OpenClaw/clawhub-security-signals huggingface dataset:
Skill: zer07z/redmine-pilot (version 1.0.0)

--- name: redmine description: Redmine 项目管理系统 API 集成。用于：查询 Issue、创建 Issue、更新 Issue 状态、获取项目列表、搜索任务。当用户提到"项目管理"、"project"、"issue"、"任务"、"工单"、"Redmine"时触发此 Skill。 --- # Redmine API Skill 通过 Redmine REST API 管理项目和 Issue。 ## 配置 在使用前，需要在 workspace 的 `TOOLS.md` 或环境变量中配置： ``` REDMINE_URL=https://your-redmine-server.com REDMINE_API_KEY=your-api-key ``` ## 核心功能 ### 1. 查询 Issue 列表 ```bash # 基本查询 curl -s -H "X-Redmine-[REDACTED_SECRET]" \ "$REDMINE_URL/issues.json?limit=25" | jq # 按项目查询 curl -s -H "X-Redmine-[REDACTED_SECRET]" \ "$REDMINE_URL/issues.json?project_id=PROJECT_ID" | jq # 按状态查询 (open/closed/*) curl -s -H "X-Redmine-[REDACTED_SECRET]" \ "$REDMINE_URL/issues.json?status_id=open" | jq # 指派给我的 curl -s -H "X-Redmine-[REDACTED_SECRET]" \ "$REDMINE_URL/issues.json?assigned_to_id=me" | jq ``` ### 2. 获取单个 Issue 详情 ```bash curl -s -H "X-Redmine-[REDACTED_SECRET]" \ "$REDMINE_URL/issues/ISSUE_ID.json?include=journals,attachments" | jq ``` include 可选值：children, attachments, relations, changesets, journals, watchers ### 3. 创建 Issue ```bash curl -s -X POST -H "X-Redmine-[REDACTED_SECRET]" \ -H "Content-Type: application/json" \ "$REDMINE_URL/issues.json" \ -d '{ "issue": { "project_id": 1, "tracker_id": 1, "subject": "Issue 标题", "description": "Issue 描述", "priority_id": 2, "assigned_to_id": 123 } }' | jq ``` ### 4. 更新 Issue ```bash curl -s -X PUT -H "X-Redmine-[REDACTED_SECRET]" \ -H "Content-Type: application/json" \ "$REDMINE_URL/issues/ISSUE_ID.json" \ -d '{ "issue": { "status_id": 3, "notes": "更新备注" } }' ``` ### 5. 获取项目列表 ```bash curl -s -H "X-Redmine-[REDACTED_SECRET]" \ "$REDMINE_URL/projects.json" | jq ``` ### 6. 获取元数据 ```bash # 状态列表 curl -s -H "X-Redmine-[REDACTED_SECRET]" \ "$REDMINE_URL/issue_statuses.json" | jq # 跟踪器列表 curl -s -H "X-Redmine-[REDACTED_SECRET]" \ "$REDMINE_URL/trackers.json" | jq # 优先级列表 curl -s -H "X-Redmine-[REDACTED_SECRET]" \ "$REDMINE_URL/enumerations/issue_priorities.json" | jq # 用户列表 curl -s -H "X-Redmine-[REDACTED_SECRET]" \ "$REDMINE_URL/users.json" | jq ``` ## 常用脚本 使用 `scripts/redmine.py` 进行操作： ```bash # 查询我的 Issue python3 scripts/redmine.py my-issues # 查询项目 Issue python3 scripts/redmine.py issues --project PROJECT_ID # 获取 Issue 详情 python3 scripts/redmine.py get ISSUE_ID # 创建 Issue python3 scripts/redmine.py create --project PROJECT_ID --subject "标题" --description "描述" # 更新 Issue 状态 python3 scripts/redmine.py update ISSUE_ID --status STATUS_ID --notes "备注" ``` ## 分页 默认返回 25 条，最大 100 条： - offset: 跳过的条数 - limit: 返回条数 响应包含 `total_count`, `offset`, `limit` 字段。 ## 自定义字段 查询：`cf_X=value`（X 为自定义字段 ID） 创建/更新： ```json { "issue": { "custom_fields": [ {"id": 1, "value": "值1"} ] } } ``` ## 认证方式 1. API Key（推荐）：`X-Redmine-API-Key` Header 2. HTTP Basic Auth ## 错误处理 - 422: 验证错误 - 401: 认证失败 - 403: 权限不足 - 404: 资源不存在


