OpenClaw/clawhub-security-signals huggingface dataset:
Skill: lrjxgl/laolei-novel-writer (version 1.0.0)

--- name: "laolei-novel-writer" description: "长篇小说创作助手，一句话就能写出完整的小说。 Novel Writing Assistant - write complete novels with just one sentence." --- # AI长篇小说写作助手 这是一个专门用于帮助用户创作长篇小说的技能。只要用户输入小说的内容和章节数量，这个skill就能自动生成完整的小说。 ## 核心功能 只要用户提供小说的创意和章节数量，AI就能自动生成完整的长篇小说。 ### 1. 输入即创作 用户只需要提供： - 小说类型和题材 - 核心创意或一句话故事 - 想要的章节数量 AI会自动完成： - 世界观和设定构建 - 人物角色设计 - 剧情大纲规划 - 章节内容撰写 由于长篇小说内容量大，需要采用以下工作流程： #### 2.1 先写章节大纲 先规划每一章的标题和核心情节，再逐步填充详细内容。 #### 2.2 章节内容生成规则 每次撰写新章节时，必须包含以下两部分内容： 1. **用户提示词**：用户输入的创作要求 2. **已写章节精简内容**：所有已完成的章节精简版本 #### 2.3 章节精简规则 每个章节完成后，需要生成精简版本，汇总到一个文件中（用于后续章节的上下文）： **精简内容只保留**： - 出现的人物（姓名、身份） - 人物关系（师徒、仇敌、恋人等） - 宗门/家族名称和地位 - 物品（法宝、武器、药材等） - 功法（名称、等级、功效） - 修为境界 - 地名（城池、秘境、洞府等） - 发生的核心事件（不包含细节描写） **不保留**：详细的场景描写、心理活动、对话内容、战斗过程等 #### 2.4 精简内容压缩规则 **统一精简文件**：所有章节的精简内容保存到单个文件 `chapters_summary.md` **压缩规则**： - 如果 `chapters_summary.md` 超过8000字，需要进行二次压缩 - **必须保留**：开头3章和最后3章的精简信息（不压缩） - **中间章节**：进行压缩，保留核心要点 **二次压缩保留**： - 人物的最新属性（修为、地位、状态） - 最新的人物关系 - 物品/功法的最新归属 - 最新的宗门/家族状态 - 核心事件的简要结果 #### 2.5 死亡人员处理 - 在精简内容中去除所有已死亡人员的相关描述 - 将已死亡人员名单单独记录到文件（记录：姓名、身份、死亡原因、死亡章节） ### 3. 修订与润色 帮助你改进已有内容： - 文风统一性检查 - 剧情逻辑性审视 - 角色一致性检查 - 文字润色和表达优化 ## 使用方法 ### 启动方式 只要告诉AI你的想法，就能开始创作： - "我想写一个关于...的小说，共X章" - "帮我写一个...类型的故事" - "以...为开头，写一部...章的小说" ### 章节撰写详细流程 #### 第一步：创建项目文件结构 首次写作时，需要建立以下文件： ``` novel/ ├── outline.md # 小说大纲 ├── characters.md # 人物设定集 ├── world.md # 世界观设定 ├── chapters/ │ ├── chapter_001.md # 第1章正文 │ ├── chapter_002.md # 第2章正文 │ └── ... ├── chapters_summary.md # 所有章节精简内容汇总（超过8000字需压缩） └── deceased.md # 死亡人员名单 ``` #### 第二步：撰写章节流程 1. **用户提供创作提示词**：描述想要写的章节内容 2. **AI检查已写章节**：读取 `chapters_summary.md` 获取所有已写章节的精简内容 3. **生成章节内容**：结合用户提示词和精简的上下文撰写新章节 4. **更新精简版本**：将新章节的关键信息添加到 `chapters_summary.md` 5. **检查字数**：如超过8000字，压缩中间章节，保留开头3章和最后3章 6. **更新死亡名单**：如有人物死亡，添加到 `deceased.md` #### 第三步：上下文管理 - 每次写新章节前，必须读取 `chapters_summary.md` 获取上下文 - 确保新章节与前文保持连贯 - 注意人物关系和实力等级的一致性 - 如果章节数量增加导致最后3章变化，需要同步更新 ### 重要提示 1. **提供详细背景**：越详细的需求能得到越精准的回答 2. **指定风格**：如果你有特定的文风偏好，请一并说明 3. **分步进行**：建议先完成大纲和人物设定，再进行具体章节撰写 4. **及时保存**：重要内容记得保存到本地文件 ## 工作流程建议 ``` 创意 → 大纲 → 人物设定 → 章节撰写 → 修订润色 ``` 1. **第一步**：先讨论你想要写的故事类型和核心创意 2. **第二步**：一起完善故事大纲和世界观 3. **第三步**：创建主要人物档案 4. **第四步**：逐章撰写或一次性生成多章 5. **第五步**：回顾和修订 ## 输出格式 根据你的需求，我可以输出： - 纯文本章节内容 - Markdown格式的文档（方便你保存和编辑） - 人物卡/设定集 - 剧情大纲表格 --- 告诉我你想写什么样的故事，要多少章节，剩下的交给我！


