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Topic: pm-tools

116 skills in this topic.

four-risks Run Marty Cagan's Four Risks assessment on an issue (value, usability, feasibility, viability). Use when evaluating features before building.
breethomas/bette-think 13
growth-loops Find your growth loop or stay stuck in linear acquisition hell. Identify viral, content, network, and paid loop opportunities using Elena Verna's framework.
breethomas/bette-think 13
issue-audit Understand how a team organizes work in Linear. Helps PMs onboarding to new teams learn conventions, see examples, and know what questions to ask.
breethomas/bette-think 13
lno-prioritize Find out if you're spending time on the wrong things. Categorize backlog by Leverage/Neutral/Overhead and challenge your time allocation.
breethomas/bette-think 13
now-next-later Generate a Now-Next-Later roadmap using Janna Bastow's framework. Communicates sequence and certainty without false dates.
breethomas/bette-think 13
pm-frameworks Expert knowledge of proven product management frameworks for discovery, growth, measurement, planning, and AI-era practices.
breethomas/bette-think 13
pmf-survey Create and analyze a PMF survey using Rahul Vohra's Superhuman framework. The magic 40% benchmark for product-market fit.
breethomas/bette-think 13
prd-writer Full 5-stage PRD framework for complex features. Use for deep PRD work via /spec --deep full-prd. For quick feature specs, use /spec --feature instead.
breethomas/bette-think 13
project-health Deep-dive health check on a single Linear project. Produces assessment with 7 dimensions - On Track / At Risk / Stalled.
breethomas/bette-think 13
prompt-engineering Expert prompt optimization system for building production-ready AI features. Use when users request help improving prompts, want to create system prompts, need prompt review/critique, ask for prompt optimization strategies, want to analyze prompt effectiveness, mention prompt engineering best practices, request prompt templates, or need guidance on structuring AI instructions. Also use when users provide prompts and want suggestions for improvement.
breethomas/bette-think 13
reflect Pattern recognition across your product decisions. Analyzes saved strategy sessions to surface themes, recurring risks, and suggested next steps.
breethomas/bette-think 13
shape-up Shape work using the Shape Up methodology (Ryan Singer, Basecamp). Walk through the 4-step shaping process to create pitches ready for betting. Distinguishes between established product mode (fixed time, variable scope) and new product mode (looser constraints). Use when planning cycle work, writing pitches, or coaching PMs on shaping.
breethomas/bette-think 13
spec Write specifications at the right depth for any project. Progressive disclosure from quick Linear issues to full AI feature specs. Embeds Linear Method philosophy (brevity, clarity, momentum) with context engineering for AI features. Use for any spec work - quick tasks, features, or AI products.
breethomas/bette-think 13
start-evals Start AI evals without overengineering. Create your first 20 test cases in a spreadsheet using PM-Friendly Evals approach.
breethomas/bette-think 13
strategy-session Your product soundboard. Work through product decisions conversationally - Claude gathers context, challenges assumptions, captures decisions, and creates Linear issues.
breethomas/bette-think 13
workspace-calibration Analyze Linear workspace health and usage patterns before jumping into backlog work. Like a pre-flight check for a new PM joining a team or organization.
breethomas/bette-think 13
build-judge Build an LLM-as-Judge evaluator for one specific failure mode. Binary pass/fail only. Use when a failure mode requires interpretation (tone, faithfulness, relevance, completeness) and cannot be checked with code. Do NOT use when the failure can be checked with regex, schema validation, or execution tests. Do NOT use before completing error analysis (/upgrade-evals).
breethomas/bette-think 13
eval-rag Evaluate RAG pipeline retrieval and generation quality separately. Measure Recall@k, Precision@k, MRR, NDCG@k for retrieval. Assess faithfulness and relevance for generation. Use when the AI feature uses retrieval (search, knowledge base, document QA). Do NOT use for non-RAG AI features.
breethomas/bette-think 13
generate-test-data Create diverse synthetic test inputs using dimension-based tuple generation. Use when bootstrapping an eval dataset, when real user data is sparse, or when stress-testing specific failure hypotheses. Do NOT use when you already have 100+ representative real traces (use stratified sampling instead).
breethomas/bette-think 13
upgrade-evals Systematic error analysis on real AI traces. Read traces, judge pass/fail, let failure categories emerge from data, compute failure rates, decide what to fix. Use when you have 50+ test cases or are seeing production failures. Do NOT use when you have fewer than 20 test cases (use /start-evals first).
breethomas/bette-think 13