product-playbook-for-agentic-coding

Safely delete a git branch from local and remote after verification

Move uncommitted changes to a new branch without committing

Create a new branch and worktree together for isolated development

Analyze git changes and create well-structured conventional commits

Create a pull request with clear title and description using GitHub CLI

Use this agent to validate consistency across interconnected documents. Checks that linked concepts match, definitions align, and cross-references are accurate. <example>\nContext: User has a set of interconnected documentation.\nuser: "Can you check if my foundations docs are consistent with each other?"\nassistant: "I'll use the cross-reference-validator-agent to check all cross-references and verify consistency."\n<commentary>\nSince the user has interconnected documents and wants consistency checking, use the cross-reference-validator-agent.\n</commentary>\n</example>\n<example>\nContext: User just updated one document and wants to ensure it didn't break references.\nuser: "I updated the personas doc - did that break anything?"\nassistant: "Let me launch the cross-reference-validator-agent to check if any documents reference the changed content."\n<commentary>\nAfter document updates, use cross-reference-validator-agent to catch cascading inconsistencies.\n</commentary>\n</example>

Use this agent for systematic debugging with a verification-first approach. This agent helps diagnose issues by first reproducing the problem, then forming and testing hypotheses. <example>\nContext: User encounters an error or unexpected behavior.\nuser: "The API is returning 500 errors intermittently"\nassistant: "I'll use the debugging-agent to systematically investigate this issue"\n<commentary>\nIntermittent errors need systematic debugging to isolate the root cause.\n</commentary>\n</example>\n<example>\nContext: Something stopped working after a change.\nuser: "The notification feature broke after the latest deploy"\nassistant: "Let me launch the debugging-agent to trace what changed and identify the cause"\n<commentary>\nRegression after deploy benefits from systematic debugging approach.\n</commentary>\n</example>

Use this agent for the Delivery phase when executing tasks from a tasks document. This agent works through tasks systematically with quality checks, progress tracking, and incremental commits. <example>\nContext: Tasks are defined, ready to execute.\nuser: "Let's start working on the notification feature tasks"\nassistant: "I'll use the delivery-agent to work through the tasks systematically"\n<commentary>\nWith tasks defined, use the delivery-agent to execute them with proper quality gates.\n</commentary>\n</example>\n<example>\nContext: User wants to continue work on existing tasks.\nuser: "What's the next task to work on?"\nassistant: "Let me launch the delivery-agent to identify and execute the next pending task"\n<commentary>\nThe delivery-agent manages task execution workflow including identifying what's next.\n</commentary>\n</example>

Use this agent to extract and organize insights from source materials (interview notes, research docs, meeting notes) with proper citations. Produces a structured insights document with source links. <example>\nContext: User has interview notes and wants to extract product insights.\nuser: "I have interview notes in interviews/ - can you extract the key product insights?"\nassistant: "I'll use the insight-extractor-agent to systematically extract and categorize insights from your interview notes."\n<commentary>\nSince the user has source materials and wants organized insights, use the insight-extractor-agent to search, extract, and cite.\n</commentary>\n</example>\n<example>\nContext: User wants to find all mentions of a topic across documents.\nuser: "What have stakeholders said about pricing across all our meeting notes?"\nassistant: "Let me launch the insight-extractor-agent to find and organize all pricing-related insights from your meeting notes."\n<commentary>\nThe user needs targeted extraction across multiple documents, which is the core use case for insight-extractor-agent.\n</commentary>\n</example>

Autonomous Chat Insights Pipeline agent that analyzes chat sessions, brainstorms product improvements, RICE-scores ideas, and generates a morning report. This is a reference for interactive ad-hoc runs — the automated pipeline uses runner.ts directly.

Quality check for AI-generated output. Detects and removes hedge stacking, throat-clearing, motivational padding, restating, and unnecessary transitions. Use before presenting any substantive artifact to the user. Don't use for code comments or commit messages (those have their own conventions).

Patterns for finding and using project documentation at runtime. Use this skill when you need to search for existing documentation, solutions, patterns, or learnings in a codebase before starting work. Don't use when you already know the file path, or when searching for code (use Grep/Glob directly).

Patterns for autonomous project execution with minimal human intervention. Use this skill when executing well-defined tasks autonomously, including validation strategies, stop conditions, and quality gates. Don't use when doing a single task interactively with the user, or when the project lacks a tasks document.

Patterns for analyzing chat sessions to extract product insights. Covers session sampling, evidence validation, idea deduplication, PII handling, data quality assessment, and prompt injection defense. Don't use for capturing a single session's learnings (use learning-capture instead), or for improving the playbook itself (use /playbook:improve-playbook instead).

Multi-trigger, dual-target learning capture patterns. Use this skill to understand how to capture learnings at different points in the development workflow and route them to appropriate targets (codebase docs or plugin improvements). Don't use when saving session state for resumption (use session-checkpoint instead), or for analyzing chat sessions at scale (use chat-insights instead).