Explorar skills

This skill should be used when the user needs to create, generate, or visualize diagrams. It actively generates diagrams using the right tool for the right complexity level, following a tiered approach from simple ASCII to complex rendered images. Triggers on: drawing flowcharts, architecture diagrams, sequence diagrams, ER diagrams, state machines, mind maps, data charts, visualizing data, adding

Defensibility and switching cost analysis for bootstrapped founders. Assesses real vs. fake moats, user-investment lock-in, network effects, data advantages, distribution moats, and long-term defensibility. Use when user runs `/moat-analysis`, asks about "defensibility", "switching costs", "moat", "competitive advantage", "lock-in", "barriers to entry", or needs to understand what makes a business

Generate a copy-paste-ready GPT prompt that produces a polished architecture or workflow diagram image, and optionally render the image directly via Codex CLI image generation. Trigger when the user wants to create, make, generate, draw, or visualize a diagram, architecture image, system workflow picture, or flowchart, or pastes a Mermaid diagram asking for a polished image.

Convert raw scanner findings into atomic work items in the backlog. Assigns priority, category, effort, and identifies file conflicts.

好吃好玩在苏州 - 苏州出行与美食规划Agent。优先推荐公交线路（旅游专线、好行观光巴士）， 辅以地铁换乘方案，结合苏州地铁食堂等特色餐饮，为用户规划吃喝玩乐一体化行程。 输出包含可视化HTML行程页面（含高德地图导航链接）。 使用场景：用户说"苏州怎么玩"、"从XX到YY怎么走"、"苏州一日游攻略"、"苏州美食推荐"等。

Show current progress of the audit and implementation system — scanner completion, backlog counts, blockers.

Run an audit scanner against ODD repositories. Reads scanner definition, picks unscanned items from coverage manifest, writes findings, updates navigation.

Verify a `review-ready` work item (or an entire batch) meets every acceptance criterion and every Quality Bar gate. Reject by default — each check requires cited evidence. Must run in a session distinct from the `/implement` session that produced the item.

Run the graph-retriever subagent — intelligent, iterative retrieval over the derived graph query layer. The maintainer asks a free-form information-need question ("where is X", "what depends on Y", "which findings concern Z"); the agent constructs a strong query, runs a bounded retrieve→read→judge→refine loop (≤10 iterations) with adaptive traversal, and returns a cited answer set. Emits substrate

Hugging Face Transformers with PyTorch Lightning - LightningModule integration, distributed training (FSDP/DeepSpeed), PEFT (LoRA/QLoRA), data pipelines with HF Datasets, evaluation metrics, and common NLP tasks

Review database schemas AND in-code data structures for correctness, integrity, performance, scalability, evolvability, simplicity, and design quality. Audits Ecto schemas, Postgres DDL/migrations, dbt models, and application types (Elixir structs/typespecs, TypeScript types). Use when: '/schema-review', 'review this schema', 'review this migration', 'review this data model', 'is this schema sound

Draft an upstream-repo GitHub issue on disk. Creates the next ISS file under issues/{repo}/ with paste-ready frontmatter + body skeleton. The actual filing into GitHub is a deliberate human action — this skill never opens a real issue.