Claude Skills by lidge-jun

PowerPoint PPTX create, read, edit, review. Triggers: PowerPoint, PPTX, presentation, slides, deck.

3D Morph PPT — extends morph-ppt with GLB model insertion, cinematographic camera, model-content layout, and enriched visual design system.

Generate Morph-animated PPTs with officecli

Use this skill when the user wants to create a pitch deck, investor presentation, product launch deck, sales presentation, or business proposal in PowerPoint format. Trigger on: 'pitch deck', 'investor deck', 'Series A deck', 'product launch presentation', 'sales deck', 'fundraising deck', 'startup pitch', 'business proposal slides', 'seed pitch', 'enterprise sales deck'. Output is always a single .pptx file. This skill does NOT use morph transitions -- for morph-animated presentations, use t...

Use this skill any time a .pptx file is involved in any way — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading, parsing, or extracting text from any .pptx file (even if the extracted content will be used elsewhere, like in an email or summary); editing, modifying, or updating existing presentations; combining or splitting slide files; working with templates, layouts, speaker notes, or comments. Trigger whenever the user mentions \"deck,\" ...

Production scheduling, job sequencing, line balancing, changeover optimization, and bottleneck resolution in discrete and batch manufacturing. Includes TOC/drum-buffer-rope, SMED, OEE analysis, disruption response frameworks, and ERP/MES interaction patterns.

Example project-specific skill template based on a real production application — use as a starting point for your own project skills.

Analyze and optimize prompts for AI coding agents. Decompose tasks into components (skills/commands/agents), detect missing context, and produce ready-to-paste improved prompts. Advisory only — outputs prompts, not code. Triggers: "optimize prompt", "improve my prompt", "rewrite this prompt", "how to write a prompt for", "help me prompt"

Provides guidance for property-based testing across multiple languages and smart contracts. Use when writing tests, reviewing code with serialization/validation/parsing patterns, designing features, or when property-based testing would provide stronger coverage than example-based tests.

Pythonic idioms, PEP 8 standards, type hints, and best practices for building robust, efficient, and maintainable Python applications.

Python testing strategies using pytest, TDD methodology, fixtures, mocking, parametrization, and coverage requirements.

PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.

Quality control, non-conformance investigation, root cause analysis, corrective action, and supplier quality management in regulated manufacturing. Includes NCR lifecycle, CAPA systems, SPC interpretation, and audit methodology across FDA, IATF 16949, and AS9100 environments.

RFC-driven multi-agent DAG execution pattern with quality gates, merge queues, and work unit orchestration.

React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation

Decision framework for choosing between regex and LLM when parsing structured text — start with regex, add LLM only for low-confidence edge cases.

Deploy applications to Render by analyzing codebases, generating render.yaml Blueprints, and providing Dashboard deeplinks. Use when the user wants to deploy, host, publish, or set up their application on Render's cloud platform.

Use when completing tasks, implementing major features, or before merging to verify work meets requirements

Search guidance and output contract for the Research employee. Read-only codebase exploration, uncertainty reduction, structured reports.

Returns authorization, receipt and inspection, disposition decisions, refund processing, fraud detection, and warranty claims management. Includes grading frameworks, disposition economics, fraud pattern recognition, and vendor recovery processes.

Scan skills to extract cross-cutting principles and distill them into rules — append, revise, or create new rule files.

Idiomatic Rust patterns, ownership, error handling, traits, concurrency, and best practices for building safe, performant applications.

Rust testing patterns including unit tests, integration tests, async testing, property-based testing, mocking, and coverage. Follows TDD methodology.

Screen capture and camera snapshots using macOS built-in tools. Full screen, region, window, webcam, and video recording. Use as default when tool-specific capture (Figma, Playwright, CDP) is unavailable. Prefer cli-jaw browser screenshot for web pages.

Perform language and framework specific security best-practice reviews and suggest improvements. Trigger only when the user explicitly requests security best practices guidance, a security review/report, or secure-by-default coding help. Trigger only for supported languages (python, javascript/typescript, go). Do not trigger for general code review, debugging, or non-security tasks.

