- 📁 references/
- 📁 scripts/
- 📄 CHANGELOG.md
- 📄 LICENSE
- 📄 README.md
AWS Cloud Development Kit (CDK) expert for building cloud infrastructure with TypeScript/Python. Use when creating CDK stacks, defining CDK constructs, implementing infrastructure as code, or when the user mentions CDK, CloudFormation, IaC, cdk synth, cdk deploy, or wants to define AWS infrastructure programmatically. Covers CDK app structure, construct patterns, stack composition, and deployment workflows.
- 📁 references/
- 📁 scripts/
- 📄 SKILL.md
Bundle a Tidybot skill and its dependencies into a single executable Python script for robot submission. Use when (1) submitting a multi-dependency skill to the robot, (2) preparing code for the /code/execute API, (3) resolving deps.txt dependency chains into one file.
Review Python code for bugs, security issues, and style problems
Generate and analyze AI Bill of Materials (AIBOM) for Python projects using AI/ML components. Identifies AI models, datasets, tools, and frameworks for security and compliance tracking.
- 📁 .codex/
- 📁 docs/
- 📁 providers/
- 📄 SKILL.md
Employee agent lifecycle management system. Use when working with agents/ directory employee agents - starting, stopping, monitoring, or assigning tasks to Dev/QA agents running in tmux sessions. Completely independent of CAO, uses only tmux + Python.
- 📁 concepts/
- 📁 examples/
- 📁 patterns/
- 📄 SKILL.md
Use this skill whenever users are designing, modeling, or writing code for NATS JetStream — including stream configuration, subject namespace design, consumer types (pull vs push), ack policies, retention policies, delivery guarantees, messaging patterns (fanout, work queue, request-reply), idempotent publishing, exactly-once semantics, or JetStream code examples in Go, JavaScript, or Python. Use this skill even when the user doesn't say "JetStream" explicitly — if they're asking how to build a message queue, event stream, or worker system on NATS, this skill applies. Do NOT use for deployment/clustering/Kubernetes questions (use jetstream-deployment) or troubleshooting/monitoring (use jetstream-operations).
Assesses whether an existing Python, bash, or hybrid pipeline is a good fit for Seamless (content-addressed caching, reproducible execution, local-to-cluster scaling). Triggers when wrapping scripts or functions without rewriting them, avoiding recomputation, comparing workflow frameworks (vs Snakemake, Nextflow, CWL, Airflow, Prefect), migrating a pipeline, or setting up remote/HPC execution. Covers direct/delayed decorators, seamless-run CLI, nesting, module inclusion, scratch/witness patterns, deep checksums, and execution backends (local, jobserver, daskserver). Provides safe guidance on remote execution and determinism — avoids naive "copy code to server" suggestions.
NVIDIA DeepStream SDK 9.0 development with Python pyservicemaker API. Use when building video analytics pipelines, GStreamer-based video processing, TensorRT inference integration, object detection/tracking, or Kafka/message broker integration.
Nerve backend (Python) and frontend (React/TS) development and code contribution. Use when writing Python code for Nerve, fixing bugs, adding features, reviewing Nerve PRs, building the frontend, running tests, or working with the Nerve codebase. Triggers on "nerve code", "nerve PR", "fix nerve", "nerve feature", "nerve test", "build nerve UI", "nerve migration".
- 📁 development/
- 📁 getting-started/
- 📁 guide/
- 📄 index.md
- 📄 SKILL.md
Use the type-bridge Python ORM for TypeDB. Covers defining entities, relations, attributes, CRUD operations, queries, expressions, and schema management. Use when working with TypeDB in Python projects.