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: runapi-ai/runapi-gpt-4o-image (version 0.2.4)

--- name: gpt-4o-image description: Generate and edit images with GPT-4o Image through RunAPI. Use when the user asks an agent to create, edit, or transform images with GPT-4o Image. Default to the RunAPI CLI for one-off generation; use SDKs only when the user is integrating RunAPI into an app or backend. documentation: https://runapi.ai/models/gpt-4o-image.md provider_page: https://runapi.ai/providers/openai.md catalog: https://runapi.ai/models.md metadata: openclaw: homepage: https://runapi.ai/models/gpt-4o-image requires: bins: - runapi install: - kind: brew formula: runapi-ai/tap/runapi bins: - runapi envVars: - name: RUNAPI_API_KEY required: false description: Optional RunAPI API key; runapi login or saved CLI config can also authenticate the runapi binary. --- # GPT-4o Image on RunAPI Generate and edit images with GPT-4o Image through RunAPI. The default path for one-off agent tasks is the `runapi` CLI; SDKs are for application integration. ## Routing decision - One-off generation, editing, or transformation for the user → use the **CLI path** with the `runapi` binary. - Building an app, backend, worker, library, or production codebase → use the **SDK integration path**. ## CLI path The `runapi` binary is the runtime dependency. Authenticate with `runapi login` (browser) or set `RUNAPI_API_KEY`; a saved CLI config also works — no required environment variable. Inspect the available actions and request fields with CLI help: ```shell runapi gpt-4o-image --help runapi gpt-4o-image text-to-image --help ``` Run a one-off task (synchronous — polls until the task completes): ```shell runapi gpt-4o-image text-to-image --input-file request.json ``` Submit asynchronously and poll separately: ```shell runapi gpt-4o-image text-to-image --async --input-file request.json runapi wait <task-id> --service gpt-4o-image --action text-to-image ``` Available actions: `text-to-image`. ## SDK integration path When integrating GPT-4o Image into an app, backend, worker, or library — not for one-off tasks — use a RunAPI SDK package: - JavaScript / TypeScript: `@runapi.ai/gpt-4o-image` - Ruby: `runapi-gpt_4o_image` - Go: `github.com/runapi-ai/gpt-4o-image-sdk/go` ## References - Model overview, pricing, and rate limits: https://runapi.ai/models/gpt-4o-image.md - Provider comparison: https://runapi.ai/providers/openai.md - Full model catalog: https://runapi.ai/models.md


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: teoslayer/pilot-task-parallel (version 1.0.0)

