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.

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.

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.

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

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.

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.

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.

Delegate complex, multi-step codebase exploration to local Ollama models. Best for analysis, review, and understanding tasks that require reasoning across multiple files.

Review current changes against project guidelines before PR

Test local Jekyll build and visualize pages using Playwright MCP. Starts the development server, navigates through key pages, captures screenshots, and validates rendering. Use when testing local changes before deployment.

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Autonomous multi-step work with dual-reviewer supervision (1A2A workflow) and compound engineering principles

Audit all outdated dependencies with detailed research on changelogs, breaking changes, bug fixes, and deprecations. Creates a temporary plan without updating anything. Use when you want to review what changed in your dependencies before upgrading.

This skill should be used when users need to write, review, or debug Stata code for data cleaning and analysis. Use this skill for tasks involving data import, variable management, data documentation, merging/appending datasets, creating analysis variables, and following IPA/DIME Analytics coding standards. This skill should be invoked when working with .do files, .dta files, or any Stata-related data processing tasks.

E2E development: investigate → dig → decompose → implement → test → review → PR

Create a GitHub pull request using gh CLI with proper formatting

Capability skill — procedure for turning a known-open finding in docs/BUGS.md into a landed fix. No persona. Any agent may invoke this skill; the procedure itself enforces greenfield discipline — falsifying test first, blast-radius walk, minimal correct fix (not quick-hack), reviewer floor, spec updates. Previously architect-only to guard against quick hacks; the safeguards baked into this procedure plus GOVERNANCE §20 reviewer floor plus the skill-creator workflow make the restriction redundant as of round 29.

Validate and triage review findings of an implementation plan. Classify each finding using the unified taxonomy (FIX, FIX_UNCLEAR, ASK_USER, REJECT_FALSE_POSITIVE, REJECT_WONT_FIX, REJECT_ALREADY_FIXED). Reads the actual codebase to verify each finding against reality.

Python best practices for pentest scripts — requests sessions, error handling, output, CLI args

Write high-quality, maintainable tests for Java projects using modern best practices.

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Review code changes for async-signal-safety violations in KSCrash crash handlers, signal handlers, and monitor code. Verifies suspect system calls by reading the actual implementation in Apple's open-source repos on github.com/apple-oss-distributions rather than guessing. Use when the user asks to review a diff/branch/PR/file for signal safety, or before landing changes that touch signal handlers, Mach exception handlers, or anything reachable from `Sources/KSCrashRecording`, `Sources/KSCrashRecordingCore`, `Sources/KSCrashBootTimeMonitor`, or `Sources/KSCrashDiscSpaceMonitor`.

Guide evaluation of healthcare AI systems with domain-specific safety criteria, clinical accuracy rubrics, and score interpretation. Use when building or reviewing health/medical AI evaluations.

plan-design-review

Create a draft pull request targeting develop. Auto-generates PR body from design files, GitHub issues, and/or commit history. Use when opening a PR.

Merge PR с pre/post проверками, sync main и cleanup. Используй при merge PR вместо ручного gh pr merge.

Full R017 verification (5+3 rounds) before commit

Monitor and report on Ralph Wiggum loop progress. Provides real-time status, iteration summaries, and progress tracking via Archon state. Use to check on running or completed loops, view iteration history, and diagnose issues.

Generates commit messages and creates commits. Use when writing commit messages, committing changes, or reviewing staged changes.

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Perform a Cynical Review and produce a findings report. Use when the user requests a critical review of something