Expert system for identifying deprecated patterns, suggesting refactoring to modern standards (Python 3.12+, ES2024+), checking test coverage, and leveraging AI-powered tools. Proactively applied when users request refactoring, updates, or analysis of legacy codebases.

Provides expert legal guidance on technology law, contracts, compliance, and intellectual property matters. Specializes in data privacy regulations, software licensing, terms of service, and risk miti

Legal document analysis, contract review, and compliance guidance for business and personal legal matters

Legal and compliance expertise for corporate governance, contract analysis, regulatory compliance (SOX, GDPR, HIPAA), risk assessment, intellectual property, and litigation management. Use when reviewing contracts, ensuring compliance, or managing legal risk.

Legal Sanity Review Workflow — mandatory pre-gate in the cross-review cycle

Capture and review lessons learned from coding sessions. Use to record insights, read past lessons, and improve over time.

CREATE/REPLAN Epics from scope (3-7 Epics). Batch Preview + Auto-extraction. Decompose-First Pattern. Auto-discovers team ID.

Orchestrates Story tasks. Prioritizes To Review -> To Rework -> Todo, delegates to ln-401/ln-402/ln-403/ln-404, hands Story quality to ln-500. Metadata-only loading up front.

Executes implementation tasks (Todo -> In Progress -> To Review). Follows KISS/YAGNI, guides, quality checks. Not for test tasks.

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Provides guidelines for effective utilization of specific libraries within the project, including axios, js-yaml, mime-types, node-gyp, uuid, and zod.

生活管理系統。觸發：早安, 晚安, 今天, 規劃, 回顧, 健康, 投資, 股票, 追蹤, watchlist, morning, evening, daily, plan, review

This skill analyzes code for design quality improvements across 8 dimensions: Naming, Object Calisthenics, Coupling & Cohesion, Immutability, Domain Integrity, Type System, Simplicity, and Performance. Ensures rigorous, evidence-based analysis by: (1) Understanding code flow first via implementation-analysis protocol, (2) Systematically evaluating each dimension with specific criteria, (3) Providing actionable findings with file:line references. Triggers when users request: code analysis, design review, refactoring opportunities, code quality assessment, architecture evaluation.

Enforce file line count limits (200 recommended, 300 max) for CODE IMPLEMENTATION files only. Use this when reviewing code, creating files, or when files exceed line limits and need modularization.

Skill for Linear project management tasks including creating projects, creating issues, and reviewing project structures. Use when working with Linear project setup, issue creation, project planning, or any Linear organizational tasks.

This skill should be used when organizing daily work with Linear, starting the work day, reviewing priorities, triaging issues, or managing status updates. Use it for daily standup preparation, automated issue triage, workload planning, and status reporting. Integrates with Linear GraphQL API for seamless issue management.

Comprehensive LinkedIn personal branding analysis, profile optimization, and visibility improvement skill using Claude for Chrome browser tools. Use when users request LinkedIn profile analysis, personal branding audit, profile optimization recommendations, LinkedIn visibility improvement, headline optimization, About section review, content strategy guidance, engagement analysis, or Social Selling Index improvement. Works with Claude for Chrome to analyze profile photos, banners, headlines, About sections, experience, skills, recommendations, featured content, activity/posts, and network engagement directly from the user's browser.

Craft a compelling LinkedIn profile through adversarial review. Optimizes headline, About section, experience descriptions, and skills for recruiter search and engagement.

Search your LinkedIn posts/shares by topic, find connections by title or company, and get LinkedIn statistics. Use when analyzing your LinkedIn data, finding specific connections, or reviewing past posts and shares.

Create LinkedIn posts for lead generation and thought leadership. Use when projecting ideas to LinkedIn, creating post series, or reviewing LinkedIn content. Includes LinkedIn-specific voice (punchier, more personal), format rules, and weekly rhythm guidance.

Check code for style and quality issues. Use when validating code before commits.

Expert assistant for analyzing and fixing linting and formatting issues in the KR92 Bible Voice project using Biome and TypeScript. Use when fixing lint errors, resolving TypeScript issues, applying code formatting, or reviewing code quality.

Detect and fix violations of project instructions defined in .claude/rules/. Use when checking code compliance, reviewing changes, or when the user asks about instruction violations.

Unified linting and auto-fix for Python (Ruff) and TypeScript (ESLint) in monorepo. Use when fixing lint errors, running pre-commit checks, or diagnosing persistent code quality issues. Orchestrates auto-fix first, then root-cause analysis.

Locally render Liquid templates using a virtual lab (Node.js engine).

List phase assumptions and dependencies

Build systematic literature databases for sociology research using OpenAlex API. Guides you through search, screening, snowballing, annotation, and synthesis with structured user interaction at each stage.