--- name: pilot-task-parallel description: > Fan-out tasks to multiple agents and merge results. Use this skill when: 1. You need to distribute independent work across multiple agents 2. You want to aggregate results from parallel task execution 3. You need to reduce execution time through parallelization Do NOT use this skill when: - Tasks have sequential dependencies (use pilot-task-chain) - You only need to run a single task - Order of execution matters for correctness tags: - pilot-protocol - task-workflow - parallel - fan-out license: AGPL-3.0 compatibility: > Requires pilot-protocol skill and pilotctl binary on PATH. The daemon must be running (pilotctl daemon start). metadata: author: vulture-labs version: "1.0" openclaw: requires: bins: - pilotctl homepage: https://pilotprotocol.network allowed-tools: - Bash --- # pilot-task-parallel Fan-out tasks to multiple agents for parallel execution and merge results. ## Commands ### Submit multiple tasks in parallel ```bash for AGENT in "${AGENTS[@]}"; do pilotctl --json task submit "$AGENT" --task "Compute task batch" & done wait ``` ### Wait for all completions ```bash while true; do ALL_DONE=true for TASK_ID in "${TASK_IDS[@]}"; do STATUS=$(pilotctl --json task list --type submitted | jq -r ".[] | select(.task_id == \"$TASK_ID\") | .status") [ "$STATUS" != "completed" ] && [ "$STATUS" != "failed" ] && ALL_DONE=false && break done [ "$ALL_DONE" == true ] && break sleep 2 done ``` ### Merge results ```bash pilotctl --json task list --type submitted | \ jq -r "[.[] | select(.task_id | IN(\"${TASK_IDS[@]}\")) | select(.status == \"completed\") | .result]" ``` ## Workflow Example Distribute image processing across GPU agents: ```bash #!/bin/bash GPU_AGENTS=$(pilotctl --json peers --search "gpu" | jq -r '.[].address') AGENT_ARRAY=($GPU_AGENTS) TASK_IDS=() for i in {1..10}; do AGENT=${AGENT_ARRAY[$((i % ${#AGENT_ARRAY[@]}))]} TASK=$(pilotctl --json task submit "$AGENT" \ --task "Process image batch index $i") TASK_IDS+=("$(echo "$TASK" | jq -r '.task_id')") done # Wait for all tasks while true; do ALL_DONE=true for TASK_ID in "${TASK_IDS[@]}"; do STATUS=$(pilotctl --json task list --type submitted | \ jq -r ".[] | select(.task_id == \"$TASK_ID\") | .status") [ "$STATUS" != "completed" ] && [ "$STATUS" != "failed" ] && ALL_DONE=false && break done [ "$ALL_DONE" == true ] && break sleep 2 done echo "All tasks completed" ``` ## Dependencies Requires pilot-protocol skill, jq, and Bash 4.0+.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: googleworkspace-bot/gws-forms (version 1.0.12)

--- name: gws-forms description: "Read and write Google Forms." metadata: version: 0.22.5 openclaw: category: "productivity" requires: bins: - gws cliHelp: "gws forms --help" --- # forms (v1) > **PREREQUISITE:** Read `../gws-shared/SKILL.md` for auth, global flags, and security rules. If missing, run `gws generate-skills` to create it. ```bash gws forms <resource> <method> [flags] ``` ## API Resources ### forms - `batchUpdate` — Change the form with a batch of updates. - `create` — Create a new form using the title given in the provided form message in the request. *Important:* Only the form.info.title and form.info.document_title fields are copied to the new form. All other fields including the form description, items and settings are disallowed. To create a new form and add items, you must first call forms.create to create an empty form with a title and (optional) document title, and then call forms.update to add the items. - `get` — Get a form. - `setPublishSettings` — Updates the publish settings of a form. Legacy forms aren't supported because they don't have the `publish_settings` field. - `responses` — Operations on the 'responses' resource - `watches` — Operations on the 'watches' resource ## Discovering Commands Before calling any API method, inspect it: ```bash # Browse resources and methods gws forms --help # Inspect a method's required params, types, and defaults gws schema forms.<resource>.<method> ``` Use `gws schema` output to build your `--params` and `--json` flags.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: aoturlab/bilibit (version 0.1.11)

