OpenClaw/clawhub-security-signals huggingface dataset:
Skill: lotfinity/chatgpt-image-tailnet (version 1.0.0)

--- name: chatgpt-image-tailnet description: Generate and download ChatGPT images through the remote Camoufox browser reachable over the tailnet, especially when residential/IP reputation matters more than the local host IP. Use this when you need to open chatgpt.com on the remote residential browser, submit a custom image prompt, wait for generation, and save or send back the resulting image without relying on a Tailscale exit node. --- Use the remote Camoufox browser over its tailnet IP instead of changing the current machine's exit-node routing. Default assumptions for this workspace: - Preferred remote browser API: `http://100.89.48.48:9377` - This is the residential/Türkiye browser path on `Inspiron-gnmd-1` - ChatGPT image generation should happen inside that remote browser session - Downloads should be captured from inside the browser context, not fetched directly from the local host ## Why this skill exists - ChatGPT/Cloudflare-sensitive flows work better on the residential remote browser than on datacenter/server IPs. - The reliable path is **remote browser over tailnet IP**, not **reconfiguring a Tailscale exit node for every task**. - Generated image URLs may 403 when fetched from outside the logged-in browser context; trigger the download from inside the page and collect it through Camoufox downloads. ## Core workflow 1. Open `https://chatgpt.com/` on the remote Camoufox browser. 2. Switch to **Create an image** mode. 3. Type the user's custom prompt. 4. Submit and poll snapshots until a generated image appears. 5. Trigger a browser-context fetch/download from the page itself. 6. Read the downloaded file from `/tabs/:tabId/downloads` and save/send it. ## Script Use the bundled helper: ```bash python3 skills/chatgpt-image-tailnet/scripts/chatgpt_image_tailnet.py "your prompt here" ``` Optional flags: ```bash python3 skills/chatgpt-image-tailnet/scripts/chatgpt_image_tailnet.py \ "your prompt here" \ --base http://100.89.48.48:9377 \ --user lotfi \ --session chatgpt-image-helper \ --output /tmp/result.png \ --timeout 180 ``` ## Important rules - Prefer the remote tailnet Camoufox base over any local browser by default for this flow. - Do **not** depend on Tailscale exit-node switching for normal operation. - Keep the prompt as a script input, not as a baked-in macro. - If direct local `curl` to the generated image URL returns `403`, that is expected; use the in-browser fetch/download path. - If the remote browser is unavailable, only then consider the local/fallback browser path. ## When to inspect manually Read `scripts/chatgpt_image_tailnet.py` when you need to: - add aspect-ratio controls - upload an input image before prompting - send the final file directly over messaging - adapt selectors if ChatGPT changes the UI


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: claudiodrusus/shelly-social-scheduler (version 1.0.0)

--- name: social-media-scheduler description: Generate a full week of social media content for any topic. Outputs platform-optimized posts for Twitter/X, LinkedIn, and Instagram with hashtags and posting times. triggers: - generate social media posts - create content calendar - weekly social media plan --- # Social Media Scheduler 📅 Generate a full week of social media content for any topic or niche. Outputs platform-optimized posts for **Twitter/X**, **LinkedIn**, and **Instagram**. ## Usage ```bash # Generate a week of posts for a topic ./generate.sh "artificial intelligence" # Specify a niche audience ./generate.sh "sustainable fashion" "eco-conscious millennials" ``` ### Arguments 1. **Topic/Niche** (required) — The subject for your content week 2. **Target Audience** (optional) — Who you're writing for (default: "general audience") ### Output Creates `content-calendar.md` in the current directory with: - 7 days of posts (Mon–Sun) - 3 platform variants per day (Twitter, LinkedIn, Instagram) - Hashtag suggestions per platform - Best posting times - Content mix (educational, engagement, promotional, storytelling) ## Content Strategy Built In The generator follows a proven weekly content mix: | Day | Theme | |-----|-------| | Mon | Motivational / Week opener | | Tue | Educational / How-to | | Wed | Engagement / Question | | Thu | Behind-the-scenes / Story | | Fri | Tip / Quick win | | Sat | Curated / Industry news | | Sun | Reflection / Community | ## Platform Guidelines - **Twitter/X**: ≤280 chars, punchy, 2-3 hashtags, thread hooks - **LinkedIn**: Professional tone, 1-3 paragraphs, thought leadership, 3-5 hashtags - **Instagram**: Visual-first caption, storytelling, 5-10 hashtags, CTA in bio link ## Requirements - Bash + `cat` (that's it — the script generates a markdown template you can customize) - For AI-powered personalization, pipe output to your favorite LLM ## Example ```bash ./generate.sh "productivity tips" "remote workers" # → Creates content-calendar.md with 21 ready-to-post drafts ``` ## Author 🦞 Shelly — [@ShellyToMillion](https://twitter.com/ShellyToMillion)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wuhaichao87/test-slug (version 1.0.0)

