Before building:

  • Create solution hypotheses (see references/frameworks.md)
  • Test with low-fidelity prototypes
  • Measure actual behavior vs stated preference
  • Iterate based on feedback
  • Document learnings for future research

PRD Development Process
Scope → Draft → Review → Refine → Approve → Track
Step 1: Choose Template

Select from references/prd_templates.md:

TemplateUse CaseTimeline
Standard PRDComplex features, cross-team6-8 weeks
One-Page PRDSimple features, single team2-4 weeks
Feature BriefExploration phase1 week
Agile EpicSprint-based deliveryOngoing
Step 2: Draft Content
  • Lead with problem statement
  • Define success metrics upfront
  • Explicitly state out-of-scope items
  • Include wireframes or mockups
Step 3: Review Cycle
  • Engineering: feasibility and effort
  • Design: user experience gaps
  • Sales: market validation
  • Support: operational impact
Step 4: Refine Based on Feedback
  • Address technical constraints
  • Adjust scope to fit timeline
  • Document trade-off decisions
Step 5: Approval and Kickoff
  • Stakeholder sign-off
  • Sprint planning integration
  • Communication to broader team
Step 6: Track Execution

After launch:

  • Compare actual metrics vs targets
  • Conduct user feedback sessions
  • Document what worked and what didn't
  • Update estimation accuracy data
  • Share learnings with team

Positioning Statement Framework

Create a Geoffrey Moore-style positioning statement to clarify product differentiation and value. Use this before writing PRDs, go-to-market plans, or pitch decks.

Core Positioning Template
For [target user/persona]
who [underserved need or painful moment],
[product name] is a [product category]
that [primary outcome delivered].
Unlike [main alternative: competitor, workaround, or status quo],
[product name] [unique differentiation in outcome terms].
One-Sentence Value Proposition

Write a single sentence a PM can reuse in docs and slides.

Differentiation Proof Points

List 3 concrete proof points that support the "unlike" claim. Focus on outcomes and evidence, not adjectives.

Writing Rules
  • Use persona-first language.
  • Focus on outcomes, not feature lists.
  • Keep wording specific and testable.
  • "Unlike X" should name the real alternative, including status quo.
  • Strong differentiation is about outcomes and evidence, not adjectives.
Optional Variants
  • Executive variant: Shorter strategic wording for board decks.
  • Customer-facing variant: Clear plain-language wording for marketing.
Next Steps
  1. Generate 3 alternate positioning directions (Recommended)
  2. Create a competitor comparison message matrix
  3. Convert into homepage headline + subheadline options

Recommendation Canvas

Evaluate product opportunities holistically using a structured canvas that connects problem framing to solution evidence. Useful for investment decisions, portfolio reviews, and stakeholder alignment.

Canvas Sections
markdown
## Product Name
[Name of the product or service]

## Business Outcome
[Direction] [Metric] [Outcome] [Context] [Acceptance criteria]

## Product Outcome
[Direction] [Metric] [Outcome] [Context] [Acceptance criteria]

## Problem Statement Narrative
[2-3 sentences telling the persona's story from their point-of-view]

## Solution Hypothesis
If we [action/solution] for [target persona],
then we will [desirable outcome].

### Tiny Acts of Discovery
- [Small experiment focused on viability]
- [Small experiment focused on customer value]

### Proof-of-Life
Within [timeframe], we observe:
- [Quantitative measurable outcome]
- [Qualitative measurable outcome]

## Positioning Statement
For [target persona] that need [underserved need],
[product] is a [category] that [benefit].
Unlike [competitor], [product] provides [differentiation].