--- name: bilibit version: 0.1.8 description: B 站视频下载工具。支持哔哩哔哩视频下载、弹幕下载。用户说"B 站下载"、"哔哩哔哩"、"bilibili"时使用。无需 API Key。纯下载工具，不支持搜索。 license: MIT homepage: https://github.com/AoturLab/bilibit metadata: openclaw: emoji: 🎬 requires: bins: [bbdown, ffmpeg] aliases: [B 站下载，哔哩哔哩下载，bilibili 下载，B 站视频，哔哩哔哩，bilibili,B 站，b 站，视频下载，弹幕下载] --- # 🎬 bilibit - B 站视频下载专家 极简 B 站视频下载工具。粘贴 URL，一键下载视频和弹幕。 --- ## 📦 快速安装 ```bash # clawhub clawhub install bilibit # npm npm install -g bilibit ``` --- ## 🚀 使用示例 ### 下载视频 ```bash bilibit https://b23.tv/BV1xx ``` ### 下载带弹幕 ```bash bilibit https://b23.tv/BV1xx --danmaku ``` --- ## 💬 AI 交互规范（重要！） ### 触发场景 **当用户说这些话时，使用 bilibit**： - "下载这个 B 站视频" + URL - "B 站下载" - "哔哩哔哩视频" - "下载弹幕" **不支持的场景**： - ❌ "搜索 B 站视频" - bilibit 不支持搜索，需要用户提供 URL - ❌ "找某个 UP 主的视频" - 不支持搜索，需用户先在 B 站找到 URL ### 输出格式规范 **输出格式规范**： ``` 📺 下载完成！已保存到：xxx 📌 回复序号查看历史 ``` **禁止行为**： - ❌ 不要转成表格格式 - ❌ 不要重新排序 - ❌ 不要用 `[]()` 包裹 URL **必须保留**： - ✅ 原始输出格式 - ✅ 下载完成提示 --- ## 📋 完整命令 | 命令 | 说明 | 示例 | |------|------|------| | `bilibit <url>` | 下载视频 | `bilibit https://b23.tv/BV1xx` | | `bilibit <url> --danmaku` | 下载 + 弹幕 | `bilibit ... --danmaku` | | `bilibit <url> --quality 4K` | 指定画质 | `bilibit ... --quality 4K` | | `bilibit history` | 下载历史 | `bilibit history` | --- ## ⚠️ 注意事项 - 仅限个人学习使用 - 大会员画质需要 Cookie - 弹幕保存为 XML 格式 --- ## 🔗 相关链接 - GitHub: https://github.com/AoturLab/bilibit - 问题反馈：https://github.com/AoturLab/bilibit/issues


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector:  ()
Skill: benmjohnson69/benos-bootstrap (version 1.0.0)

# benos-bootstrap BenOS Bootstrap is a system-initialization skill for OpenClaw-based agent stacks. ## Purpose This skill initializes a BenOS runtime environment inside OpenClaw. It performs startup validation, establishes baseline state, and ensures required system dependencies are reachable before other BenOS skills execute. ## What It Does - Verifies workspace structure and sandbox integrity - Confirms agent runtime configuration is valid - Checks required environment variables - Validates Node runtime compatibility - Returns structured health status for orchestration logic ## Why It Exists BenOS is designed as a deterministic, structured executive runtime overlay. The bootstrap layer ensures predictable startup behavior and prevents downstream skills from executing in an inconsistent environment. This skill is intentionally lightweight but foundational. It is meant to run prior to advanced orchestration or automation layers. ## Usage This skill is invoked during startup or manually when system validation is required. Example: Run benos-bootstrap to verify runtime health before deploying additional automation modules. ## Output Returns structured JSON with: - ok (boolean) - message (string) - environment metadata (future expansion) ## Roadmap Future versions will: - Add environment diff detection - Integrate structured logging - Support pre-flight dependency resolution

path: index.js
content: module.exports = { name: "benos-bootstrap", description: "Bootstrap layer for BenOS", async run(ctx) { return { ok: true, message: "BenOS bootstrap active." }; } };


path: package.json
content: name: benos-bootstrap
version: 1.0.0
description: Bootstrap layer for BenOS identity alignment.
private: no
type: commonjs

path: skill.json
content: name: benos-bootstrap
version: 1.0.0
description: Bootstrap layer for BenOS identity alignment.
type: agent
main: index.js