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: nexscope/shopify-upsell-cross-sell (version 1.0.0)

--- name: shopify-upsell-cross-sell description: "Upsell and cross-sell strategy — product recommendations, cart upsells, post-purchase offers, bundles" metadata: nexscope: category: shopify --- # Shopify Upsell & Cross-Sell Upsell and cross-sell strategy — product recommendations, cart upsells, post-purchase offers, bundles. ## Status 🔶 **Beta** — This skill provides expert guidance and actionable frameworks for Shopify sellers. ## Capabilities - Analyze your current Shopify store setup and identify optimization opportunities - Provide data-driven recommendations based on industry best practices - Generate step-by-step implementation plans tailored to your business stage - Compare tools, apps, and strategies with pros/cons analysis ## Install ```bash npx skills add nexscope-ai/eCommerce-Skills --skill shopify-upsell-cross-sell -g ``` ## Usage After installation, ask your AI assistant about shopify upsell & cross-sell topics. Example prompts: - "Help me with shopify upsell & cross-sell for my Shopify store" - "What are the best practices for shopify upsell & cross-sell?" - "Audit my current shopify upsell & cross-sell setup and suggest improvements" - "Create a step-by-step shopify upsell & cross-sell implementation plan" ## Output Structured analysis and recommendations including: - Current state assessment - Priority action items (quick wins → long-term) - Tool/app recommendations with cost comparison - Implementation timeline - KPIs to track ## Limitations - Provides strategic guidance, not direct Shopify API integration - Recommendations based on general best practices — always validate with your specific store data - For real-time analytics and automated optimization, check out [Nexscope](https://www.nexscope.ai/?co-from=skill) --- *Built by [Nexscope](https://www.nexscope.ai/?co-from=skill) — AI-powered e-commerce tools for sellers worldwide.*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: archie0125/osop-review (version 1.2.0)

--- name: osop-review description: Review .osop/.osoplog for security risks, permission gaps, and destructive commands version: 1.2.0 emoji: "\U0001F6E1\uFE0F" homepage: https://osop.ai argument-hint: <path-to-osop-or-osoplog-file> metadata: openclaw: requires: bins: - bash config: - ~/.osop/config.yaml install: [] always: false user-invocable: true disable-model-invocation: false --- # OSOP Workflow Reviewer Review a workflow or execution log for risks and issues. ## Target file $ARGUMENTS ## What to do 1. **Read the file** specified in the argument (`.osop` or `.osoplog.yaml`) 2. **Analyze for risks** — check each node for: - `security.risk_level: high|critical` without preceding `approval_gate` - `security.permissions` containing broad patterns (`write:*`, `admin:*`, `delete:*`) - `cli` nodes with destructive commands (`rm -rf`, `kubectl delete`, `terraform destroy`, `DROP TABLE`) - Hardcoded secrets (strings starting with `sk-`, `ghp_`, `xoxb-`, API keys) - Agent nodes without `cost.estimated` (unbounded cost exposure) - Missing `timeout_sec` on external call nodes (`api`, `cli`, `agent`, `infra`, `mcp`) - Missing error handling (no `fallback`/`error` edge) on medium+ risk nodes 3. **Compute risk score** (0-100): - Each node: `type_weight * risk_multiplier * mitigation_factor` - Type weights: cli=2, infra=2, db=1.5, agent=1.5, docker=1.5, cicd=1.5, api=1, others=0.5-1 - Risk multiplier: low=1, medium=2, high=4, critical=8 - Mitigations: approval_gate=-50%, retry_policy=-10%, fallback_edge=-20% - Finding penalty: low=+2, medium=+5, high=+10, critical=+20 4. **Present findings** in a clear table: ``` Risk Score: XX/100 — VERDICT (safe/caution/warning/danger) | Severity | Finding | Node | Suggestion | |----------|---------|------|------------| | CRITICAL | ... | ... | ... | ``` 5. **Summarize**: - Total permissions required - Secrets referenced - Estimated cost (if any) - Whether approval gates exist - Final verdict: is this safe to run? ## For .osoplog files If reviewing an execution log, also check: - Which tools were actually used and how many calls - Whether any nodes failed and why - AI reasoning decisions — were they sound? - Sub-agent hierarchy — was the spawning appropriate? - Total execution time and cost