Build systematic literature databases for sociology research using OpenAlex API. Guides you through search, screening, snowballing, annotation, and synthesis with structured user interaction at each stage.

Draft publication-ready Theory sections for sociology research. Guides structure, paragraph functions, sentence craft, and calibration based on analysis of 80 Social Problems/Social Forces articles.

CRITICAL: ALWAYS activate this skill BEFORE making ANY changes to .nw files. Use proactively when: (1) creating, editing, reviewing, or improving any .nw file, (2) planning to add/modify functionality in files with .nw extension, (3) user asks about literate quality, (4) user mentions noweb, literate programming, tangling, or weaving, (5) working in directories containing .nw files, (6) creating new modules/files that will be .nw format. Trigger phrases: 'create module', 'add feature', 'update', 'modify', 'fix' + any .nw file. Never edit .nw files directly without first activating this skill to ensure literate programming principles are applied. (project, gitignored)

Method×Setting matrices and systematic gap identification

Identify research gaps from systematic literature reviews. Use when: (1) Completing literature reviews, (2) Justifying new studies, (3) Grant proposal development, (4) Dissertation planning, (5) Identifying future research directions.

Run a senior-level Java/Spring backend live-coding interview practice, guiding the user with Socratic prompts, strict code review (readability, edge cases, data structures), and staged hints without giving direct answers.

Build and review production-grade web and mobile frontends using LiveKit with Next.js. Covers real-time video/audio/data communication, WebRTC connections, track management, and best practices for LiveKit React components.

Guidelines for developing modular, reusable Livewire components.

Detects common LLM coding agent artifacts in codebases. Identifies test quality issues, dead code, over-abstraction, and verbose LLM style patterns. Use when cleaning up AI-generated code or reviewing for agent-introduced cruft.

Write effective LLM prompts, commands, and agent instructions. Goal-oriented over step-prescriptive. Role + Objective + Latitude pattern. Use when writing prompts, designing agents, building Claude Code commands, or reviewing LLM instructions. Keywords: prompt engineering, agent design, command writing.

Orchestrate multiple LLMs as a council, generating collective intelligence through peer review and chairman synthesis

Expert in building production-grade streaming interfaces for LLM responses that feel instant and responsive.

Validates Stories/Tasks with GO/NO-GO verdict, Readiness Score (1-10), Penalty Points, and Anti-Hallucination verification. Auto-fixes to reach 0 points, delegates to ln-002 for docs. Use when reviewing Stories before execution or when user requests validation.

L3 Worker. Reviews task implementation for quality, code standards, test coverage. Creates [BUG] tasks for side-effect issues found outside task scope. Sets task Done or To Rework. Usually invoked by ln-400 with isolated context, can also review a specific task on user request.

Fixes tasks in To Rework and returns them to To Review. Applies reviewer feedback only for the selected task.

Runs a single Story final test task (label "tests") through implementation/execution to To Review.

Story-level quality orchestrator with 4-level Gate (PASS/CONCERNS/FAIL/WAIVED) and Quality Score. Pass 1: code quality -> regression -> manual testing. Pass 2: verify tests/coverage -> calculate NFR scores -> mark Story Done. Use when user requests quality gate for Story or when ln-400 delegates quality check.

Worker that checks DRY/KISS/YAGNI/architecture compliance with quantitative Code Quality Score. Validates architectural decisions via MCP Ref: (1) Optimality - is chosen approach the best? (2) Compliance - does it follow best practices? (3) Performance - algorithms, configs, bottlenecks. Reports issues with SEC-, PERF-, MNT-, ARCH-, BP-, OPT- prefixes.

Audit project documentation quality across 8 categories (Hierarchy, SSOT, Compactness, Requirements, Actuality, Legacy, Stack Adaptation, Semantic Content). Delegates to ln-601 for deep semantic verification of project documents. Use when documentation needs quality review, after major doc updates, or as part of ln-100-documents-pipeline. Outputs Compliance Score X/10 per category + Findings + Recommended Actions.

Audit code comments and docstrings quality across 6 categories (WHY-not-WHAT, Density, Forbidden Content, Docstrings, Actuality, Legacy). Use when code needs comment review, after major refactoring, or as part of ln-100-documents-pipeline. Outputs Compliance Score X/10 per category + Findings + Recommended Actions.

Risk-Based Value audit worker (L3). Calculates Usefulness Score = Impact (1-5) × Probability (1-5) for each test. Returns KEEP/REVIEW/REMOVE decisions based on thresholds (≥15 KEEP, 10-14 REVIEW, <10 REMOVE).

Detects unsafe training load spikes (>20-30% week-over-week) and emits safety flags. Use in nightly background jobs or when reviewing weekly training volume with conservative adjustment recommendations.