Security scan verdicts — ClawScan: suspicious | static analysis:  | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xueyetianya/awesome-cheatsheets (version 2.0.0)

--- version: "2.0.0" name: Awesome Cheatsheets description: "👩‍💻👨‍💻 Awesome cheatsheets for popular programming languages, frameworks and development tools. They awesome cheatsheets, javascript, backend, bash." author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills --- # Awesome Cheatsheets 👩‍💻👨‍💻 Awesome cheatsheets for popular programming languages, frameworks and development tools. They include everything you should know in one single file. ## Commands - `help` - Help - `run` - Run - `info` - Info - `status` - Status ## Features - Core functionality from LeCoupa/awesome-cheatsheets ## Usage Run any command: `awesome-cheatsheets <command> [args]` --- 💬 Feedback & Feature Requests: https://bytesagain.com/feedback Powered by BytesAgain | bytesagain.com ## Examples ```bash # Show help awesome-cheatsheets help # Run awesome-cheatsheets run ``` - Run `awesome-cheatsheets help` for all commands ## Configuration Set `AWESOME_CHEATSHEETS_DIR` to change data directory. Default: `~/.local/share/awesome-cheatsheets/`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: cinience/volcengine-ai-search-rag (version 1.0.0)

--- name: volcengine-ai-search-rag description: Retrieval and RAG workflow on Volcengine AI stack. Use when users need embedding search, document indexing, top-k retrieval, grounding prompts, or search relevance tuning. --- # volcengine-ai-search-rag Implement retrieval-first answering with explicit indexing, retrieval, and grounding stages. ## Execution Checklist 1. Confirm corpus source and chunking strategy. 2. Generate embeddings and build/update index. 3. Retrieve top-k context with filters. 4. Build grounded answer with citations to retrieved chunks. ## Quality Rules - Separate retrieval prompt from generation prompt. - Keep chunk metadata (source, timestamp, id). - Return confidence and fallback path if no hits. ## References - `references/sources.md`

path: agents/openai.yaml
content: interface:
  display_name: "Volcengine Search RAG"
  short_description: "Build retrieval pipelines with grounded answers"
  default_prompt: "Use $volcengine-ai-search-rag to retrieve context and answer this question."


path: references/sources.md
content: # Sources

- Volcengine embedding and vector retrieval documentation
- RAG architecture and evaluation guidelines


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ivangdavila/brainstorm (version 1.0.0)

--- name: Brainstorm description: Generate ideas fast. Adapt depth and structure to what the user actually needs. --- ## Default: Ideas First When user asks for ideas, **generate immediately**. Don't interrogate first. If context is missing, the ideas will show it — user will clarify naturally. ## Adapt to the Request | Signal | Response | |--------|----------| | "Give me 5 ideas" | 5 ideas, simple list | | "I'm stuck on X" | 3-5 angles to unstick | | "Brainstorm names" | 10-20 options, varied styles | | "Help me think through X" | Explore, ask clarifying Qs | | "What are my options?" | Structured comparison | Match the energy. Quick request → quick response. Deep request → go deep. ## Only Ask If - Request is genuinely ambiguous (not just incomplete) - You're about to invest significant effort - Constraints would completely change the output One question max. Not five. ## Structure When It Helps Don't force categories. Use structure only when it clarifies: - **Comparisons:** Table with trade-offs - **Many ideas:** Group by theme - **Recommendations:** Highlight top pick with reasoning Flat list is fine for most requests. ## After First Round If user wants more: go deeper, wider, or different direction. If user is silent: they got what they needed. Don't push. Check `modes.md` for different brainstorm approaches. Check `prompts.md` for creative expansion techniques. --- **Related:** For multi-perspective exploration, see `diverge`. For iteration, see `loop`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: competitor-monitor (version 0.1.0)