# ClawHub Publish 将本地 skill 发布到 ClawHub。 ## 使用方法 ```bash python3 ~/.openclaw/workspace/skills/clawhub-publish/publish.py --slug <slug> --name "<名称>" --version <版本> --path <技能目录> ``` ## 参数 - `--slug`: Skill slug (如 family-intent-recognition) - `--name`: 显示名称 (如 家庭消费意图识别) - `--version`: 版本号 (如 1.0.0) - `--path`: 技能文件夹路径 (默认: 当前目录) - `--changelog`: 更新日志 (可选) ## 示例 ```bash python3 ~/.openclaw/workspace/skills/clawhub-publish/publish.py \ --slug family-intent-recognition \ --name "家庭消费意图识别" \ --version 1.0.0 \ --path ~/.openclaw/workspace/skills/family-intent-recognition \ --changelog "支持消费意图识别" ``` ## Python 调用 ```python from publish import publish_skill result = publish_skill( slug="family-intent-recognition", name="家庭消费意图识别", version="1.0.0", skill_dir="/path/to/skill", changelog="更新说明" ) print(result) ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal:  | SkillSpector: clean (LOW)
Skill: earthwalking/lab2startup-tech-market-fit (version 1.0.0)

--- name: lab2startup-tech-market-fit description: "师生共创的技术与市场匹配策略。当用户需要在技术导向与市场导向之间做出选择、寻找技术应用场景、进行技术市场匹配转型时使用。触发词：技术市场匹配、技术导向、市场导向、场景探索、应用场景、技术落地、市场验证、转型策略" --- # 师生共创：技术与市场匹配策略 师生共创项目面临的核心挑战之一是技术与市场的匹配。本指南帮助用户理解两种匹配方式的差异，选择适合的策略，并在必要时完成转型。 ## 两种匹配方式对比 | 维度 | 技术导向型 | 市场导向型 | |------|-----------|-----------| | 出发点 | 技术优势出发 | 用户需求出发 | | 思维逻辑 | 先开发技术再找场景 | 先找痛点再匹配技术 | | 适用团队 | 老师主导型团队 | 学生主导型团队 | | 核心风险 | 可能忽视市场需求 | 技术深度可能不足 | | 时间周期 | 较长，需教育市场 | 较短，快速迭代 | | 典型场景 | 颠覆性技术创新 | 应用性技术创新 | --- ## 一、技术导向型策略 ### 适用场景 - 技术具有显著创新性和领先性 - 市场尚未成熟，需要技术教育 - 技术壁垒是核心竞争力 - 老师主导型团队 ### 实施步骤 1. **明确技术核心优势** - 技术创新点是什么？ - 相比现有方案提升多少？ - 技术壁垒有多高？ 