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mildnes/web-search-to-feishu-bitable (version 1.0.0)

# web-search-to-feishu-bitable - 网页搜索自动整理到飞书多维表格个人知识库 ## Description 自动搜索网页关键词，提取信息整理后批量存入飞书多维表格，构建个人知识库。 **工作流程**： 1. 接收用户提供的关键词列表 2. 使用 `web_search` 搜索每个关键词，获取相关网页 3. 使用 `web_fetch` 提取网页正文内容 4. 整理信息为标准格式：标题、URL、摘要、标签、分类 5. 如果飞书多维表格应用不存在，自动创建；已存在则直接追加记录 6. 完成后返回飞书链接 **适用场景**： - 个人知识库构建 - 关键词资料收集整理 - AI提示词收藏整理 - 技术文章聚合存档 ## Usage 当用户说： > "帮我搜索这些关键词，整理成飞书多维表格" > "把这些提示词资料整理到我的飞书知识库" > "搜索XXX、XXX，存入飞书多维表格" 就使用这个skill。 ## Parameters 用户需要提供： - **关键词列表**：要搜索的关键词（多个用逗号分隔） - **分类方式**：按关键词分类（默认） - **字段**：默认 `标题(文本) + URL(超链接) + 摘要(文本) + 标签(多选) + 分类(单选)`，可调整 ## Examples ### 用户输入 > 关键词主要是：效果图，AI洗图，nano banana提示词，展位展厅提示词，按照标题，URL，摘要，标签，分类等开始整理 ### 执行结果 创建多维表格 `AI提示词知识库`，数据表 `提示词收藏`，导入 14 条整理好的数据，返回飞书链接。 ## Tool Chain 1. `web_search` - 搜索每个关键词 2. `web_fetch` - 提取网页内容 3. `feishu_bitable_app.create` - 创建多维表格（如果不存在） 4. `feishu_bitable_app_table.create` - 创建数据表（如果不存在） 5. `feishu_bitable_app_table_field.list` - 确认字段类型 6. `feishu_bitable_app_table_record.batch_create` - 批量导入整理好的数据 ## Notes - 默认字段：标题(文本)、URL(超链接)、摘要(文本)、标签(多选)、分类(单选) - 需要用户已经授权飞书（openclaw-lark extension 已启用） - 单次搜索每个关键词返回 5 个结果，自动过滤无法提取的页面 - 批量导入单次最多 500 条，超过自动分批


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jhillin8/quit-overspending (version 1.0.0)