## Assumptions & Unknowns
- [Assumption 1]
- [Assumption 2]

## Issues/Risks (PESTEL lens)
- Political: [Risk]
- Economic: [Risk]
- Social: [Risk]
- Technological: [Risk]
- Environmental: [Risk]
- Legal: [Risk]

## Value Justification
[Yes/Yes with caveats/No with alternatives/No]
Justification: [Why this is or isn't valuable]

## Success Metrics
1. [SMART metric 1]
2. [SMART metric 2]
3. [SMART metric 3]

## What's Next
1. [Next step with owner]
2. [Next step with owner]
When to Use
  • Evaluating whether to invest in a new product or feature.
  • Preparing for portfolio review or investment committee.
  • Aligning stakeholders on go/no-go decisions.

Tools Reference

RICE Prioritizer

Advanced RICE framework implementation with portfolio analysis.

Features:

  • RICE score calculation with configurable weights
  • Portfolio balance analysis (quick wins vs big bets)
  • Quarterly roadmap generation based on capacity
  • Multiple output formats (text, JSON, CSV)

CSV Input Format:

csv
name,reach,impact,confidence,effort,description
User Dashboard Redesign,5000,high,high,l,Complete redesign
Mobile Push Notifications,10000,massive,medium,m,Add push support
Dark Mode,8000,medium,high,s,Dark theme option

Commands:

bash
# Create sample data
python scripts/rice_prioritizer.py sample

# Run with default capacity (10 person-months)
python scripts/rice_prioritizer.py features.csv

# Custom capacity
python scripts/rice_prioritizer.py features.csv --capacity 20

# JSON output for integration
python scripts/rice_prioritizer.py features.csv --output json

# CSV output for spreadsheets
python scripts/rice_prioritizer.py features.csv --output csv

Customer Interview Analyzer

NLP-based interview analysis for extracting actionable insights.

Capabilities:

  • Pain point extraction with severity assessment
  • Feature request identification and classification
  • Jobs-to-be-done pattern recognition
  • Sentiment analysis per section
  • Theme and quote extraction
  • Competitor mention detection

Commands:

bash
# Analyze interview transcript
python scripts/customer_interview_analyzer.py interview.txt

# JSON output for aggregation
python scripts/customer_interview_analyzer.py interview.txt json

Input/Output Examples

RICE Prioritizer Example

Input (features.csv):

csv
name,reach,impact,confidence,effort
Onboarding Flow,20000,massive,high,s
Search Improvements,15000,high,high,m
Social Login,12000,high,medium,m
Push Notifications,10000,massive,medium,m
Dark Mode,8000,medium,high,s

Command:

bash
python scripts/rice_prioritizer.py features.csv --capacity 15

Output:

============================================================
RICE PRIORITIZATION RESULTS
============================================================

📊 TOP PRIORITIZED FEATURES

1. Onboarding Flow
   RICE Score: 16000.0
   Reach: 20000 | Impact: massive | Confidence: high | Effort: s

2. Search Improvements
   RICE Score: 4800.0
   Reach: 15000 | Impact: high | Confidence: high | Effort: m

3. Social Login
   RICE Score: 3072.0
   Reach: 12000 | Impact: high | Confidence: medium | Effort: m

4. Push Notifications
   RICE Score: 3840.0
   Reach: 10000 | Impact: massive | Confidence: medium | Effort: m

5. Dark Mode
   RICE Score: 2133.33
   Reach: 8000 | Impact: medium | Confidence: high | Effort: s

📈 PORTFOLIO ANALYSIS

Total Features: 5
Total Effort: 19 person-months
Total Reach: 65,000 users
Average RICE Score: 5969.07

🎯 Quick Wins: 2 features
   • Onboarding Flow (RICE: 16000.0)
   • Dark Mode (RICE: 2133.33)

🚀 Big Bets: 0 features

📅 SUGGESTED ROADMAP

Q1 - Capacity: 11/15 person-months
   • Onboarding Flow (RICE: 16000.0)
   • Search Improvements (RICE: 4800.0)
   • Dark Mode (RICE: 2133.33)

Q2 - Capacity: 10/15 person-months
   • Push Notifications (RICE: 3840.0)
   • Social Login (RICE: 3072.0)

Customer Interview Analyzer Example

Input (interview.txt):

Customer: Jane, Enterprise PM at TechCorp
Date: 2024-01-15

Interviewer: What's the hardest part of your current workflow?