--- name: competitor-monitor description: 竞品监控 - 自动追踪对手动态，生成监控报告。支持自定义关键词、网站列表，定时抓取，生成报告推送到飞书/邮件。适用于运营，市场，研究人员。 --- # 竞品监控 (Competitor Monitor) ## 功能 - 自定义监控关键词：设置想要监控的竞争对手品牌、产品关键词 - 追踪指定网站：监控竞争对手官网、电商页面的更新 - 社交媒体监控：追踪对手的微博、微信、抖音等账号动态 - 定时自动抓取：支持每日/每周定时执行 - 报告生成：自动生成结构化监控报告 - 多渠道推送：支持飞书、邮件、企业微信推送 ## 使用场景 1. 竞品新品发布监控 2. 对手价格变动追踪 3. 营销活动监控 4. 舆情监控 ## 使用方式 ```bash # 设置监控任务 competitor-monitor setup --keywords "竞品A,竞品B" --sites example.com # 手动执行监控 competitor-monitor run # 查看报告 competitor-monitor report ``` ## 配置说明 支持通过环境变量或配置文件设置： - 监控关键词列表 - 目标网站列表 - 推送渠道（飞书/邮件） - 抓取频率 ## 价格 - 免费版：2个关键词，1个网站 - 专业版：¥99/月：无限制 - 企业版：¥299/月：API接入 + 定制 ## 适用人群 - 运营人员 - 市场研究人员 - 产品经理 - 创业者


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: daniellummis/github-actions-run-gap-audit (version 1.1.0)

--- name: github-actions-run-gap-audit description: Detect GitHub Actions workflow groups that stopped running on their normal cadence using median run intervals and current inactivity gap. version: 1.1.0 metadata: {"openclaw":{"requires":{"bins":["bash","python3"]}}} --- # GitHub Actions Run Gap Audit Use this skill to detect workflow groups that have gone unexpectedly quiet (stale triggers, broken schedules, disabled automation, branch drift). ## What this skill does - Reads GitHub Actions run JSON exports - Groups by repository + workflow + branch + event - Computes historical cadence (median and p90 interval hours) - Compares latest inactivity gap vs historical cadence - Scores risk severity (`ok`, `warn`, `critical`) - Emits text or JSON for CI checks and automation guardrails ## Inputs Optional: - `RUN_GLOB` (default: `artifacts/github-actions/*.json`) - `TOP_N` (default: `20`) - `OUTPUT_FORMAT` (`text` or `json`, default: `text`) - `MIN_RUNS` (default: `4`) - `WARN_GAP_MULTIPLIER` (default: `2.0`) - `CRITICAL_GAP_MULTIPLIER` (default: `3.5`) - `MIN_WARN_GAP_HOURS` (default: `12`) - `MIN_CRITICAL_GAP_HOURS` (default: `24`) - `WORKFLOW_MATCH` (regex, optional) - `WORKFLOW_EXCLUDE` (regex, optional) - `BRANCH_MATCH` (regex, optional) - `BRANCH_EXCLUDE` (regex, optional) - `EVENT_MATCH` (regex, optional) - `EVENT_EXCLUDE` (regex, optional) - `REPO_MATCH` (regex, optional) - `REPO_EXCLUDE` (regex, optional) - `RUN_ID_MATCH` (regex, optional) - `RUN_ID_EXCLUDE` (regex, optional) - `RUN_URL_MATCH` (regex, optional) - `RUN_URL_EXCLUDE` (regex, optional) - `NOW_ISO` (optional fixed evaluation time for deterministic CI tests) - `FAIL_ON_CRITICAL` (`0` or `1`, default: `0`) ## Collect run JSON ```bash gh run view <run-id> --json databaseId,workflowName,event,conclusion,headBranch,headSha,createdAt,updatedAt,startedAt,url,repository \ > artifacts/github-actions/run-<run-id>.json ``` ## Run Text report: ```bash RUN_GLOB='artifacts/github-actions/*.json' \ MIN_RUNS=5 \ WARN_GAP_MULTIPLIER=2.25 \ bash skills/github-actions-run-gap-audit/scripts/run-gap-audit.sh ``` JSON output with fail gate: ```bash RUN_GLOB='artifacts/github-actions/*.json' \ OUTPUT_FORMAT=json \ FAIL_ON_CRITICAL=1 \ bash skills/github-actions-run-gap-audit/scripts/run-gap-audit.sh ``` Targeted run-scope triage: ```bash RUN_GLOB='artifacts/github-actions/*.json' \ RUN_ID_MATCH='^(88|89)' \ RUN_URL_EXCLUDE='rerun' \ OUTPUT_FORMAT=json \ bash skills/github-actions-run-gap-audit/scripts/run-gap-audit.sh ``` Run with bundled fixtures: ```bash RUN_GLOB='skills/github-actions-run-gap-audit/fixtures/*.json' \ NOW_ISO='2026-03-07T00:00:00Z' \ bash skills/github-actions-run-gap-audit/scripts/run-gap-audit.sh ``` ## Output contract - Exit `0` in report mode (default) - Exit `1` when `FAIL_ON_CRITICAL=1` and one or more groups are critical - Text mode prints summary + ranked stale workflow groups - JSON mode prints summary + ranked groups + critical group details