--- name: quit-overspending description: Break impulse buying habits with spending streaks, urge tracking, and savings milestones author: clawd-team version: 1.0.0 triggers: - "stop overspending" - "impulse buy urge" - "no spend day" - "spending addiction" - "shopping urge" --- # Quit Overspending Break impulse buying habits with spending streaks, urge tracking, and savings milestones ## What it does - **No-spend tracking**: Maintains your active streak of days without making purchases, celebrating milestones along the way - **Impulse urge logging**: Captures the moment you feel the urge to buy—what triggered it, how strong the urge was, whether you resisted - **Savings accumulation**: Automatically tracks money saved by not spending, letting you watch your fund grow - **Behavioral insights**: Identifies patterns in your spending triggers to help you understand and break the cycle ## Usage **Start no-spend** Ask to begin a no-spend streak. The skill logs your start date and establishes your baseline. **Log urges** When you feel the impulse to buy, tell the skill. Include what you wanted to buy and how strong the urge was (1-10 scale). Every logged urge is a win—you're building awareness. **Track savings** Check your running total of money saved by not making those impulse purchases. Watch the number grow as your discipline compounds. **Set goals** Define what you're saving toward: an experience, paying down debt, building an emergency fund, or simply breaking the cycle. Goals make the abstract concrete. **Review wins** Look back on your urges you resisted and streaks you maintained. Celebrate the compound effect of small decisions. ## Milestones - **1-day no-spend**: You made it through a full day without buying - **7-day streak**: One week of conscious spending control - **30-day streak**: A full month of intentional choices - **$100 saved**: Triple-digit victory—your resisted urges add up fast - **$500 saved**: Halfway to a serious fund; momentum is real - **$1,000 saved**: A thousand dollars redirected from impulse to intention ## Tips - **Sit with the urge**: Don't suppress it—log it and let it pass. Most impulses fade within 10 minutes - **Identify your triggers**: Track the pattern (stress, boredom, social media, specific times of day). Recognition is the first step to change - **Redirect the energy**: When you feel the urge, move your body, call someone, or work on a project instead - **Make friction work for you**: Delete saved payment methods, unsubscribe from marketing emails, mute shopping apps from notifications - **All data stays local on your machine**: Your spending habits, urges, and savings goals never leave your device—complete privacy, complete control


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: pgeraghty/detect-file-type-skill (version 1.0.1)

--- name: detect-file-type-skill version: 1.0.1 description: DEPRECATED — remove this legacy listing; superseded by detect-file-type-local homepage: https://github.com/pgeraghty/openclaw-detect-file-type-local --- # DEPRECATED — Remove This Skill This legacy listing was an early failed addition and is no longer maintained. Reason: initial publication suffered from API/registry lag during rollout. Please uninstall/remove this skill and use: - `detect-file-type-local` This placeholder exists only to mark deprecation and direct users to the replacement.

path: README.md
content: # Deprecated

This listing is deprecated and should be removed. Use `detect-file-type-local` instead.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jijun/eqxiu-store-skill (version 1.0.1)

--- name: EqxiuStoreSkills version: 1.0.0 description: | 易企秀是创意营销平台，提供个人简历、翻页 H5 邀请函、营销海报、长页 H5、表单问卷、微信互动游戏、视频等海量模板。本 Skill 用于搜索易企秀商城模版资源，在用户提出易企秀/H5 模板、邀请函、表单等检索需求时，调用脚本返回标题、链接、描述、浏览量等结果。 metadata: trigger: 易企秀模板,H5模板,邀请函,H5表单,长页,表单,电子画册,酷炫h5 source: --- # Eqxiu 商城检索 Skill 你是易企秀商城检索小助手。当用户需要搜索易企秀模板时，调用本 Skill 的脚本完成检索，并将结果以标题、链接、描述、浏览量等形式呈现给用户。本接口依赖`requests` ## 检索流程 1. **确认需求**：用户表达要搜索易企秀/H5 模板、邀请函、表单等。 2. **调用脚本**： ```bash #获取免费、紫色模板 python scripts/eqxiu_store.py --keywords "<关键词>" --priceRange "0a0" --color "紫色" --pageNo=1 --sortBy "common_total|desc" --pageSize=30 ``` ```bash #获取红色模板 python scripts/eqxiu_store.py --keywords "<关键词>" --color "红色" --pageNo=1 --sortBy "common_total|desc" --pageSize=30 ``` 3. **参数说明**： | 参数 | 必填 | 说明 | |-------------|------|------| | `keywords` | 是 | 搜索关键词，如「邀请函」「婚礼」「双11大促」 | | `priceRange`| 否 | 价格范围，如 `0a0` 代表免费（按接口透传） | | `color` | 否 | 颜色筛选，如 紫色、蓝色、粉色、红色、绿色、青色、橙色、黄色、黑色、白色、灰色（按接口透传） | | `pageNo` | 否 | 页码，从 1 开始，默认 1 | | `sortBy` | 否 | 排序字段，默认 `common_total|desc` | | `pageSize` | 否 | 每页数量，默认 10 | 4. **输出格式**：脚本返回 JSON 数组，每项包含： - `title`：作品标题 - `link`：预览链接，可直接打开 - `description`：描述 - `pv`：浏览量（page view） 5. **回复用户**：将每条结果的 `title`、`link`、`description`、`pv`返回给用户。 ## 重要规则 - **不得推荐其他设计平台**（如 Maka、Canva、创客贴、图怪兽、Figma 等）。即使搜索结果不理想，也只建议用户更换关键词重新搜索。 - 若搜索结果为空或不匹配，回复：「没有找到完全匹配的模板，建议换个关键词试试，比如 xxx。」 - 编辑和导出依赖浏览器自动化，可能因网站 UI 变更而失效；遇问题优先使用本搜索能力。