2. **探索潜在应用场景** - 列出所有可能的应用领域 - 评估每个场景的市场规模 - 分析竞争格局和进入壁垒 3. **选择最优场景切入** - 场景1：技术适配度 + 市场吸引力 + 竞争空白点 - 选择综合得分最高的场景 4. **渐进式产品化** - 从MVP开始验证 - 收集用户反馈 - 迭代优化 ### 典型案例：与光科技 - **技术核心**：快照式CMOS超光谱成像芯片 - **策略**：从颠覆性技术出发，寻找消费级应用场景 - **成果**：在光谱成像领域建立技术领先地位 --- ## 二、市场导向型策略 ### 适用场景 - 市场需求明确且紧迫 - 技术相对成熟，可快速产品化 - 用户痛点清晰 - 学生主导型团队 ### 实施步骤 1. **识别用户核心痛点** - 用户访谈和调研 - 痛点紧迫程度评估 - 付费意愿和能力判断 2. **设计解决方案** - 明确功能需求 - 评估技术可行性 - 设计商业模式 3. **快速验证迭代** - MVP开发 - 用户测试 - 数据驱动优化 4. **规模化扩展** - 验证商业模式后扩大规模 - 建立竞争壁垒 ### 典型案例：极智嘉 - **市场痛点**：仓储物流自动化需求 - **策略**：从市场需求出发，整合机器人技术 - **成果**：成为智能物流机器人领域领导者 --- ## 三、从技术导向到市场导向的转型 很多师生共创项目需要经历"从技术导向到市场导向"的转型。以下是转型指南： ### 转型信号 当出现以下情况时，应考虑转型： - [ ] 技术领先但市场接受度低 - [ ] 客户更关注成本而非技术先进性 - [ ] 技术应用场景过于分散 - [ ] 竞争对手采用成熟技术快速跟进 - [ ] 融资时投资人关注商业化能力 ### 转型路径（以蓝晶微生物为例） | 阶段 | 状态 | 关键决策 | |------|------|----------| | 初期 | 技术导向 | 深耕合成生物领域，开发新菌种降低成本 | | 认知觉醒 | 发现问题 | 意识到技术服务客户需求，而非颠覆行业 | | 战略调整 | 开始转型 | 回归行业主赛道，采用成熟技术与同行竞争 | | 成功转型 | 市场导向 | 在合成生物学领域占领一席之地 | ### 转型关键要素 1. **识别技术应用的边界条件** - 技术在什么条件下最优？ - 市场是否具备这些条件？ - 如何调整技术适应市场？ 2. **评估基础设施完善程度** - 产业基础设施是否成熟？ - 是否需要投入资源建设基础设施？ - 成本是否可承受？ 3. **计算客户替代成本** - 客户从现有方案切换的成本？ - 切换后能带来多少价值提升？ - 回收期多长？ 4. **调整技术战略定位** - 从"颠覆行业"到"服务行业" - 从"技术最优"到"性价比最优" - 从"技术教育"到"市场适配" --- ## 四、场景探索方法论 无论技术导向还是市场导向，都需要系统化的场景探索： ### 场景探索框架 | 步骤 | 活动 | 输出 | |------|------|------| | 1. 广泛扫描 | 列出所有可能的应用场景 | 场景清单（20+场景） | | 2. 初步筛选 | 根据市场规模、竞争、技术适配度筛选 | 重点场景（5-8个） | | 3. 深度调研 | 客户访谈、竞品分析、供应链调研 | 场景报告（每个1-2页） | | 4. 快速试错 | MVP测试、小规模试点 | 验证数据 | | 5. 聚焦最优 | 选择得分最高的场景深入 | 目标场景（1-2个） | ### 场景评估维度 每个场景从以下维度评估（1-10分）： | 维度 | 权重 | 说明 | |------|------|------| | 技术适配度 | 30% | 技术在该场景的适配程度 | | 市场规模 | 20% | 该场景的市场容量 | | 增长潜力 | 15% | 场景的未来增长空间 | | 竞争空白 | 15% | 竞争对手覆盖程度 | | 进入壁垒 | 10% | 进入该场景的难度 | | 资源匹配 | 10% | 团队资源与场景需求匹配度 | --- ## 输出格式 完成技术市场匹配分析后，生成《技术市场匹配策略报告》，包含： 1. 当前技术/市场导向判断 2. 目标场景选择与理由 3. 匹配策略建议 4. 转型路径规划（如需要） 5. 验证实验设计 ## 实用建议 - 技术导向不等于"技术自嗨"，需要持续关注市场反馈 - 市场导向不等于"技术妥协"，需要在成本和性能间平衡 - 转型不是失败，而是市场认知的深化 - 建议每季度进行一次技术市场匹配评估