- 📁 assets/
- 📁 references/
- 📁 scripts/
- 📄 .gitignore
- 📄 LICENSE
- 📄 README.md
AI 虚拟试穿 Agent。用户提供服装信息（图片或文字描述均可）， Agent 全程引导完成：服装图预处理 → AI 生成模特 → 虚拟试穿合成 → 生成展示视频。 支持阿里云百炼试衣 API、豆包 Seedream 生图、豆包 Seedance 生视频。 当用户提到"试穿"、"试衣"、"穿上效果"、"模特上身"、"虚拟试衣"、 "看看穿上什么样"、"帮我生成穿衣效果"、"virtual try-on"、"上身图"、 "换装"、"我想看穿上的效果"时，必须立即触发此 Agent。 --- # AI 虚拟试穿 Agent ## 职责 引导用户完成虚拟试穿全流程，输出试穿效果图和展示视频。 不涉及上架、文案、定价。有上架需求告知使用 shopify-quick-listing。 --- ## 配置说明（告知用户时必须按此说明） **.env 文件的唯一标准位置是 `scripts/` 目录：** ``` ~/.claude/skills/ai-tryon/scripts/.env ← 正确位置 ~/.claude/skills/ai-tryon/.env ← 错误，不要放这里 ``` 告知用户配置的标准话术： > 请在 Skill 的 scripts 目录下创建 .env 文件： > ```bash > cp ~/.claude/skills/ai-tryon/scripts/.env.example \ > ~/.claude/skills/ai-tryon/scripts/.env > # 然后编辑填入 Key > ``` 不要让用户在 `ai-tryon/` 根目录或其他位置创建 .env。 --- ## 输出目录约束（最高优先级规则） **所有脚本调用都必须传 `--output-dir`，绝对禁止省略。** 输出目录的唯一真实来源是 `.env` 中的 `TRYON_OUTPUT_DIR` 环境变量： ```bash # .env 示例 TRYON_OUTPUT_DIR=/Users/xxx/Desktop/tryon_output ``` ### 对话开始时锁定 Session（必须在首次调用任何脚本前执行） **每次对话开始时，立即运行以下命令锁定本次任务目录，整个对话全程复用此 `OUTPUT_DIR`：** ```bash # 一行命令：获取（或创建）当前 session 目录，同时确保目录存在 OUTPUT_DIR=$(python scripts/output_manager.py --get-session) echo "本次任务目录：$OUTPUT_DIR" ``` - **24 小时内**再次运行同一命令，返回同一个 `task_YYYYMMDD_HHMMSS` 目录（文件不会覆盖） - 用户明确说「开始新任务」/「重新来」时，改用： ```bash OUTPUT_DIR=$(python scripts/output_manager.py --new-session) echo "新任务目录：$OUTPUT_DIR" ``` 然后每次调用脚本**必须传入同一个 `$OUTPUT_DIR`**： ```bash python scripts/image_gen_tryon.py --desc "..." --output-dir "$OUTPUT_DIR" python scripts/tryon_runner.py --garment g.jpg --output-dir "$OUTPUT_DIR" python scripts/video_gen.py --image img.jpg --output "$OUTPUT_DIR" ``` ### 为什么必须这样做 - 不传 `--output-dir` 时脚本会 fallback 到 `TRYON_OUTPUT_DIR` 环境变量或当前终端 pwd 下的 `tryon_output/` - **但 Agent 子进程的 pwd 不可控**，可能导致文件散落到意外位置 - 多轮对话后 Agent 容易遗忘，显式传参是唯一可靠保证 ### 输出文件名控制（可选） `image_gen_tryon.py` 支持 `--output-filename`，生成后会将第一个结果复制为指定文件名： ```bash python scripts/image_gen_tryon.py --desc "..." --output-dir "$OUTPUT_DIR" \ --output-filename model_ruyan_custom.jpg ``` ### 目录结构 每次对话/试穿任务自动创建独立的 session 子目录（以日期
BookLib — curated skills from canonical programming books. Covers Kotlin, Python, Java, TypeScript, Rust, architecture, DDD, data-intensive systems, UI design, and more. Install individual skills via npx skillsadd booklib-ai/booklib/<name>.
