- 📁 assets/
- 📁 references/
- 📄 SKILL.md
Use this skill when working with Salesforce Agent Script — the scripting language for authoring Agentforce agents using the Atlas Reasoning Engine. Triggers include: creating, modifying, or comprehending Agent Script agents; working with AiAuthoringBundle files or .agent files; designing topic graphs or flow control; producing or updating an Agent Spec; validating Agent Script or diagnosing compilation errors; previewing agents or debugging behavioral issues; deploying, publishing, activating, or deactivating agents; deleting or renaming agents; authoring AiEvaluationDefinition test specs or running agent tests. This skill teaches Agent Script from scratch — AI models have zero prior training data on this language. Do NOT use for Apex development, Flow building, Prompt Template authoring, Experience Cloud configuration, or general Salesforce CLI tasks unrelated to Agent Script.
- 📁 examples/
- 📄 enforce-hooks.py
- 📄 engine.sh
- 📄 generate.sh
Analyze a CLAUDE.md file and generate PreToolUse hook scripts that enforce its rules at the tool-call level. Use when users want their CLAUDE.md directives enforced as code rather than relying on prompt compliance. Reads the CLAUDE.md, identifies enforceable rules, generates standalone bash hook scripts, and wires them into .claude/settings.json.
Comprehensive guide for building Chrome extensions with Manifest V3. Use this skill whenever the user mentions Chrome extension, browser extension, manifest.json, content script, service worker (in extension context), popup, side panel, chrome.runtime, chrome.tabs, chrome.storage, chrome.scripting, background script, MV3, Manifest V3, or any Chrome extension API. Also trigger when the user wants to inject scripts into web pages, communicate between page and background, bypass CSP from a content script, build an RPC layer over chrome messaging, or publish to the Chrome Web Store. Covers both new extension projects and adding features to existing ones. Do NOT use for framework-specific questions.
- 📁 .github/
- 📁 adapters/
- 📁 assets/
- 📄 .clawhubignore
- 📄 .env.example
- 📄 .gitignore
Local Python orchestration skill: multi-agent workflows via shared blackboard file, permission gating, token budget scripts, and persistent project context. The bundled Python scripts make no network calls and have zero third-party dependencies. Workflow delegations via the host platform's sessions_send may invoke external model APIs.
Create a browser automation script that bundles multi-step browser interactions into a single browser_run_code call. Use when: 'automate browser flow', 'create browser script', 'script this browser task', 'bundle browser steps', 'reduce browser tokens'.
- 📁 scripts/
- 📄 README.md
- 📄 SKILL.md
Real-time AI agent security scanner. Detects prompt injection, jailbreak attempts, credential/secret leaks, PII exposure, and dangerous tool calls. Activate when: (1) scanning inbound messages or tool outputs for prompt injection, (2) checking outbound content for credential leaks or PII, (3) auditing agent session logs for security events, (4) evaluating tool call safety before execution, (5) user asks about security scanning or threat detection. Covers OWASP Top 10 Agentic AI risks. --- # ClawMoat — Security Moat for AI Agents ## Scripts All scripts are in `scripts/`. They wrap the `clawmoat` CLI and log results to `clawmoat-scan.log`. ### Scan Text Scan any text for threats (prompt injection, secrets, PII, exfiltration): ```bash scripts/scan.sh "text to scan" ``` Returns JSON with findings. Logs to `clawmoat-scan.log`. Exits non-zero on CRITICAL/HIGH findings. ### Scan File ```bash scripts/scan.sh --file /path/to/file.txt ``` ### Audit Session
- 📁 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