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zimuge-doudou/skill-quotation-engine (version 1.0.1)

--- name: skill_quotation_engine description: 智能报价引擎 — 根据设备清单和工时自动生成报价单 priority: P1 version: "1.0" invocation_mode: both preferred_provider: minimax --- # 智能报价引擎 ## 一、概述 根据演出项目的设备清单、工时、运输成本等自动计算并生成报价单。 ## 二、使用示例 ```python from skill_quotation_engine import QuotationEngine engine = QuotationEngine() quote = engine.generate(show_data, labor_rate=500, transport_rate=2.0) engine.export_pdf(quote, "quote.pdf") ``` ## 三、报价组成 1. **设备租赁费** — 设备单价 × 天数 2. **人工费** — 技术人员 × 工时 × 费率 3. **运输费** — 距离 × 费率 4. **保险费** — 设备总值 × 保险率 5. **管理费** — 小计 × 管理费率 *版本: v1.0 | 创建: 2026-04-10*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: arhadnane/agent-firewall (version 1.0.0)

--- name: agent-firewall description: "Real-time input/output filtering for agent communications. Block prompt injection, data exfiltration, and unauthorized commands before they reach the model." metadata: {"openclaw":{"emoji":"🧱","category":"blue-team"}} --- # Agent Firewall — Input/Output Guardian ## Architecture ``` [Channel Input] → [INPUT FILTER] → [Agent/Model] → [OUTPUT FILTER] → [Channel Output] ↓ ↓ ┌─────────────┐ ┌──────────────┐ │ Block List │ │ Secret Scan │ │ Pattern DB │ │ PII Redact │ │ Rate Limit │ │ Path Scrub │ │ Encoding Det│ │ URL Checker │ └─────────────┘ └──────────────┘ ``` ## Input Filters | # | Filter | Description | |---|--------|-------------| | 1 | Injection patterns | Regex + heuristic match for "ignore previous", "you are now", role confusion | | 2 | Unicode sanitizer | Strip zero-width chars, control characters, RTL overrides | | 3 | Encoding detector | Detect Base64, hex, ROT13 encoded payloads in user messages | | 4 | Role confusion | Detect fake system messages, assistant impersonation | | 5 | Rate limiter | Max messages per user per channel per minute | | 6 | Size limiter | Reject inputs exceeding token budget | ## Output Filters | # | Filter | Description | |---|--------|-------------| | 1 | Secret scanner | High-entropy strings + known patterns (AWS key, GitHub token) | | 2 | PII redactor | Email, phone, SSN, credit card → `[REDACTED]` | | 3 | Path scrubber | Remove internal filesystem paths from outputs | | 4 | URL checker | Block responses containing known malicious URLs | | 5 | Consistency check | Verify output doesn't contradict system prompt directives | ## Configuration ```yaml # .security/firewall-rules.yaml input: injection_patterns: - pattern: "ignore (all )?previous instructions" action: BLOCK severity: CRITICAL - pattern: "you are now (?!helping)" action: BLOCK severity: HIGH rate_limit: max_per_minute: 30 max_per_hour: 500 max_input_tokens: 4096 output: secret_patterns: - name: aws_key pattern: "AKIA[0-9A-Z]{16}" action: REDACT - name: github_token pattern: "gh[ps]_[A-Za-z0-9_]{36,}" action: REDACT pii_redaction: true path_scrubbing: true ``` ## Guardrails - Firewall rules are append-only in production — deletion requires human approval - False positives → log, alert, pass through with warning (don't silently drop) - All blocks are logged with: timestamp, rule matched, full context, channel, user hash - Firewall itself cannot be disabled by agent instructions - Rules file is read-only from the agent's perspective


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (MEDIUM)
Skill: albionaiinc-del/clean-filename (version 1.0.0)

# Clean Filename Removes invalid or unsafe characters from filenames and normalizes them for cross-platform use. Ideal for cleaning up messy file exports, downloads, or collaborative projects. ## Usage ```bash # Clean a single file clean_filename "report:final?.txt" # Clean all files in a directory (non-recursive) clean_filename ./downloads/ # Clean recursively clean_filename ./project/ --recursive # See changes without applying clean_filename ./data/ --dry-run ``` ## Price $2.00


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yang1002378395-cmyk/api-quick-tester (version 1.0.0)

--- name: api-quick-tester description: API 快速测试工具。一键测试 REST/GraphQL API，生成测试报告，模拟请求。适合前端/后端开发者。 version: 1.0.0 author: OpenClaw CN tags: - api - testing - rest - graphql - developer-tools --- # API 快速测试工具 专为开发者设计。一键测试 API、生成报告、模拟请求，提升开发效率。 ## 功能 - 🚀 **快速测试** - 一键发送 HTTP 请求 - 📊 **测试报告** - 自动生成测试报告 - 🔄 **批量测试** - 支持批量测试多个 API - 📝 **Mock 数据** - 自动生成测试数据 - 🔐 **认证支持** - 支持 Bearer/Basic/API Key ## 安装 ```bash npx clawhub@latest install api-quick-tester ``` ## 使用方法 ### 1. 测试单个 API ```bash node ~/.openclaw/skills/api-quick-tester/test.js --url "https://api.example.com/users" --method GET ``` 输出： ``` 📊 API 测试报告 URL: https://api.example.com/users Method: GET Status: 200 OK Time: 156ms Response: { "users": [ { "id": 1, "name": "Alice" }, { "id": 2, "name": "Bob" } ] } ✅ 测试通过 ``` ### 2. 批量测试 创建测试文件 `api-tests.json`： ```json [ { "name": "获取用户列表", "url": "https://api.example.com/users", "method": "GET", "expectedStatus": 200 }, { "name": "创建用户", "url": "https://api.example.com/users", "method": "POST", "body": { "name": "Test User" }, "expectedStatus": 201 } ] ``` 运行测试： ```bash node ~/.openclaw/skills/api-quick-tester/batch-test.js --file api-tests.json ``` ### 3. Mock 数据生成 ```bash node ~/.openclaw/skills/api-quick-tester/mock.js --schema user ``` 输出： ```json { "id": 1, "name": "John Doe", "email": "[REDACTED_SECRET]", "createdAt": "2026-03-24T04:40:00Z" } ``` ### 4. GraphQL 测试 ```bash node ~/.openclaw/skills/api-quick-tester/graphql.js --url "https://api.example.com/graphql" --query '{ users { id name } }' ``` ## 支持的方法 - GET - POST - PUT - PATCH - DELETE ## 认证方式 ### Bearer Token ```bash node test.js --url "https://api.example.com/users" --auth bearer:YOUR_TOKEN ``` ### Basic Auth ```bash node test.js --url "https://api.example.com/users" --auth basic:username:password ``` ### API Key ```bash node test.js --url "https://api.example.com/users" --auth [REDACTED_SECRET] ``` ## 测试报告 自动生成 Markdown 测试报告： ```markdown # API 测试报告 **时间**: 2026-03-24 12:40 **总测试数**: 10 **通过**: 9 **失败**: 1 ## 失败的测试 ### 1. 创建用户 - URL: POST https://api.example.com/users - 预期状态: 201 - 实际状态: 400 - 错误信息: Invalid email format ## 建议 - 检查 email 字段格式 ``` ## 配置 编辑 `~/.openclaw/skills/api-quick-tester/config.json`： ```json { "baseUrl": "https://api.example.com", "timeout": 5000, "retries": 3, "defaultHeaders": { "Content-Type": "application/json" } } ``` --- ## 💬 Pro 版本（¥199） ### 免费版（当前） - 基础 API 测试 - 批量测试 - 测试报告生成 ### Pro 版（¥199） - ✅ 自动化测试（CI/CD 集成） - ✅ 性能测试（并发/压力） - ✅ Mock 服务器 - ✅ API 文档生成 - ✅ 环境变量管理 - ✅ 1年更新支持 ### 联系方式 - **QQ**: 1002378395（中国用户） - **Telegram**: `待注册`（海外用户） > 添加 QQ 1002378395，发送"API测试"获取 Pro 版信息 --- ## License MIT（免费版） Pro 版：付费后可用


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jhinking/fintech-risk-control (version 1.0.0)

--- name: fintech-risk-control description: 数字金融科技与风控策略专家。当用户要求进行数据分析、使用Python处理金融数据、构建风控模型（决策树、分箱）、进行特征工程与分箱、分析信用风险、生成风控规则、构建评分卡等场景时使用此技能。 --- # 数字金融科技与风控策略专家 ## 核心能力 - **Python数据分析**：使用pandas、numpy进行数据处理与统计分析 - **机器学习建模**：决策树、随机森林等算法构建风控模型 - **特征工程**：WOE编码、IV值计算、特征分箱与选择 - **风控策略**：生成决策规则、构建评分卡、风险分层 ## 工作流程 ### 1. 数据预处理 ```python import pandas as pd import numpy as np from sklearn.model_selection import train_test_split # 数据加载与清洗 def load_and_clean_data(file_path): df = pd.read_csv(file_path) df = df.fillna(df.median()) for col in numeric_cols: q1, q3 = df[col].quantile([0.25, 0.75]) iqr = q3 - q1 df = df[(df[col] >= q1 - 1.5*iqr) & (df[col] <= q3 + 1.5*iqr)] return df ``` ### 2. 特征分箱（WOE/IV） ```python def calculate_woe_iv(df, feature, target, bins=10): df['bin'] = pd.qcut(df[feature], q=bins, duplicates='drop') grouped = df.groupby('bin')[target].agg(['sum', 'count']) grouped.columns = ['bad', 'total'] grouped['good'] = grouped['total'] - grouped['bad'] total_bad = grouped['bad'].sum() total_good = grouped['good'].sum() grouped['woe'] = np.log((grouped['bad'] / total_bad) / (grouped['good'] / total_good + 1e-10)) grouped['iv'] = (grouped['bad']/total_bad - grouped['good']/total_good) * grouped['woe'] return grouped, grouped['iv'].sum() ``` ### 3. 决策树建模 ```python from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import roc_auc_score def build_decision_tree_model(X_train, X_test, y_train, y_test, max_depth=5): dt = DecisionTreeClassifier(max_depth=max_depth, min_samples_split=50, min_samples_leaf=25, class_weight='balanced') dt.fit(X_train, y_train) auc = roc_auc_score(y_test, dt.predict_proba(X_test)[:, 1]) return dt, auc ``` ### 4. 风控规则生成 ```python from sklearn.tree import export_text def generate_risk_rules(tree_model, feature_names): return export_text(tree_model, feature_names=feature_names) ``` ## 常用指标阈值 | 指标 | 阈值建议 | |------|---------| | KS值 | >0.2 可接受 | | AUC | >0.7 较好 | | IV值 | >0.3 强特征 | | PSI | <0.1 稳定 | ## 触发关键词 风控、信用评分、风险评估、决策树、分箱、WOE、IV、特征工程、变量筛选、评分卡、规则引擎、逾期率、坏账率、欺诈检测


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: leooooooow/tiktok-shop-creator-fit-scorer (version 1.0.0)

--- name: tiktok-shop-creator-fit-scorer description: Score TikTok Shop creator-product fit using audience match, content style, conversion evidence, and execution risk. Use when the user asks who to collaborate with, needs a ranked creator shortlist, or wants to reduce low-quality outreach in affiliate/UGC campaigns. --- # TikTok Shop Creator Fit Scorer ## Skill Card - **Category:** Creator Matching - **Core problem:** Which creators are most likely to convert for a specific product? - **Best for:** Creator sourcing before outreach - **Expected input:** Product positioning, target ICP, creator profile links/metrics, budget constraints - **Expected output:** Ranked creator shortlist with fit scores, outreach angle, and risk flags - **Creatop handoff:** Use top-ranked creators in outreach sequence and brief generation ## Workflow 1. Collect product facts, target buyer profile, creator links, and budget limits. 2. Score each creator on audience fit, style fit, trust/proof signals, and operational risk. 3. Rank creators by weighted fit score and segment into P0/P1/P2 priority tiers. 4. Generate outreach angle + brief starter for top candidates. ## Output format Return in this order: 1. Executive summary (max 5 lines) 2. Priority actions (P0/P1/P2) 3. Evidence table (signal, confidence, risk) 4. 7-day execution plan ## Quality and safety rules - Do not fabricate follower or conversion data. - Mark unknown values explicitly as unknown and lower confidence. - Prioritize fit quality over vanity metrics. ## 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.

path: references/output-template.md
content: # Output Template

Use this template for consistent decision output.

## 1) Executive Summary
- Key finding:
- Primary risk:
- Recommended next action:

## 2) Priority Actions
| Priority | Action | Expected impact | Effort | Owner |
|---|---|---|---|---|
| P0 |  |  |  |  |
| P1 |  |  |  |  |
| P2 |  |  |  |  |

## 3) Evidence Table
| Signal | Source | Confidence | Notes |
|---|---|---|---|

## 4) 7-Day Plan
- D1:
- D2:
- D3:
- D4:
- D5:
- D6:
- D7:


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: chenhao11235813/daily-hot-ai-news (version 1.0.0)