Jane: The biggest frustration is the lack of real-time collaboration.
When I'm working on a PRD, I have to constantly ping my team on Slack
to get updates. It's really frustrating to wait for responses,
especially when we're on a tight deadline.

I've tried using Google Docs for collaboration, but it doesn't
integrate with our roadmap tools. I'd pay extra for something that
just worked seamlessly.

Interviewer: How often does this happen?

Jane: Literally every day. I probably waste 30 minutes just on
back-and-forth messages. It's my biggest pain point right now.

Command:

bash
python scripts/customer_interview_analyzer.py interview.txt

Output:

============================================================
CUSTOMER INTERVIEW ANALYSIS
============================================================

📋 INTERVIEW METADATA
Segments found: 1
Lines analyzed: 15

😟 PAIN POINTS (3 found)

1. [HIGH] Lack of real-time collaboration
   "I have to constantly ping my team on Slack to get updates"

2. [MEDIUM] Tool integration gaps
   "Google Docs...doesn't integrate with our roadmap tools"

3. [HIGH] Time wasted on communication
   "waste 30 minutes just on back-and-forth messages"

💡 FEATURE REQUESTS (2 found)

1. Real-time collaboration - Priority: High
2. Seamless tool integration - Priority: Medium

🎯 JOBS TO BE DONE

When working on PRDs with tight deadlines
I want real-time visibility into team updates
So I can avoid wasted time on status checks

📊 SENTIMENT ANALYSIS

Overall: Negative (pain-focused interview)
Key emotions: Frustration, Time pressure

💬 KEY QUOTES

• "It's really frustrating to wait for responses"
• "I'd pay extra for something that just worked seamlessly"
• "It's my biggest pain point right now"

🏷️ THEMES

- Collaboration friction
- Tool fragmentation
- Time efficiency

Integration Points

Compatible tools and platforms:

CategoryPlatforms
AnalyticsAmplitude, Mixpanel, Google Analytics
RoadmappingProductBoard, Aha!, Roadmunk, Productplan
DesignFigma, Sketch, Miro
DevelopmentJira, Linear, GitHub, Asana
ResearchDovetail, UserVoice, Pendo, Maze
CommunicationSlack, Notion, Confluence

JSON export enables integration with most tools:

bash
# Export for Jira import
python scripts/rice_prioritizer.py features.csv --output json > priorities.json

# Export for dashboard
python scripts/customer_interview_analyzer.py interview.txt json > insights.json

Common Pitfalls to Avoid

PitfallDescriptionPrevention
Solution-FirstJumping to features before understanding problemsStart every PRD with problem statement
Analysis ParalysisOver-researching without shippingSet time-boxes for research phases
Feature FactoryShipping features without measuring impactDefine success metrics before building
Ignoring Tech DebtNot allocating time for platform healthReserve 20% capacity for maintenance
Stakeholder SurpriseNot communicating early and oftenWeekly async updates, monthly demos
Metric TheaterOptimizing vanity metrics over real valueTie metrics to user value delivered

Show full SKILL.md (647 more words)Show less

Best Practices

Writing Great PRDs:

  • Start with the problem, not the solution
  • Include clear success metrics upfront
  • Explicitly state what's out of scope
  • Use visuals (wireframes, flows, diagrams)
  • Keep technical details in appendix
  • Version control all changes

Effective Prioritization:

  • Mix quick wins with strategic bets
  • Consider opportunity cost of delays
  • Account for dependencies between features
  • Buffer 20% for unexpected work
  • Revisit priorities quarterly
  • Communicate decisions with context

Customer Discovery:

  • Ask "why" five times to find root cause
  • Focus on past behavior, not future intentions
  • Avoid leading questions ("Wouldn't you love...")
  • Interview in the user's natural environment
  • Watch for emotional reactions (pain = opportunity)
  • Validate qualitative with quantitative data

Quick Reference

bash
# Prioritization
python scripts/rice_prioritizer.py features.csv --capacity 15

# Interview Analysis
python scripts/customer_interview_analyzer.py interview.txt

# Generate sample data
python scripts/rice_prioritizer.py sample

# JSON outputs
python scripts/rice_prioritizer.py features.csv --output json
python scripts/customer_interview_analyzer.py interview.txt json

Reference Documents

  • references/prd_templates.md - PRD templates for different contexts
  • references/frameworks.md - Detailed framework documentation (RICE, MoSCoW, Kano, JTBD, etc.)

Tool Reference

rice_prioritizer.py

RICE framework implementation with portfolio analysis and quarterly roadmap generation.

FlagTypeDefaultDescription
inputpositional(optional)CSV file with features or "sample" to create sample
--capacityint10Team capacity per quarter in person-months
--outputchoicetextOutput format: text, json, csv

CSV columns: name, reach, impact, confidence, effort, description

Impact values: massive, high, medium, low, minimal Confidence values: high (100%), medium (80%), low (50%) Effort values: xl (13mo), l (8mo), m (5mo), s (3mo), xs (1mo)

bash
python scripts/rice_prioritizer.py sample                          # Create sample CSV
python scripts/rice_prioritizer.py features.csv                    # Default capacity (10)
python scripts/rice_prioritizer.py features.csv --capacity 20      # Custom capacity
python scripts/rice_prioritizer.py features.csv --output json      # JSON for integration
python scripts/rice_prioritizer.py features.csv --output csv       # CSV for spreadsheets
customer_interview_analyzer.py

Keyword-based interview transcript analysis for extracting actionable insights.

ArgumentTypeDefaultDescription
interview_filepositional(required)Path to interview transcript text file
jsonpositional(optional)Add "json" as second arg for JSON output

Extraction capabilities: pain points (with severity), feature requests (with type and priority), jobs-to-be-done patterns, sentiment analysis, key themes, notable quotes, metrics mentioned, competitor mentions.

bash
python scripts/customer_interview_analyzer.py interview.txt        # Human-readable
python scripts/customer_interview_analyzer.py interview.txt json   # JSON output

Troubleshooting

ProblemCauseSolution
RICE scores cluster togetherImpact/confidence not differentiated enoughCalibrate scoring rubric with team; use specific examples for each level
Roadmap overcommits capacityEffort estimates too optimisticAdd 20% buffer; validate estimates with engineering before finalizing
Interview analysis misses key insightsTranscript is too short or uses unexpected phrasingSupplement with manual review; ensure transcripts capture full context
Stakeholders disagree with prioritiesDifferent value perceptionsShare raw RICE inputs transparently; allow stakeholders to adjust weights
Quick wins dominate roadmapBias toward low-effort itemsReserve 30-40% of capacity for strategic big bets
PRD scope creeps after approvalInsufficient out-of-scope definitionExplicitly list excluded items; require change request for additions
Feature factory behaviorShipping without measuring impactDefine success metrics in PRD before development starts

Success Criteria

CriterionTargetHow to Measure
Prioritization velocity<2 hours from data to ranked backlogTime from CSV input to roadmap output
Interview analysis coverage>80% of pain points capturedCompare tool output to manual expert review
Estimation accuracyActual effort within 1.5x of RICE estimateTrack actual vs estimated effort post-delivery
Roadmap confidence>70% of Q1 roadmap items shipped in quarterShipped items / Planned items
Discovery cadence5-8 interviews per segment per quarterCount completed interviews
PRD quality0 scope change requests after approvalTrack change requests per PRD
Feature impact rate>60% of shipped features hit success metricsPost-launch metric comparison

Scope & Limitations

In scope:

  • RICE prioritization with portfolio analysis
  • Quarterly roadmap generation with capacity planning
  • Customer interview transcript analysis
  • Pain point, feature request, and JTBD extraction
  • Sentiment analysis using keyword heuristics
  • PRD development process and templates
  • CSV/JSON import and export

