Use this skill for structured competitor tracking — feature comparison matrices, pricing intelligence, messaging changes, hiring signals, and strategic implications. Repeatable, cadence-friendly monitoring with delta detection.

Use this for creating, auditing, or applying design systems — including DESIGN.md generation, design token definition, component specification, brand reference calibration, and systematic design language enforcement.

Four principles applied at the start of any non-trivial coding task. They reduce the most common LLM coding failures: wrong assumptions taken silently, overcomplicated code, drive-by edits, and ambiguous goals that need re-prompting.

Use this skill when preparing investor-facing materials — pitch decks, one-pagers, investor memos, financial models, or accelerator applications. Packages the business story for external stakeholders with consistent, defensible numbers.

Use this for securing AI-agent workspaces themselves, including instruction files, MCP setups, memory, external content, prompt-injection surfaces, and least-privilege configuration.

Use this for instrumentation strategy, event design, funnels, experimentation, north-star metrics, KPI definitions, attribution logic, and making sure product decisions can be measured reliably.

Use this for API contract design across REST, GraphQL, and event-driven interfaces, including resource modeling, versioning, errors, pagination, idempotency, and integration-facing correctness.

Find deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.

Use this skill for browser-level testing, UI verification, automated user flow testing, visual regression, and scraping JavaScript-rendered pages. Covers QA smoke tests through full user journey automation.

Use this to analyze a repository and recommend Claude Code setup improvements, including CLAUDE.md, MCP servers, hooks, skills, commands, permissions, and onboarding configuration.

Use this to audit, maintain, and improve CLAUDE.md and Claude-specific rule files, including instruction quality, session learning capture, overlap control, and cross-tool consistency with BosskuAI memory.

Use this for safe code modernization, structural cleanup, legacy refactors, and revamps that should still respect the current codebase structure and avoid unnecessary churn.

Use this for tracing execution paths, mapping call chains and module boundaries, identifying side effects, and summarizing how code actually behaves at runtime. Use bosskuai-project-understanding first when the project purpose and stack are still unknown.

Use this for implementation quality, maintainability, readability, testing expectations, error handling, naming, and pragmatic best-practice checks that still fit the current repo conventions.

Use this when a task risks hitting model context, token limits, or tight usage/quota mid-process. Stop cleanly, write progress into ai-assistant/memory/active-continuation.md, pair with bosskuai-ai-model-selection to recommend which model should complete the remaining work, and tell the user explicitly to open a fresh chat/session with that model. Also contains token-budget guidance for keeping agent sessions efficient.

Use this after meaningful tasks, reviews, incidents, or repeated observations to decide the smallest durable artifact that should capture the lesson and keep future sessions from relearning it.

Use this for cloud/server/API/model cost control, token budgets, queue sizing, storage costs, vendor spend, and unit economics.

Use this when the current model is stuck, low-confidence, missing a capability, or repeating failed attempts. It defines how to bring in another model, tool surface, or session for scoped assistance across Claude, Codex, and Cursor without losing ownership of the task.

Use this skill for user interview planning, customer discovery, survey design, interview transcript analysis, and persona building from primary research. Gathers and structures the evidence that product strategy acts on.

Use this for support SOPs, onboarding, ticket triage, customer success playbooks, retention, churn reduction, and feedback loops.