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Comprehensive codebase analysis for building mental model of project structure, dependencies, and implementation context. Use when needing to: (1) Understand project architecture before review or documentation, (2) Find dependencies and shared modules, (3) Locate implementation markers (AICODE-*), (4) Prepare context for review, memory generation, or agent creation. Triggers on: analyze code, load code context, scan codebase, understand project structure.

This skill automates the capture of high-quality screenshots for MicroSim visualizations using Chrome headless mode. Use this skill when working with MicroSims that need preview images for social media sharing, documentation, or quality assessment. The skill handles JavaScript-heavy visualizations that require proper rendering time and external CDN resources.

Python-based threat modeling using pytm library for programmatic STRIDE analysis, data flow diagram generation, and automated security threat identification. Use when: (1) Creating threat models programmatically using Python code, (2) Generating data flow diagrams (DFDs) with automatic STRIDE threat identification, (3) Integrating threat modeling into CI/CD pipelines and shift-left security practices, (4) Analyzing system architecture for security threats across trust boundaries, (5) Producing threat reports with STRIDE categories and mitigation recommendations, (6) Maintaining threat models as code for version control and automation.

Up-to-date library documentation retrieval using Context7 MCP tools. Process THINK → RESOLVE → FETCH → APPLY. Use when fetching library docs, resolving package names to IDs, getting implementation guides, exploring API references. Provides package resolution strategy, trust score evaluation, token scaling (3K-20K), topic selection patterns.

The model must apply when tasks involve .gitlab-ci.yml configuration, GitLab Flavored Markdown (GLFM) syntax, gitlab-ci-local testing, CI/CD pipeline optimization, GitLab CI Steps composition, Docker-in-Docker workflows, or GitLab documentation creation. Triggers include modifying pipelines, writing GitLab README/Wiki content, debugging CI jobs locally, implementing caching strategies, or configuring release workflows.

Universal markdown content quality standards for active voice, heading hierarchy, accessibility compliance (alt text, WCAG AA contrast, screen reader support), and professional formatting. Essential for all markdown content creation across docs/, Hugo sites, plans/, and repository files. Auto-loads when creating or editing markdown content.

Markdown documentation standards for LLM-optimized architecture including section numbering rules for Pandoc PDF generation. Use when writing markdown documentation, creating skills, or authoring content that may be converted to PDF.

Straightforward text extraction from document files (text-based PDF only for now, no OCR or docx). Use when you just need to read/extract text from binary documents.

Create ASCII architecture diagrams for ADRs using graph-easy. Use when writing ADRs, adding diagrams to existing ADRs, or when user mentions "ADR diagram", "architecture diagram", "ASCII diagram". Zero external dependencies for rendering - pure text output embeds directly in markdown.

Terraform infrastructure-as-code best practices for scalable and maintainable cloud infrastructure. Use when writing Terraform modules, managing infrastructure state, or implementing infrastructure automation at scale.

CRITICAL for ALL Elixir/Phoenix/Ash development work. Invoke when working with Elixir code, Ecto schemas, Ash resources, Phoenix applications, or databases in Elixir projects. Provides MCP tools for live code evaluation (via IEx), instant module navigation, direct SQL execution, schema introspection, and documentation access. Tidewave provides superior alternatives to bash/grep/read for Elixir tasks.

コードベース分析からドメイン理解ドキュメントを生成。 エンティティ、ビジネスロジック、ドメイン用語、概念関係を抽出。 トリガー: domain understanding, glossary, entities, business logic, domain model, ER diagram, use cases.

LLM-powered documentation generation for narrative architecture docs, tutorials, and developer guides. Uses AI consultation to create contextual, human-readable documentation from code analysis and spec data.

Repair malformed markdown code fence closings. Use when markdown files have closing fences with language identifiers (```text instead of ```) or when generating markdown with code blocks to ensure proper fence closure.

Comprehensive assistance with latex-wikibook

Data modeling with Entity-Relationship Diagrams (ERDs), data dictionaries, and conceptual/logical/physical models. Documents data structures, relationships, and attributes.

Automated technical debt identification, tracking, and prioritization system

Use when writing Jest tests - covers testing patterns for interpreters, parsers, and async code

Multi-persona stakeholder simulation for solo requirements work. Generates diverse perspectives from simulated End User, Technical, Business, Compliance, and Operations stakeholders when real stakeholders are unavailable.

This skill should be used when performing systematic scans of research paper text to identify and fix typos, grammar errors, inappropriate words, and awkward expressions. Use for thorough proofreading and error correction in academic writing for computer science conferences.