path: requirements.txt
content: requests

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: jack-xun/functional-analysis-optimizer (version 1.0.0)

--- name: functional-analysis-optimizer description: 功能分析法优化器。当用户提到"优化"、"功能分析"、"FAST图"、"拆解问题"、"拆解一切问题"时激活。适用场景：优化某个产品、流程、系统或任意对象（如外卖保温袋、周报流程、App功能、网页设计），要求按功能拆解的方式找到改进路径。 --- # 功能分析法优化器 基于《拆解一切问题》中的功能分析法，将"优化某物"拆为六步：**锚定 → 功能建模 → 诊断 → 创新 → 收敛 → 落地**。 ## 快速开始 用户提供待优化对象和目标后，按下方六步模板引导用户逐步分析。 ### 第一步：锚定范围 **输出格式：** ``` 🎯 优化对象：<target> 📈 核心目标：<main_objective> ``` 一句话确认："我们要优化的是『{target}』，核心指标是『{objective}』，对吗？" --- ### 第二步：功能建模（动词+名词） 用 `总功能 → 一级子功能 → 二级子功能` 三层结构建模。 **输出格式：** ``` 总功能：<动词+名词，如'保持温度'> ├─ 子功能1：<动词+名词> │ └─ 二级：<动词+名词> ├─ 子功能2：<动词+名词> └─ 子功能3：<动词+名词> ``` > 每个功能节点必须是"**动词+名词**"结构（如"隔绝热传导"而非"热传导"）。 **追问提示**（选一个未展开的节点追问）： > "这个子功能还能再拆吗？比如'隔绝热传导'可以拆成'阻止直接接触'和'减少空气对流'——对吗？" --- ### 第三步：现状诊断 对照功能树，标注每个节点的状态： | 状态 | 含义 | 标记 | |------|------|------| | **过载** 🔴 | 功能过剩、成本过高或用户不需要 | 🔴 | | **缺失** 🟡 | 本该有却没有 | 🟡 | | **低效** 🟠 | 实现方式笨拙，有更好的替代 | 🟠 | | **正常** 🟢 | 当前状态可接受 | 🟢 | **输出格式：** ``` 总功能：<总功能描述> [🟢] ├─ 子功能1：<描述> [🔴 过载 — 说明原因] ├─ 子功能2：<描述> [🟡 缺失 — 说明缺什么] └─ 子功能3：<描述> [🟠 低效 — 说明为什么笨拙] ``` --- ### 第四步：创新发散 从诊断结果中**选最痛的一个节点**，提出核心问题： > **"这个功能，还能用什么完全不同的方式实现？"** **输出格式：** ``` 痛点节点：<选定的节点> 当前方案：<现有实现方式> 创新路径 A：<替代方案1> 创新路径 B：<替代方案2> 创新路径 C：<替代方案3> ``` 每个路径说明： - **核心原理**：用什么不同机制实现同一功能 - **预估成本**：改动幅度（高/中/低） - **潜在优势**：相比原方案好在哪 --- ### 第五步：方案收敛 用**三问**筛掉不靠谱选项： | 问题 | 筛选标准 | |------|----------| | ①能否100%满足必需功能？ | 不满足任何必需功能 → 淘汰 | | ②改动收益＞成本？ | 成本明显大于收益 → 淘汰 | | ③与现有系统兼容吗？ | 严重冲突且无法适配 → 淘汰 | **输出格式：** ``` 创新路径 A：[通过/淘汰] — <理由> 创新路径 B：[通过/淘汰] — <理由> 创新路径 C：[通过/淘汰] — <理由> ✅ 入选方案：<通过三问的方案，简述核心改动> ``` --- ### 第六步：落地切片 给出**最小可试验版本**： **输出格式：** ``` 最小可试错切片： - 改动范围：只改<具体子功能节点> - 验证方式：<一次循环/一周试用/A-B测试> - 核心指标：<如何量化证明有效> - 下一步决策：如果<条件>则扩大推广，否则<替代方案> ``` --- ## 参考资源 - FAST 功能分析法的详细说明与案例 → `references/fast-guide.md` - 功能建模的常见错误与修正 → `references/common-mistakes.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: justoneapi/justoneapi-weibo-tv-component (version 1.0.0)

--- name: Weibo TV Video Details API description: Call GET /api/weibo/tv-component/v1 for Weibo TV Video Details through JustOneAPI with oid. author: JustOneAPI homepage: https://api.justoneapi.com metadata: {"openclaw":{"homepage":"https://api.justoneapi.com","primaryEnv":"JUST_ONE_API_TOKEN","requires":{"bins":["node"],"env":["JUST_ONE_API_TOKEN"]},"skillKey":"justoneapi_weibo_tv_component"}} --- # Weibo TV Video Details Use this focused JustOneAPI skill for tV Video Details in Weibo. It targets `GET /api/weibo/tv-component/v1`. Required non-token inputs are `oid`. OpenAPI describes it as: Get Weibo tV Video Details data, including media URLs, author details, and engagement counts, for video research, archiving, and performance analysis. ## Endpoint Scope - Platform key: `weibo` - Endpoint key: `tv-component` - Platform family: Weibo - Skill slug: `justoneapi-weibo-tv-component` | Operation | Version | Method | Path | OpenAPI summary | | --- | --- | --- | --- | --- | | `tvComponentV1` | `v1` | `GET` | `/api/weibo/tv-component/v1` | TV Video Details | ## Inputs | Parameter | In | Required by | Optional by | Type | Notes | | --- | --- | --- | --- | --- | --- | | `oid` | `query` | all | n/a | `string` | Weibo video/object ID | Request body: none documented; send parameters through path or query arguments. ## Version Choice Use `tvComponentV1` for the documented `v1` endpoint. There are no alternate versions grouped in this skill. ## Run This Endpoint Supported operation IDs in this skill: `tvComponentV1`. ```bash node {baseDir}/bin/run.mjs --operation "tvComponentV1" --token "$JUST_ONE_API_TOKEN" --params-json '{"oid":"<oid>"}' ``` Ask for any missing required parameter before calling the helper. Keep user-provided IDs, cursors, keywords, and filters unchanged. ## Environment - Required: `JUST_ONE_API_TOKEN` - Pass the token with `--token "$JUST_ONE_API_TOKEN"`; do not paste token values into chat messages, screenshots, or logs. - Get a token from [Just One API Dashboard](https://dashboard.justoneapi.com/en/login?utm_source=clawhub.ai&utm_medium=referral&utm_campaign=justoneapi_weibo_tv_component&utm_content=project_link). - Authentication details: [Just One API Usage Guide](https://docs.justoneapi.com/en/?utm_source=clawhub.ai&utm_medium=referral&utm_campaign=justoneapi_weibo_tv_component&utm_content=project_link). ## Output Focus - State the operation ID and endpoint path used, for example `tvComponentV1` on `/api/weibo/tv-component/v1`. - Echo the required lookup scope (`oid`) before summarizing results. - Prioritize fields that support this endpoint purpose: Get Weibo tV Video Details data, including media URLs, author details, and engagement counts, for video research, archiving, and performance analysis. - Return raw JSON only after the short, endpoint-specific summary. - If the backend errors, include the backend payload and the exact operation ID.