Analyze git repositories to build a security ownership topology (people-to-file), compute bus factor and sensitive-code ownership, and export CSV/JSON for graph databases and visualization. Trigger only when the user explicitly wants a security-oriented ownership or bus-factor analysis grounded in git history (for example: orphaned sensitive code, security maintainers, CODEOWNERS reality checks for risk, sensitive hotspots, or ownership clusters). Do not trigger for general maintainer lists o...

Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown threat model. Trigger only when the user explicitly asks to threat model a codebase or path, enumerate threats/abuse paths, or perform AppSec threat modeling. Do not trigger for general architecture summaries, code review, or non-security design work.

System architecture design, ADRs, dependency analysis, architecture pattern selection (monolith, microservices, CQRS, event sourcing, hexagonal), database and tech stack decision matrices. Use when making architecture decisions or evaluating system design.

Use when the user asks to inspect Sentry issues or events, summarize recent production errors, or pull basic Sentry health data via the Sentry API; perform read-only queries with the bundled script and require `SENTRY_AUTH_TOKEN`.

Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.

Audit skills and commands for quality. Supports Quick Scan (changed only) and Full Stocktake modes with batch evaluation.

Use when the user asks to generate, remix, poll, list, download, or delete Sora videos via OpenAI\u2019s video API using the bundled CLI (`scripts/sora.py`), including requests like \u201cgenerate AI video,\u201d \u201cSora,\u201d \u201cvideo remix,\u201d \u201cdownload video/thumbnail/spritesheet,\u201d and batch video generation; requires `OPENAI_API_KEY` and Sora API access.

Use when the user asks for text-to-speech narration or voiceover, accessibility reads, audio prompts, or batch speech generation via the OpenAI Audio API; run the bundled CLI (`scripts/text_to_speech.py`) with built-in voices and require `OPENAI_API_KEY` for live calls. Custom voice creation is out of scope.

Terminal Spotify playback/search via spogo (preferred) or spotify_player.

Spring Security best practices for authn/authz, validation, CSRF, secrets, headers, rate limiting, and dependency security in Java Spring Boot services.

Runs CodeQL static analysis for security vulnerability detection using interprocedural data flow and taint tracking. Applicable when finding vulnerabilities, running a security scan, performing a security audit, running CodeQL, building a CodeQL database, selecting query rulesets, creating data extension models, or processing CodeQL SARIF output. NOT for writing custom QL queries or CI/CD pipeline setup.

Parse, analyze, and process SARIF (Static Analysis Results Interchange Format) files. Use when reading security scan results, aggregating findings from multiple tools, deduplicating alerts, extracting specific vulnerabilities, or integrating SARIF data into CI/CD pipelines.

Run Semgrep static analysis scan on a codebase using parallel subagents. Automatically

Manual context compaction at logical workflow boundaries to preserve context through task phases.

Summarize or extract text/transcripts from URLs, podcasts, and local files (great fallback for “transcribe this YouTube/video”).

Thread-safe data persistence in Swift using actors — in-memory cache with file-backed storage, eliminating data races by design.

Protocol-based dependency injection for testable Swift code — mock file system, network, and external APIs using focused protocols and Swift Testing.

Use when implementing any feature or bugfix, before writing implementation code

Interactive agent picker for composing and dispatching parallel teams from agent persona files.

Send voice/photos/documents (and optional text notices) to Telegram. Prefer Bot API first for non-text delivery; use local API for text/status and fallback.

Azure Verified Modules (AVM) requirements and best practices for developing certified Azure Terraform modules. Use when creating or reviewing Azure modules that need AVM certification.

Generate Terraform HCL code following HashiCorp's official style conventions and best practices. Use when writing, reviewing, or generating Terraform configurations.

Guide for writing Terraform tests (.tftest.hcl). Covers run blocks, assertions, mock providers, test modes, and CI integration.

Transform monolithic Terraform configurations into reusable, maintainable modules following HashiCorp's module design principles and community best practices.