Write, review, debug, and explain PLECS C-Script code for custom control blocks in PLECS simulations. Use this skill whenever the user asks about C-Script, wants to implement a custom block in PLECS, needs help with PLECS macros (InputSignal, OutputSignal, ContState, DiscState, ZCSignal, etc.), asks about sample time configuration, state variables, zero-crossing detection, user parameters, or needs to port controller C code into a PLECS simulation. Trigger even if the user just mentions "PLECS block", "custom block", "C-Script", or "cscript". --- # PLECS C-Script Skill You are an expert on PLECS C-Script custom control blocks. When this skill is active, generate correct, well-structured C-Script code that integrates cleanly with the PLECS solver. For the full macro reference, see [references/macros.md](references/macros.md). For complete worked examples, see [references/examples.md](references/examples.md). If the user is editing or generating a `.plecs` file, load [references/plecs-file-format.md](references/plecs-file-format.md) and [references/cscript.plecs](references/cscript.plecs) for the complete file format and a working reference model.(CAUTION: if not required or edited directly, DO NOT GENERATE .plecs files) --- *ALWAYS READ ALL LINES OF THIS DOCUMENT SKILL.MD before making changes.* # C-Script Architecture ## Block Setup Parameters These are configured in the **Setup** tab of the C-Script block dialog before writing any code. ### `Number of inputs` Defines the number and width of input ports. | Value | Effect | |---|---| | `n` (scalar integer) | Single input port accepting a scalar signal | | `[n1, n2, ...]` (vector) | Multiple input ports; port `i` accepts a signal of width `ni` | | `-1` | Dynamic sizing: width determined by connected signal | > **Format note:** In the PLECS dialog both comma-separated (`[2, 3]`) and space-separated (`[2 3]`) are accepted. Inside `.plecs` files the space-separated form is used (e.g. `"[2 3]"`).
Runs shell script quality checks. Use when checking shell script quality, linting bash code, or validating scripts. Covers formatting with shfmt, static analysis with shellcheck, and portability checks.
- 📁 agents/
- 📁 evals/
- 📁 outputs/
- 📄 SKILL.md
Create low-friction coursework deliverables for general education, elective, and low-stakes college assignments, including PPT slides, short papers, reading reports, reflection essays, presentation scripts, discussion posts, and course summaries. Use this skill whenever the user mentions 水课, 通识课, 选修课, 小论文, 课程论文, 读书报告, 观后感, 汇报PPT, 课堂展示, 演讲稿, or asks to turn scattered course materials into a polished student-style deliverable. --- # Coursework Helper Skill Produce practical course deliverables from messy prompts, readings, course slides, topic requirements, and user notes. The goal is to help a busy student quickly get a usable PPT, short paper, speech script, or reflection while keeping claims grounded in provided material. ## Architecture ```text User request -> Intake: infer task type, deadline pressure, source materials, tone, and format -> Setup: index materials, check official file skills, initialize output directory -> Plan: choose deliverable path and create a lightweight outline -> Draft: produce content, slides/script, or paper -> Polish: add citations/evidence notes, fix tone, export requested formats ``` | Path | Use when | Primary output | |------|----------|----------------| | `slides` | PPT/class presentation/课堂展示/汇报 | `final_slides.md`, optional `.pptx`, speech script | | `paper` | 小论文/课程论文/读书报告/观后感/心得体会 | `final_paper.md`, optional `.docx`/`.pdf` | | `script` | 演讲稿/发言稿/答辩稿/课堂分享 | `final_script.md` | | `mixed` | User asks for PPT + paper + script, or task is ambiguous | combined deliverables | ## Startup Layer ### 1. Low-Friction Intake Infer as much as possible from the user's words and files. Ask only when the missing answer would change the deliverable.
Scripts, webhooks, cron, glue code
- 📁 assets/
- 📁 references/
- 📄 README.md
- 📄 SKILL.md
Build, modify, debug, and deploy agents with Agentforce Agent Script. TRIGGER when: user creates, modifies, or asks about .agent files or aiAuthoringBundle metadata; changes agent behavior, responses, or conversation logic; designs agent topics, actions, tools, sub-agents, or flow control; writes or reviews an Agent Spec; previews, debugs, deploys, publishes, or tests agents; uses Agent Script CLI commands (sf agent generate/preview/publish/test). DO NOT TRIGGER when: Apex development, Flow building, Prompt Template authoring, Experience Cloud configuration, or general Salesforce CLI tasks unrelated to Agent Script.
