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Expands a raw idea into a structured, research-backed spec in .productize/tasks/<slug>/_idea.md through interactive brainstorming, web research, business analysis, and multi-advisor debate. Use when the user has a feature idea and wants to explore and structure it before creating a PRD. Do not use for PRD creation, technical specifications, task breakdown, or code implementation.

Minimal starter skill for a Productize extension skill pack.

Creates a Product Requirements Document through interactive brainstorming with parallel codebase and web research. Use when starting a new feature or product, building a PRD, or brainstorming requirements. Do not use for technical specifications, task breakdowns, or code implementation.

Decomposes PRDs and TechSpecs into detailed, independently implementable task files with enrichment from codebase exploration. Use when a PRD or TechSpec exists and needs to be broken down into executable tasks, or when task files need enrichment with implementation context. Do not use for PRD creation, TechSpec generation, or direct task execution.

Creates a Technical Specification by translating PRD business requirements into implementation designs through interactive technical clarification. Use when a PRD exists and needs a technical plan, or when technical architecture decisions need documentation. Do not use for PRD creation, task breakdown, or direct code implementation.

Executes one PRD task end-to-end using a provided task file, PRD directory, tracking file paths, and auto-commit mode. Use when a prompt includes a task specification that must be implemented, validated, and reflected in task tracking files. Do not use for PR review batches, generic coding tasks without a PRD task file, or standalone verification-only work.

Enforces fresh verification evidence before any completion, fix, or passing claim, and before commits or PR creation. Use when an agent is about to report success, hand off work, or commit code. Do not use for early planning, brainstorming, or tasks that have not yet reached a concrete verification step.

Executes provider-agnostic PR review remediation using existing review round files under .productize/tasks/<name>/reviews-NNN/. Use when resolving batched review issues, updating issue markdown files, implementing fixes, and verifying the result. Do not use for PRD task execution, review export/fetch, or generic coding tasks without review issue files.

Surface and categorize assumptions underlying product strategy. Use early in development to document what you're betting on.

Prioritize assumptions by risk and importance. Use after assumption mapping to decide what to test first.

Sync Jira issues with local tasks. Use during daily planning or when user asks about Jira issues.

Design backend architecture for implementation with API contracts, data model changes, service boundaries, security, and observability.

Turn ideas into designs through collaborative dialogue. Use before any creative work - features, components, or modifications.

Generate rigorous stakeholder challenge questions for proposal or meeting preparation.

Analyze competitors using Porter's four-component model - drivers, assumptions, strategy, capabilities.

Identify the pivotal obstacle between current state and desired future. Use when developing strategy.

Guides design of LLM-as-judge prompts for subjective evaluation criteria. Use when deterministic checks are insufficient and you need a judge prompt for quality dimensions like helpfulness, faithfulness, clarity, or tone.

Guides building or improving interfaces for human review of eval traces. Use when humans need to inspect failures, label outputs, compare model behavior, or audit evaluator decisions at scale.

Guides systematic analysis of eval failures using traces. Use when a suite is failing, model outputs seem inconsistent, evaluator behavior is suspect, or you need to classify failures before changing prompts, metrics, or datasets.

Guides evaluation of RAG pipeline retrieval and generation quality. Use when evaluating a retrieval-augmented generation system, measuring retrieval quality, assessing generation faithfulness or relevance, generating synthetic QA pairs for retrieval testing, or optimizing chunking strategies.

Guides creation of synthetic eval cases that expand coverage without drifting away from real usage. Use when the current eval set is too small, too repetitive, or missing edge cases, and you need more diverse prompts, distractors, or structured scenarios.

Drafts a reviewable judge-template patch from evaluator validation disagreements.

Guides validation of evaluators, especially LLM judges, against labeled examples. Use when evaluator quality is uncertain, judge scores seem inconsistent, or you need to check whether the evaluator is biased, noisy, or misaligned.

Guides model and provider comparison on a shared eval suite. Use when comparing providers, selecting a default model, investigating model-specific regressions, or turning one suite into a reusable benchmark.