Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.
日本語の概要は準備中です。原文の説明を表示しています。
Comprehensive toolkit for product managers including RICE prioritization, customer interview analysis, PRD templates, discovery frameworks, and go-to-market strategies. Use for feature prioritization, user research synthesis, requirement documentation, and product strategy development.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Essential tools and frameworks for modern product management, from discovery to delivery.
python scripts/rice_prioritizer.py sample # Create sample CSV
python scripts/rice_prioritizer.py sample_features.csv --capacity 15
python scripts/customer_interview_analyzer.py interview_transcript.txt
references/prd_templates.mdGather Feature Requests
Score with RICE
# Create CSV with: name,reach,impact,confidence,effort
python scripts/rice_prioritizer.py features.csv
Analyze Portfolio
Generate Roadmap
Conduct Interviews
Analyze Insights
python scripts/customer_interview_analyzer.py transcript.txt
Extracts:
Synthesize Findings
Validate Solutions
Choose Template
Structure Content
Collaborate
Advanced RICE framework implementation with portfolio analysis.
Features:
Usage Examples:
# Basic prioritization
python scripts/rice_prioritizer.py features.csv
# With custom team capacity (person-months per quarter)
python scripts/rice_prioritizer.py features.csv --capacity 20
# Output as JSON for integration
python scripts/rice_prioritizer.py features.csv --output json
NLP-based interview analysis for extracting actionable insights.
Capabilities:
Usage Examples:
# Analyze single interview
python scripts/customer_interview_analyzer.py interview.txt
# Output as JSON for aggregation
python scripts/customer_interview_analyzer.py interview.txt json
Multiple PRD formats for different contexts:
Standard PRD Template
One-Page PRD
Agile Epic Template
Feature Brief
Score = (Reach × Impact × Confidence) / Effort
Reach: # of users/quarter
Impact:
- Massive = 3x
- High = 2x
- Medium = 1x
- Low = 0.5x
- Minimal = 0.25x
Confidence:
- High = 100%
- Medium = 80%
- Low = 50%
Effort: Person-months
Low Effort High Effort
High QUICK WINS BIG BETS
Value [Prioritize] [Strategic]
Low FILL-INS TIME SINKS
Value [Maybe] [Avoid]
1. Context Questions (5 min)
- Role and responsibilities
- Current workflow
- Tools used
2. Problem Exploration (15 min)
- Pain points
- Frequency and impact
- Current workarounds
3. Solution Validation (10 min)
- Reaction to concepts
- Value perception
- Willingness to pay
4. Wrap-up (5 min)
- Other thoughts
- Referrals
- Follow-up permission
We believe that [building this feature]
For [these users]
Will [achieve this outcome]
We'll know we're right when [metric]
Outcome
├── Opportunity 1
│ ├── Solution A
│ └── Solution B
└── Opportunity 2
├── Solution C
└── Solution D
Acquisition → Activation → Retention → Revenue → Referral
Key Metrics:
- Conversion rate at each step
- Drop-off points
- Time between steps
- Cohort variations
This toolkit integrates with:
# Prioritization
python scripts/rice_prioritizer.py features.csv --capacity 15
# Interview Analysis
python scripts/customer_interview_analyzer.py interview.txt
# Create sample data
python scripts/rice_prioritizer.py sample
# JSON outputs for integration
python scripts/rice_prioritizer.py features.csv --output json
python scripts/customer_interview_analyzer.py interview.txt json
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.
日本語の概要は準備中です。原文の説明を表示しています。
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
日本語の概要は準備中です。原文の説明を表示しています。
Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
日本語の概要は準備中です。原文の説明を表示しています。
This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
日本語の概要は準備中です。原文の説明を表示しています。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
日本語の概要は準備中です。原文の説明を表示しています。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
日本語の概要は準備中です。原文の説明を表示しています。