Out of scope:

  • Real-time analytics integration (use Amplitude/Mixpanel APIs)
  • NLP model-based analysis (tool uses keyword heuristics, not ML)
  • Multi-language transcript analysis (English only)
  • Visual wireframe or prototype generation
  • Competitive intelligence gathering (see business-growth skills)
  • Revenue impact modeling (see finance skills)

Integration Points

Tool / PlatformIntegration MethodUse Case
Jira / Linear--output json from rice_prioritizerImport prioritized features as tickets
Google Sheets--output csv from rice_prioritizerShare roadmap with stakeholders
Dovetail / NotionJSON output from interview analyzerAggregate interview insights in research repo
agile-product-ownerRICE priorities feed sprint backlogConnect strategy to execution
product-strategistOKR cascade informs RICE reach/impactAlign features with strategic objectives
Slack / EmailHuman-readable output from both toolsAsync stakeholder communication

© borghei, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (scripts, references) in product-team/product-manager-toolkit of borghei/Claude-Skills.

  • SKILL.md
  • references/frameworks.md
  • references/prd_templates.md
  • scripts/customer_interview_analyzer.py
  • scripts/rice_prioritizer.py

Open the folder on GitHubat commit 4a698e8

Compare with similar skills

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Questions about Product Manager Toolkit

What does Product Manager Toolkit do?

Product manager toolkit covering RICE prioritization, customer interview analysis, PRDs, and discovery frameworks. Product Manager Toolkit is an agent skill from borghei/Claude-Skills. Product manager toolkit covering RICE prioritization, customer interview analysis, PRDs, and discovery frameworks.

When should I use Product Manager Toolkit?

Product Manager Toolkit fits situations like: feature prioritization; user research synthesis; requirement documentation; product strategy.

How do I install Product Manager Toolkit in Claude Code?

Run `npx skills add borghei/Claude-Skills --skill product-manager-toolkit -a claude-code`. Or copy the skill folder (product-team/product-manager-toolkit in borghei/Claude-Skills) into .claude/skills/product-manager-toolkit in your project. Claude Code loads it when a task matches its description.

How do I install Product Manager Toolkit in Codex?

Run `npx skills add borghei/Claude-Skills --skill product-manager-toolkit -a codex`. Or copy the skill folder (product-team/product-manager-toolkit in borghei/Claude-Skills) into .agents/skills/product-manager-toolkit in your project. Codex loads it when a task matches its description.

Can I use Product Manager Toolkit in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add borghei/Claude-Skills --skill product-manager-toolkit -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/product-manager-toolkit, .gemini/skills/product-manager-toolkit, .github/skills/product-manager-toolkit and .opencode/skills/product-manager-toolkit in your project.

What does Product Manager Toolkit need to run?

Going by SKILL.md and its folder, Product Manager Toolkit needs Python for the scripts in its folder and the command-line tools its instructions call (python and just). Our summary lists: Python 3.

Does Product Manager Toolkit access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Product Manager Toolkit safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Product Manager Toolkit use?

Product Manager Toolkit is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Product Manager Toolkit use?

About 5.8k tokens (SKILL.md is roughly 23k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 6k tokens, read only when the agent opens those files.

What are the alternatives to Product Manager Toolkit?

Skills that share tags, products or a category with Product Manager Toolkit: Product Manager Toolkit (davila7/claude-code-templates, 32k stars), Opportunity Solution Tree (avelikiy/great_cto, 103 stars), Product Manager Toolkit (majiayu000/spellbook, 287 stars) and Jobs To Be Done Analysis (deanpeters/Product-Manager-Skills, 7.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Product Manager Toolkit?

borghei (a GitHub user) maintains it in borghei/Claude-Skills, which has 886 GitHub stars. The repository holds 354 skills in this directory. The repository was last updated on October 7, 2026.

Source: borghei/Claude-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.