Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations. Use when evaluating experiment results, checking if a test reached significance, interpreting split test data, or deciding whether to ship a variant.
Perform cohort analysis on user engagement data — retention curves, feature adoption trends, and segment-level insights. Use when analyzing user retention by cohort, studying feature adoption over time, investigating churn patterns, or identifying engagement trends.
Generate SQL queries from natural language descriptions. Supports BigQuery, PostgreSQL, MySQL, and other dialects. Reads database schemas from uploaded diagrams or documentation. Use when writing SQL, building data reports, exploring databases, or translating business questions into queries.
Design multi-agent AI workflows with clear boundaries, handoffs, and monitoring. Use when a complex PM task should run as parallel specialized agents instead of one linear process.
Research a company, industry, or competitor set using web search and seven analytical lenses. Use when you need structured intel that feeds downstream PM skills.
Generate creature acronyms that name PM dysfunctions, blockers, or winning strategies. Use when you want to give your team shared vocabulary for the animals hiding in your product work.
Zero-config goal-to-tasks engine (the Atlas engine). Takes any goal (software, pentest, business, learning), runs adaptive discovery via brainstorming, generates a validated spec, parses into TaskMaster tasks, and hands off to execution. Use when user says "PRD", "product requirements", "I want to build", invokes…
Execute the next TaskMaster task using the implementation plan with CDD verification. Picks the next ready task, matches it to the plan step, implements via a dispatched subagent, verifies subtasks with evidence, marks the task done, and loops until every task is complete. Wraps the TaskMaster next -> in-progress ->…
Phase 3 of the prd-taskmaster pipeline: smart mode selection and user handoff. Detects installed capabilities (superpowers, ralph-loop, task-master-ai, playwright, research providers), recommends ONE execution mode (A/B/C) with reasoned justification, appends the task-execution workflow to CLAUDE.md, surfaces a…
Validates internet access, compares the locally installed pm-skills version against the latest public release, and updates local files with conflict-aware overwrite-or-skip options. Produces an update report listing changed files, skipped files, and new capabilities. Use when you want to bring a local pm-skills…
Maps a customer journey across stages, touchpoints, emotional curve, pain points, and moments of truth into a markdown artifact with an optional mermaid timeline or flowchart. Use when synthesizing existing research into the shape of a customer's experience, end-to-end or for one phase. Without research signal yet…
Estimate market opportunity (TAM, SAM, SOM) using multiple sizing frameworks (top-down, bottom-up, comparable company, analogous market). Triangulates across frameworks, highlights where they converge and diverge as signal, and produces a calibrated range with source-graded confidence labels. Refuses unbounded…
Builds AI-native products using OpenAI's development philosophy and modern AI UX patterns. Use when integrating AI features, designing for model improvements, implementing evals as product specs, or creating AI-first experiences. Based on Kevin Weil (OpenAI CPO) on building for future models, hybrid approaches, and…
Implements Teresa Torres' continuous discovery habits for weekly customer contact, opportunity solution trees, and assumption testing. Use when building discovery processes, conducting user research, validating assumptions, or establishing product trio workflows.
Follows Airbnb's design-led development and Figma's craft quality standards. Use when building user-facing features, making UI/UX decisions, determining when details matter, or applying design system thinking. Guides when to move fast vs when quality creates moats. Based on Brian Chesky staying in every design detail…
Author and maintain RAC (requirements-as-code) Markdown artifacts — requirements, decisions, roadmaps, prompts, designs — using the decided CLI. Use when asked to create, read, validate, update, or link AsDecided (RAC) artifacts in a project's decisions/ directory.
Capture a NEW decision or requirement from a conversation (an interview) into ONE valid RAC (requirements-as-code) artifact — you interview and propose, the human ratifies, decided validate closes, and promotion into the trusted corpus is by pull request reviewed by someone other than the author. Use when a user wants…
Reformat ONE existing document (a decision, requirement, design, roadmap, or prompt) into ONE valid RAC (requirements-as-code) artifact, with a mandatory human-review step before any file is written and decided validate as the deterministic close. Use when a user wants to add or import a single existing decision or…
Writes, validates, and converts ProductSpec files (.product-spec.md), the Markdown format for recording product intent before implementation. Use when authoring a new Product Spec, converting an existing PRD or feature doc into one, validating spec files locally or in CI, or recording how a spec's intent changed over…
Use when implementing, reviewing, planning, or changing work governed by a Product Spec. Treat .product-spec.md files as the product contract for the work.
Activate on "synthesize research", "analyze interviews", "research findings", "interview synthesis".
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At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: