OpenClaw/clawhub-security-signals huggingface dataset:
Skill: rustyorb/agent-evaluation (version 1.0.0)

--- name: agent-evaluation description: "Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent." source: vibeship-spawner-skills (Apache 2.0) --- # Agent Evaluation You're a quality engineer who has seen agents that aced benchmarks fail spectacularly in production. You've learned that evaluating LLM agents is fundamentally different from testing traditional software—the same input can produce different outputs, and "correct" often has no single answer. You've built evaluation frameworks that catch issues before production: behavioral regression tests, capability assessments, and reliability metrics. You understand that the goal isn't 100% test pass rate—it ## Capabilities - agent-testing - benchmark-design - capability-assessment - reliability-metrics - regression-testing ## Requirements - testing-fundamentals - llm-fundamentals ## Patterns ### Statistical Test Evaluation Run tests multiple times and analyze result distributions ### Behavioral Contract Testing Define and test agent behavioral invariants ### Adversarial Testing Actively try to break agent behavior ## Anti-Patterns ### ❌ Single-Run Testing ### ❌ Only Happy Path Tests ### ❌ Output String Matching ## ⚠️ Sharp Edges | Issue | Severity | Solution | |-------|----------|----------| | Agent scores well on benchmarks but fails in production | high | // Bridge benchmark and production evaluation | | Same test passes sometimes, fails other times | high | // Handle flaky tests in LLM agent evaluation | | Agent optimized for metric, not actual task | medium | // Multi-dimensional evaluation to prevent gaming | | Test data accidentally used in training or prompts | critical | // Prevent data leakage in agent evaluation | ## Related Skills Works well with: `multi-agent-orchestration`, `agent-communication`, `autonomous-agents`


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

--- name: yahoo-data-fetcher description: Fetch real-time stock quotes from Yahoo Finance. user-invocable: true metadata: moltbot: emoji: "📈" requires: bins: ["node"] homepage: https://query1.finance.yahoo.com/v7/finance/quote --- # Yahoo Data Fetcher – Stock Quote Get current stock price data from Yahoo Finance. This skill fetches the latest market quote for one or more stock symbols and returns normalized JSON output. --- ## Command ### `/stock quote` Fetch the latest quote for one or more stock symbols. --- ## Input - `symbols` (string or array of strings) Examples: - `"AAPL"` - `"AAPL MSFT TSLA"` - `"AAPL,MSFT,TSLA"` - `["AAPL", "MSFT"]` - `{ "symbols": ["AAPL", "MSFT"] }` --- ## Output For each symbol: - `symbol` – stock ticker - `price` – latest market price - `change` – absolute price change - `changePercent` – percentage change - `currency` – trading currency - `marketState` – market status (e.g. `REGULAR`, `CLOSED`) Example output: ```json [ { "symbol": "AAPL", "price": 189.12, "change": 1.23, "changePercent": 0.65, "currency": "USD", "marketState": "REGULAR" } ]

path: alltemp.sh
content: #!/bin/bash
# Script: my-pi-temp.sh
# Purpose: Display the ARM CPU and GPU  temperature of Raspberry Pi 2/3 
# Author: Vivek Gite <www.cyberciti.biz> under GPL v2.x+
# -------------------------------------------------------
cpu=$( < /sys/class/thermal/thermal_zone0/temp)
#echo "$(date) @ $(hostname)"
#echo "-------------------------------------------"
echo "CPU=$((cpu/1000))"
echo "GPU=$(vcgencmd measure_temp | egrep -o '[0-9]*\.[0-9]*')"

#vcgencmd measure_temp | egrep -o '[0-9]*\.[0-9]*'


Security scan verdicts — ClawScan: suspicious | static analysis:  | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: chinasong/gougoubi-create-condition (version 1.0.2)

--- name: gougoubi-create-condition description: Create a Gougoubi proposal condition from minimal input with deterministic defaults for deadline, trade deadline, normalization, and transaction submission. Use when users want to add conditions to an existing proposal. metadata: pattern: generator interaction: single-turn domain: gougoubi-pbft outputs: structured-json clawdbot: emoji: "🧩" os: ["darwin", "linux", "win32"] --- # Gougoubi Create Condition Use this skill to create a condition under an existing Gougoubi proposal from the smallest practical input. ## Use This Skill When - The user wants to add one or more conditions to an existing proposal. - The user only provides proposal identifier and condition title. - The agent should auto-fill default dates and flags. ## Do Not Use This Skill When - The user wants to create a new proposal. Use `gougoubi-create-prediction`. - The user wants activation, result submission, or reward claiming. ## Minimal Input ```json { "proposalId": "0x... or proposal identifier", "conditionName": "Will Team A win the match?" } ``` ## Defaults - Resolve `proposalId` to `proposalAddress`. - `deadlineDateTime`: proposal deadline. - `deadlineTimezone`: user locale, fallback `UTC`. - `tradeDeadlineDateTime`: `deadlineDateTime - 1 hour`, clamped to a valid future time. - `tradeDeadlineTimezone`: same as `deadlineTimezone`. - `conditionImageUrl`: empty string. - `conditionRules`: empty string. - `skills`: empty string. - `isNormalized`: `true`. ## Generator Flow Step 1: Validate minimal input. Step 2: Resolve proposal and load proposal deadline. Step 3: Generate all default date fields and clamp invalid values. Step 4: Convert datetimes to unix seconds. Step 5: Validate final payload: - `conditionName` non-empty - `deadline > now` - `tradeDeadline > now` - `tradeDeadline <= deadline` Step 6: Submit the canonical contract call in this order: 1. `conditionName` 2. `deadline` 3. `tradeDeadline` 4. `conditionImageUrl` 5. `conditionRules` 6. `skills` 7. `isNormalized` Step 7: Wait for confirmation and return the normalized payload. ## Output ```json { "ok": true, "mode": "browser|contract", "txHash": "0x...", "proposalAddress": "0x...", "normalizedInput": { "proposalId": "", "proposalAddress": "", "conditionName": "", "deadlineDateTime": "", "deadlineTimezone": "", "tradeDeadlineDateTime": "", "tradeDeadlineTimezone": "", "defaultsApplied": true, "tradeDeadlinePolicy": [REDACTED_SECRET] }, "warnings": [] } ``` Failure: ```json { "ok": false, "stage": "validation|resolve-proposal|create|confirm", "error": "reason", "retryable": true } ``` ## Boundaries - Never bypass wallet confirmation. - Keep defaults deterministic and explain them in output when relevant.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: leooooooow/creator-attribution-lite (version 1.0.3)

--- name: creator-attribution-lite description: Connect creator content outputs to practical business outcomes using a lightweight attribution model. Use when the user asks which posts drove clicks/leads/sales, wants to prioritize high-ROI content types, or needs simple performance decisions without full BI setup. --- # Creator Attribution Lite ## Skill Card - **Category:** Measurement - **Core problem:** Which content actions actually move outcomes? - **Best for:** Small-team performance review - **Expected input:** Content list + campaign metadata + simple outcome fields - **Expected output:** Lightweight attribution view + insight notes - **Creatop handoff:** Feed winning patterns back to Creatop templates ## What this does Show which content likely moved business outcomes, not just vanity metrics. ## Workflow ### 1) Validate data availability Minimum fields per content item: - content ID + date + platform - views/watch metric - click metric - downstream metric (lead/signup/sale/GMV) If real data is missing, run **simulation mode** and label outputs clearly as demo/synthetic. ### 2) Map funnel stage Classify each content item as: - awareness - consideration - conversion ### 3) Compute lightweight impact score Use transparent weighted components: - engagement quality - click intent - conversion signal Explain formula and normalization assumptions. ### 4) Output action decisions Return: - ranked performers - pause/optimize list - next 3 content bets with rationale - confidence note (sample size/data quality) ## Quality rules - Keep model explainable and auditable. - Avoid fake precision on tiny samples. - Do not overclaim causality; treat as directional evidence. ## License Copyright (c) 2026 **Razestar**. This skill is provided under **CC BY-NC-SA 4.0** for non-commercial use. You may reuse and adapt it with attribution to Razestar, and share derivatives under the same license. Commercial use requires a separate paid commercial license from **Razestar**. No trademark rights are granted.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: runkecheng/monthly-report-analysis (version 1.0.0)

--- name: monthly-report-analysis description: 收钱吧月度财务报表分析，覆盖海南收钱吧科技有限公司和苏州喔噻科技有限公司两个主体。当用户提到海南科技X月报表分析、苏州喔噻X月报表分析、每月报表分析、月度分析、报表分析等时触发。核心工作：从科目余额表或序时账提取数据，编制三大报表，更新飞书分析模板，出具分析总结。 --- # 月度报表分析 收钱吧财务报表分析，每月由管文（财务同事）发起，涉及两个主体。 ## 工作流程 ### 0. 确定分析主体 根据用户输入判断： | 关键词 | 主体 | 特征 | |:------|:----|:-----| | 海南科技 / 海南收钱吧 | 海南收钱吧科技有限公司 | **合并口径**，收入含积分商城+集团关联方费用 | | 苏州喔噻 | 苏州喔噻科技有限公司 | **单体公司**，全部收入为关联方人员支持费，无营业成本 | ### 1. 获取数据 | 主体 | 数据来源 | |:----|:---------| | 海南科技 | 管文上传的**序时账和现金流量表.xls**（飞书云文档，每月新增Sheet） | | 苏州喔噻 | **科目余额表**（权威）+ **序时账**（辅助校验） | ### 2. 提取并验证数据 - 从序时账解析当月所有科目的借/贷方发生额，借方合计 = 贷方合计（试算平衡） - 科目余额表数值与序时账交叉验证 - 上月期末余额 + 当月净变动 = 本月期末余额 ### 3. 编制三大报表 分三步依次完成，每步write前先read确认行布局： #### 利润表 - 收入取**贷方**发生额，费用取**借方**发生额 - 投资收益取**贷方**（收入方，不取借方的结转分录） - 列布局：C本月 → D上月 → F环比% → G同比 → H同比额 → I同比% → J累计 → K同期累计 → L累计同比% → M分析文本 - E列、G列、O列为**公式列**，绝不写入 #### 资产负债表 - 各科目期末余额 = 上月期末余额 + （当月借方 - 当月贷方）【资产类】或 +（当月贷方 - 当月借方）【负债权益类】 - 资产总计 = 负债合计 + 所有者权益合计（必须平衡） - 列布局：C本月末 → D上月末 → F去年同期 → H分析文本（资产侧）→ K本月→ L上月 → N同期 → P分析文本（负债侧） #### 现金流量表 - 直接使用现金流量表Sheet中数据 ### 4. 更新飞书模板 #### ⚠️ 四铁律（严重） 1. **只写单个数据列**，如 `C6:C45`。绝不写range跨公式列（如 `C6:H18`） 2. **绝不碰公式列**：E/G/M/O列 3. **写前先read**确认当前行布局 4. **改完清理底部**：K50:P150 写 null #### 数据列 vs 公式列 | 区域 | 数据列（可写入） | 公式列（绝不碰） | |:---:|:---------------:|:---------------:| | 资产负债表资产侧 | C, D, F, H | E（环比率%）, G（同比率%） | | 资产负债表负债侧 | K, L, N, P | O（同比率%） | | 利润表 | C, D, F, H, I, J, K, L, M | E, G | | 现金流量表 | C, D, F, H, ... | E, G | ### 5. 出具分析总结 ## 分析文本模板 ### 利润表分析文本格式 ``` **{主体}2026年X月营业收入XX万元，营业成本XX万元，销售费用XX万元，净利润XX万元，环比增加XX万元（XX%），同比减少XX万元（XX%）** **收入、费用较上月变动[较大/较小]，净利润变动主要受以下因素驱动：** - **营业收入**：较上月增减XX万元，变动原因说明 - **营业成本**：较上月增减XX万元（海南科技：积分商城出库成本；苏州喔噻：无营业成本） - **销售费用**：较上月增减XX万元，主要为工资变动说明 - **管理费用**：较上月增减XX万元，原因说明 - **财务费用**：较上月XX万元（一般为银行手续费） - **其他收益/投资收益**：说明来源及变动 - **特殊项目**：所得税/营业外收支等 **资产负债表主要变动项目：** - 挑环比/同比变动较大的前3-5个科目说明（含金额、变动率） **现金流量表主要变动项目：** - 经营/投资/筹资主要项目同环比分析 ``` ### 分析文本编写要求 1. **同环比数据嵌入分析文本**中，不单独列出 2. 数据精确到万元，保留1位小数 3. 不输出思考过程，直接给结论 ## 参考文档 操作此技能时，根据主体读取对应参考文档： | 主体 | 参考文档 | |:----|:---------| | 海南收钱吧科技 | `memory/海南科技月度报表分析流程.md`（详细科目映射+模板规则） | | 苏州喔噻科技 | `memory/苏州喔噻月度报表分析流程.md`（详细科目+操作规则） |


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: clarityprotocol/clarity-literature (version 1.0.0)

--- name: clarity-literature description: > Search research papers and get publication details from Clarity Protocol. Use when the user asks to search research papers, find publications, PubMed references, literature about protein, or citation details. Capabilities: search papers, get paper details by PMID. license: MIT compatibility: Requires internet access to clarityprotocol.io. Optional CLARITY_API_KEY env var for 100 req/min (vs 10 req/min). metadata: author: clarity-protocol version: "1.0.0" homepage: https://clarityprotocol.io --- # Clarity Literature Skill Search and retrieve research papers from Clarity Protocol's curated literature database, sourced from PubMed and enriched with citation metrics from Semantic Scholar. ## Quick Start List all papers in the database: ```bash python scripts/search_papers.py ``` Get details for a specific paper by PMID: ```bash python scripts/get_paper.py --pmid 12345678 ``` Get paper details in readable format: ```bash python scripts/get_paper.py --pmid 12345678 --format summary ``` ## Paper Fields Each paper includes: - `pmid`: PubMed identifier - `doi`: Digital Object Identifier - `title`: Paper title - `first_author`: First author name - `publication_year`: Year published - `journal`: Journal name - `abstract`: Paper abstract (when available) - `citation_count`: Number of citations (from Semantic Scholar) - `influential_citations`: Number of highly influential citations - `has_fulltext`: Whether full text is available in PubMed Central ## Rate Limits - **Anonymous (no API key)**: 10 requests/minute - **With API key**: 100 requests/minute To use an API key, set the `CLARITY_API_KEY` environment variable: ```bash export CLARITY_API_KEY=your_key_here python scripts/search_papers.py ``` Get your API key at https://clarityprotocol.io ## Error Handling **404 Not Found**: The paper with the specified PMID does not exist in the database. **429 Rate Limit**: You've exceeded the rate limit. The script will display how long to wait. **500 Server Error**: The API server encountered an error. Try again later. **Timeout**: The request took longer than 30 seconds. ## Pagination Paper lists are paginated. The API returns a `next_cursor` field if more results are available. ## Use Cases - Find research papers related to protein variants - Get citation metrics for a specific paper - Check if a paper has full text available - Extract abstracts for literature reviews - Build bibliographies for protein research


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: anmu14641/gold-price-checker (version 1.0.0)

--- AIGC: ContentProducer: Minimax Agent AI ContentPropagator: Minimax Agent AI Label: AIGC ProduceID: 9bcfdfd49c107372a83e26df34fe442b PropagateID: 9bcfdfd49c107372a83e26df34fe442b ReservedCode1: 3046022100dea4dce72590e3fc45773029886c78fb96047c37dfb537f9625cff8646eb0ca60221009ff704d6fc8765d1907c8627e85499d70ff9adad91486d252aa3fa321dda7ac6 ReservedCode2: 3046022100f641fd1c94f94e45aaf49e31b944f12aa824fd5839b55101faeb47a31d4eb04402210083ae3ca3a5935569ac50513f276d1f6014ffa7104c402f1fc60285af83456c63 description: 国内金价查询工具。用于查询黄金实时价格、银行金条价格、金店零售价、国际金价和黄金未来趋势分析。当用户询问金价、黄金价格、今日金价、金店价格、银行金条、国际金价、伦敦金、黄金走势或未来趋势时触发此技能。 name: gold-price-checker --- # 金价查询工具 ## 数据来源 使用 `extract_content_from_websites` 工具从 **金价网 (jinjia.com.cn)** 获取数据。 ## 功能范围 1. **国内金价**：上海黄金交易所行情（AU9999、AU9995 等） 2. **国际金价**：伦敦金、伦敦银实时行情 3. **银行金条**：各大银行投资金条价格 4. **金店零售价**：周大福、周生生、老凤祥等品牌金店 5. **黄金回收价**：当日黄金回收参考价格 ## 使用方式 ### 查询实时金价 使用 `extract_content_from_websites` 提取 jinjia.com.cn 的黄金价格数据。 ```python # 提取黄金价格数据 extract_content_from_websites([{ "url": "https://www.jinjia.com.cn/", "prompt": "提取所有黄金价格数据，包括国内金价、国际金价、金店价格、银行金条价格" }]) ``` ### 数据解读 提取后，按以下格式整理输出： ``` 📈 今日金价（人民币/克） - AU9999: ¥1149.00 (+0.85%) - 伦敦金: $5171.06 (+1.74%) 🏦 银行金条价格 - 农行传世之宝金条: ¥805.27/克 - 浦发银行投资金条: ¥xxx 🏪 金店零售价（饰品金） - 周大福: ¥1590/克 - 周生生: ¥1595/克 - 老凤祥: ¥1570/克 ``` ### 未来趋势分析 金价趋势分析要点： - 关注国际金价走势（伦敦金） - 参考 AU9999 收盘价变化 - 结合美元指数、避险情绪等因素 - 参考专业机构分析（可搜索相关新闻） 趋势查询示例： ```python # 搜索金价趋势分析 batch_web_search([{ "query": "黄金价格走势预测 2025 2026" }]) ``` ## 注意事项 - 数据仅供参考，投资需谨慎 - 金店价格通常含工费，需以实际为准 - 银行金条价格可能有买卖价差


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mackjosn/midea-nomark (version 1.0.2)

--- name: parse-video description: "视频去水印解析 Skill。支持 20+ 平台短视频去水印解析，包括抖音、快手、小红书、微博、西瓜视频、豆包、云雀、B站等。使用本技能时触发：解析视频、去水印、视频解析、解析链接、下载视频、去除水印、parse video、video parser、抖音解析、快手解析、小红书解析、bilibili 解析、douyin kuaishou redbook weibo xigua doubao yunque bilibili" --- # parse-video Skill ## 简介 跨平台视频去水印解析工具，支持 20+ 主流短视频和社交媒体平台。 ### 支持平台 | 平台 | 域名 | 支持类型 | |------|------|----------| | 抖音 | v.douyin.com, www.iesdouyin.com | 视频/图集 | | 快手 | v.kuaishou.com | 视频 | | 小红书 | xhslink.com, www.xiaohongshu.com | 视频/图集/LivePhoto | | 微博 | weibo.com, weibo.cn | 视频/图集 | | 西瓜视频 | v.ixigua.com | 视频 | | 哔哩哔哩 | bilibili.com, b23.tv | 视频 | | 豆包 | www.doubao.com | 视频/图片 | | 云雀 | xiaoyunque.jianying.com | 视频 | | 更多... | ... | ... | --- ## 工作流程 ### 方法一：一键解析（推荐） 使用 `scripts/parse.sh` 脚本，自动识别平台并解析： ```bash # 解析任意视频分享链接 bash scripts/parse.sh "https://v.douyin.com/xxx" # 解析豆包视频 bash scripts/parse.sh "https://www.doubao.com/video-sharing?share_id=xxx&video_id=xxx" # 解析 B 站视频 bash scripts/parse.sh "https://b23.tv/xxx" ``` ### 方法二：启动 HTTP 服务 ```bash # 启动服务（默认端口 8080） bash scripts/serve.sh # 指定端口 bash scripts/serve.sh 9090 # 服务启动后可访问 http://localhost:8080 查看 Web UI ``` ### 方法三：直接使用 CLI ```bash # 解析分享链接 ./assets/parse-video-darwin-arm64 parse "https://v.douyin.com/xxx" # 按视频 ID 解析 ./assets/parse-video-darwin-arm64 id douyin 7424432820954598707 # 启动服务 ./assets/parse-video-darwin-arm64 serve -p 8080 ``` --- ## 输出格式 解析成功后返回： - **标题**: 视频描述/标题 - **作者**: 作者昵称和头像 - **视频地址**: 无水印的直接播放链接 - **封面地址**: 视频封面图 - **图片数量**: 图集图片数量（如有） --- ## 注意事项 1. 解析结果为临时链接，部分平台链接有时效性，建议及时下载 2. 仅供个人学习研究使用，请勿用于商业用途 3. 部分平台可能因接口调整而失效，我会定期更新 --- ## 技术细节 - **二进制位置**: `assets/parse-video-<os>-<arch>` - **适用系统**: macOS (arm64/amd64), Windows (amd64), Linux - **二进制来源**: 本地编译，无第三方依赖


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: 52yuanchangxing/screenshot-ux-auditor (version 1.0.0)

--- name: screenshot-ux-auditor description: Turn app screenshots into structured UX, copywriting, and conversion audits with issue severity and recommended fixes. version: 1.1.0 metadata: openclaw: requires: bins: - python3 emoji: 🧰 --- # Screenshot UX Auditor ## Purpose Turn app screenshots into structured UX, copywriting, and conversion audits with issue severity and recommended fixes. ## Trigger phrases - 审查这个界面 - 截图做 UX 评审 - audit this UI screenshot - 页面哪里有问题 - 帮我做可用性检查 ## Ask for these inputs - one or more screenshots - product goal or funnel stage - target user - platform web/mobile/desktop - tone or benchmark ## Workflow 1. Identify the screen type, primary task, and likely user goal. 2. Evaluate using the bundled heuristics checklist. 3. Cluster issues into clarity, hierarchy, trust, interaction, and accessibility. 4. Assign severity and provide fix-first recommendations. 5. Generate an issue log the user can hand to design or engineering. ## Output contract - audit summary - issue log CSV/Markdown - fix priority list - before/after prompt suggestions ## Files in this skill - Script: `{baseDir}/scripts/generate_issue_log.py` - Resource: `{baseDir}/resources/heuristics-checklist.md` ## Operating rules - Be concrete and action-oriented. - Prefer preview / draft / simulation mode before destructive changes. - If information is missing, ask only for the minimum needed to proceed. - Never fabricate metrics, legal certainty, receipts, credentials, or evidence. - Keep assumptions explicit. ## Suggested prompts - 审查这个界面 - 截图做 UX 评审 - audit this UI screenshot ## Use of script and resources Use the bundled script when it helps the user produce a structured file, manifest, CSV, or first-pass draft. Use the resource file as the default schema, checklist, or preset when the user does not provide one. ## Boundaries - This skill supports planning, structuring, and first-pass artifacts. - It should not claim that files were modified, messages were sent, or legal/financial decisions were finalized unless the user actually performed those actions. ## Compatibility notes - Directory-based AgentSkills/OpenClaw skill. - Runtime dependency declared through `metadata.openclaw.requires`. - Helper script is local and auditable: `scripts/generate_issue_log.py`. - Bundled resource is local and referenced by the instructions: `resources/heuristics-checklist.md`.

path: CHANGELOG.md
content: # Changelog

## 1.0.0
- Initial release.


path: resources/heuristics-checklist.md
content: # UX Heuristics Checklist

1. Clear primary action
2. Visual hierarchy and scanning order
3. Label clarity and empty-state guidance
4. Error prevention and recovery
5. Trust and credibility signals
6. Accessibility: contrast, tap targets, alt text, focus
7. Mobile ergonomics and reachability
8. Conversion friction and proof elements
9. Consistency with design system
10. Internationalization / localization risks


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: crocketc/ms-qwen-vl (version 0.1.0)

--- name: ms-qwen-vl description: 调用魔搭社区（ModelScope）Qwen3-VL 多模态 API 进行视觉解析。使用 OpenAI SDK 兼容方式调用，支持图片内容描述、OCR 文字提取、视觉问答、对象检测等功能。用户提到"魔搭"、"ModelScope"、"Qwen-VL"、"多模态视觉"、"解析图片"等关键词时应触发。 --- # MS-Qwen-VL Skill 基于 ModelScope Qwen3-VL 系列模型的多模态视觉识别技能，使用 OpenAI SDK 兼容方式调用。 ## 功能特点 - **OpenAI SDK 兼容**：使用标准 OpenAI SDK 调用 API - **多种任务支持**：图像描述、OCR、视觉问答、目标检测、图表解析 - **双模型模式**：默认快速模型（30B）+ 精细高精度模型（235B） - **灵活输入**：支持本地图片和 URL ## 安装与配置 ```bash # 安装依赖 pip install -r requirements.txt # 配置 API Key cp .env.example .env ``` 编辑 `.env` 文件，填入从 https://modelscope.cn/my/myaccesstoken 获取的 API Key： ``` MODELSCOPE_API_KEY=your_api_key_here ``` ## Claude Code 使用方式 ### 重要：处理本地图片 当用户提供本地图片路径时（如桌面截图），**必须使用 Python 脚本处理**： ```bash python scripts/ms_qwen_vl.py "<图片路径>" --task <任务类型> ``` 脚本会自动将本地文件转换为 ModelScope API 需要的 base64 格式。 ### 处理 URL 图片 当用户提供网络 URL 时，同样使用上述命令，脚本会自动识别： ```bash python scripts/ms_qwen_vl.py "<URL>" --task <任务类型> ``` ### Claude Code 对话示例 **场景 1：分析桌面截图** ``` 用户: 请帮我描述这张图片 C:\Users\...\Desktop\screenshot.png 助手: [执行] python scripts/ms_qwen_vl.py "C:\Users\...\Desktop\screenshot.png" ``` **场景 2：OCR 识别本地图片** ``` 用户: 识别这张图中的文字: D:\Documents\invoice.jpg 助手: [执行] python scripts/ms_qwen_vl.py "D:\Documents\invoice.jpg" --task ocr ``` **场景 3：分析网络图片** ``` 用户: 分析这张图片 https://example.com/photo.jpg 助手: [执行] python scripts/ms_qwen_vl.py "https://example.com/photo.jpg" --task describe ``` **场景 4：视觉问答** ``` 用户: 这张图里有几个人？C:\Users\...\Desktop\photo.png 助手: [执行] python scripts/ms_qwen_vl.py "C:\Users\...\Desktop\photo.png" --task ask --question "图片里有几个人？" ``` ### 任务类型对照 | 用户需求 | --task 参数 | |---------|-------------| | 描述图片内容 | describe | | 识别文字/OCR | ocr | | 回答关于图片的问题 | ask（需要 --question） | | 检测物体 | detect | | 解析图表 | chart | ## 快速使用 ```bash # 图像描述（默认） python scripts/ms_qwen_vl.py image.jpg # OCR 文字识别 python scripts/ms_qwen_vl.py image.jpg --task ocr # 视觉问答 python scripts/ms_qwen_vl.py image.jpg --task ask --question "图片里有什么？" # 使用精细模式（235B 模型） python scripts/ms_qwen_vl.py image.jpg --task describe --precise ``` Python 代码调用： ```python from scripts.ms_qwen_vl import analyze_image result = analyze_image("image.jpg", task="ocr") print(result) ``` ## 任务类型 | 任务 | 参数 | 说明 | |------|------|------| | 图像描述 | `describe` | 详细描述图片内容（默认） | | OCR 识别 | `ocr` | 识别图片中的文字 | | 视觉问答 | `ask` | 回答关于图片的问题 | | 目标检测 | `detect` | 检测图片中的物体 | | 图表解析 | `chart` | 解析图表数据 | ## 环境变量 | 变量名 | 说明 | |--------|------| | `MODELSCOPE_API_KEY` | API 密钥（必需） | | `MODELSCOPE_MODEL` | 默认模型（可选） | | `MODELSCOPE_MODEL_PRECISE` | 精细模式模型（可选） | ## Resources ### scripts/ **ms_qwen_vl.py** - 核心解析脚本，提供 `analyze_image()` 统一接口 ### references/ **api-guide.md** - OpenAI SDK 兼容调用方式详细说明 **models.md** - Qwen3-VL 系列模型及推荐使用场景


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