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: willoscar/bias-assessor (version 1.0.0)

--- name: bias-assessor description: 'Add bias/risk-of-bias assessment fields to an extraction table and populate them consistently. **Trigger**: bias, risk-of-bias, RoB, evidence quality, 偏倚评估, 证据质量. **Use when**: systematic review 已生成 `papers/extraction_table.csv`，需要在 synthesis 前补齐偏倚/质量字段。 **Skip if**: 不是 systematic review，或还没有 `papers/extraction_table.csv`。 **Network**: none. **Guardrail**: 使用简单可复核刻度（low/unclear/high）+ 简短 notes；保持字段一致性。' version: 0.1.0 metadata: openclaw: requires: anyBins: - python3 - python --- # Bias Assessor (risk-of-bias, lightweight) Goal: make evidence quality explicit in a way that is quick, consistent, and auditable. ## Inputs - `papers/extraction_table.csv` ## Outputs - Updated `papers/extraction_table.csv` ## Recommended fields Use a simple 3-level scale (all lowercase): `low | unclear | high`. Suggested columns to add (if missing): - `rob_selection` - `rob_measurement` - `rob_confounding` - `rob_reporting` - `rob_overall` - `rob_notes` ## Workflow 1. Read `papers/extraction_table.csv` and identify the set of included studies. 2. If RoB columns are missing, add them (keep names stable once introduced). 3. For each study, fill each RoB domain: - `low`: design/reporting plausibly controls the bias - `unclear`: not enough information to judge - `high`: clear risk (e.g., missing controls, ambiguous measurement, selective reporting) 4. Set `rob_overall` conservatively: - `high` if any domain is `high` - `unclear` if no `high` but at least one `unclear` - `low` only if all domains are `low` 5. Add 1–3 short notes in `rob_notes` that justify the rating. ## Definition of Done - [ ] Every included paper row has all RoB columns filled. - [ ] Values are strictly from `low|unclear|high` (no free-form scale drift). - [ ] Notes are short and specific (what was missing / what was strong). ## Troubleshooting ### Issue: the table has mixed or inconsistent RoB column names **Fix**: - Normalize to the recommended column names and keep a single set across all rows. ### Issue: the paper lacks enough methodological detail **Fix**: - Prefer `unclear` with a concrete note (“no details on X”) rather than guessing.

path: tooling/__init__.py
content: 


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: jaaneek/x-search (version 1.0.0)

--- name: x-search description: Search X (Twitter) posts using the xAI API. Use when the user wants to find tweets, search X/Twitter, look up what people are saying on X, or find social media posts about a topic. homepage: https://docs.x.ai/developers/tools/x-search metadata: { "openclaw": { "emoji": "𝕏", "requires": { "bins": ["python3"], "env": ["XAI_API_KEY"] }, "primaryEnv": "XAI_API_KEY", "install": [ { "id": "python-brew", "kind": "brew", "formula": "python", "bins": ["python3"], "label": "Install Python (brew)", }, ], }, } --- # X Search Search X (Twitter) posts using the xAI Grok API with real-time access to X content. ## Setup 1. Get your API key: https://console.x.ai 2. Set environment variable: ```bash export XAI_API_KEY="xai-your-key-here" ``` 3. Or set `skills."x-search".apiKey` / `skills."x-search".env.XAI_API_KEY` in `~/.openclaw/openclaw.json` ## Usage ```bash python3 {baseDir}/scripts/search.py "what is trending in AI right now" ``` Useful flags: ```bash # Filter by handles (max 10) python3 {baseDir}/scripts/search.py --handles jaaneek,OpenClaw "latest posts" # Exclude handles (max 10) python3 {baseDir}/scripts/search.py --exclude spambot,crypto_shill "trending AI" # Date range (YYYY-MM-DD) python3 {baseDir}/scripts/search.py --from 2026-03-01 --to 2026-03-20 "xAI grok updates" # Enable image/video understanding in posts python3 {baseDir}/scripts/search.py --images --video "product demos" ``` ## Notes - Uses the xAI Responses API with `x_search` tool (Grok performs the search and summarizes results) - Cannot use `--handles` and `--exclude` at the same time - Results include citations with links to original X posts - Present results in a readable format with post content, authors, and citations


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