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: franklu0819-lang/coze-asr (version 1.0.0)

--- name: coze-asr description: Automatic Speech Recognition (ASR) using Coze API. Use when you need to transcribe audio files to text. Supports Chinese audio transcription via Coze's speech-to-text API. metadata: { "openclaw": { "requires": { "bins": ["jq"], "env": ["COZE_API_KEY"] }, }, } --- # Coze Automatic Speech Recognition (ASR) Transcribe audio files to text using Coze API. ## Setup **1. Get your API Key:** Get a key from [Coze Platform](https://www.coze.cn/) **2. Set it in your environment:** ```bash export COZE_API_KEY="your-key-here" ``` ## Supported Audio Formats - **MP3** - Recommended - **WAV** - Supported - **OGG** - Supported (包括 opus 编码) > **Note:** Coze API 原生支持 mp3、wav、ogg 格式，无需转换。 ## Usage ### Basic Transcription Transcribe an audio file: ```bash bash scripts/speech_to_text.sh recording.mp3 ``` ### Full Options ```bash bash scripts/speech_to_text.sh <audio_file> [language] ``` **Parameters:** - `audio_file` (required): Path to audio file - `language` (optional): Language code (default: zh) ## Output Format The script outputs JSON with transcribed text. Example output: ```json { "text": "你好，这是转录的文本内容" } ``` ## Troubleshooting **File Size Issues:** - Check Coze API documentation for file size limits - Reduce sample rate or bit depth if needed **Poor Accuracy:** - Improve audio quality - Ensure clear speech and minimal noise - Use appropriate language code **Format Issues:** - Ensure file is not corrupted - Verify audio can be played by standard players


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: polityang/telegram-voice (version 1.0.0)

--- name: voice description: Voice communication via Telegram. Automatically transcribes incoming voice messages using faster-whisper and replies with TTS voice. Use for all voice-related interactions on Telegram. --- # Voice Communication This skill enables voice communication on Telegram: 1. **Receive**: Transcribe voice messages using faster-whisper 2. **Reply**: Send voice replies using TTS ## Incoming Voice (Automatic) When receiving voice messages (.ogg files), use faster-whisper to transcribe: ```python from faster_whisper import WhisperModel model = WhisperModel('base', device='cpu', compute_type='int8') segments, info = model.transcribe('<file_path>', language='zh') result = ''.join([s.text for s in segments]) ``` ## Outgoing Voice (TTS) Use the tts tool to send voice replies: ```json { "action": "send", "channel": "telegram", "message": "<text>", "asVoice": true } ``` Or use the tts tool directly: ```json { "channel": "telegram", "text": "<text to speak>" } ``` ## Language - Input: Auto-detect or specify language (zh for Chinese) - Output: Match user's language preference ## Requirements - faster-whisper: `pip install faster-whisper` - TTS already configured in OpenClaw


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)