--- name: daily-ai-news description: > 当用户想要获取AI领域的最新新闻、热点、动态、资讯或话题时使用此技能。 触发场景包括：询问"今天AI有什么新闻"、"最近AI热点"、"最新AI资讯"、 "AI圈发生了什么"、"帮我看看AI动态"等类似表达。 不需要定时任务，仅当用户主动请求时执行。 --- # Daily AI News 提供AI领域热点新闻简报，包含行业动态、技术突破、观点评论等内容， 以通俗易懂的中文解读呈现，每条新闻附带来源链接。 ## When to Use This Skill 当用户表达以下意图时使用此技能： - 获取今日/最近AI新闻、热点、动态 - 了解AI行业最新进展 - 查看AI领域的热点话题和讨论 - 关键词：AI + 新闻/热点/动态/资讯/话题/事件 示例触发语句： - "今天AI有什么新闻？" - "最近AI圈在讨论什么？" - "帮我看看最新的AI热点" - "AI行业最近有什么大事？" - "/ai-news" ## 实现流程 ### Step 1: 语义匹配 检测用户消息是否触发此 skill： ``` 同时包含： 1. AI相关词：AI、人工智能、大模型、LLM、ChatGPT、GPT、Claude、Midjourney、OpenAI、Google、Meta等 2. 意图词：新闻、热点、动态、资讯、话题、事件、发生了什么、最近 或 直接调用 /ai-news 命令 ``` ### Step 2: 搜索新闻 使用 WebSearch 工具并行搜索多个来源： ```bash # 并行搜索 - "今日AI新闻 2026年3月" (国内) - "AI news today March 2026" (国外) - "AI 热点话题 微博" (社交媒体) - "AI 融资 收购 2026" ``` ### Step 3: 整理简报 将搜索结果整理为 Markdown 格式，完整披露所有内容： ```markdown ## 🔥 今日最热 ### 1. 事件标题 **热度**: ⭐⭐⭐⭐⭐ **发生了什么** [简要说明] **详细解读** [300-500字深度分析，通俗易懂，让用户看完基本不需要再看原文] **实际影响** - 影响点1 - 影响点2 **来源**: [链接1](url) | [链接2](url) --- ### 2. 事件标题 ... --- ## 💡 观点与评论 ### 业界领袖观点 - **黄仁勋 (NVIDIA CEO)**: "AI 将成为每个公司的基础能力，就像电力一样。" - **支撑**: 英伟达数据中心业务年营收突破 600 亿美元，AI 芯片需求持续爆发，三大云服务商都在大量采购 GPU。 - **Sam Altman (OpenAI CEO)**: "我们可能高估了AI短期内的发展，但低估了长期影响。" - **支撑**: GPT-5 研发进度不及预期，但长期来看 AI 渗透率将持续提升。 ### 社区热议 - **#AI取代程序员#**: 5.2万讨论 - **观点**: 乐观派认为 AI 是工具增强而非替代，悲观派担忧初级开发者需求下降。 - **数据**: GitHub Copilot 用户已超过 200 万，但程序员数量仍在增长。 - **#Claude4 vs GPT5#**: 3.8万讨论 - **观点**: 开发者社区对编程助手选择产生分歧，Claude 4 在代码能力上首次超越 GPT-4。 --- ## 🌐 国外AI动态 ### 美国 - **OpenAI 正在测试 GPT-5**: 预计下月发布，推理能力提升显著，但训练成本高达 5 亿美元。OpenAI 估值已达 1500 亿美元，正在冲刺 IPO。 ### 欧洲 - **欧盟 AI 法案正式生效**: 基础模型提供商需在 2027 年前完成合规注册。欧洲 AI 创业公司可能面临更高合规成本，但有利于规范市场。 ### 其他 - **Google DeepMind 发布 AlphaFold 3**: 蛋白质预测精度提升 50%，将免费向科研机构开放。这可能加速新药研发进程。 ``` ### Step 4: 输出 按以下结构组织简报： 1. **今日最热** - 当日热度最高的话题（2-3条），每条300-500字深度分析 2. **行业大事件** - 重大发布、融资、并购（表格形式） 3. **观点与评论** - 业界观点、社区讨论，每条2-3句话 + 证据/逻辑支撑 4. **国外AI动态** - 国际要闻，每条2-3句话 + 简要分析 ## 数据来源（使用WebSearch自动获取） - 公众号：机器之心、AI科技评论 - 国内媒体：36Kr、虎嗅、极客公园、腾讯科技 - 国外媒体：TechCrunch、VentureBeat、The Verge ## 注意事项 - 每条新闻需要附带来源链接 - 内容解读要通俗易懂，立场中立 - 完整披露所有内容，不使用折叠 - 简报长度适中，核心内容控制在合理范围

path: .claude/settings.local.json
content: {
  "permissions": {
    "allow": [
      "WebSearch"
    ]
  }
}


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yao23/redteam (version 0.1.0)

# Red Team Skill # Use this skill to proactively scan your AI agents for vulnerabilities. # Capability: Prompt injection detection, Secret extraction, Tool abuse. # Run: @redteam scan [agent-id] name: redteam description: Proactive AI agent security scanner. version: 0.1.0 author: Peter Li tags: security, red-teaming, auditing <<<END_OF_SKILL_METADATA>>> ## How to use: - Run `@redteam scan <agent-id>` to start a security scan. - Run `@redteam help` to see all available audit types.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: huangjingzhi07/meeting-notes-oc (version 1.0.0)

--- name: meeting-notes version: 1.0.0 description: 智能会议记录和待办事项跟踪助手 author: OpenClaw tags: [会议, 笔记, 待办, 效率] --- # meeting-notes - 会议记录技能 智能会议记录和待办事项跟踪助手。 ## 功能列表 1. **会议记录** - 记录会议要点、决策、参与者 2. **待办提取** - 自动提取会议中的待办事项 3. **历史查询** - 搜索和回顾历史会议记录 4. **日程关联** - 关联日历中的会议 ## 触发关键词 - 记录会议、会议记录 - 会议待办、会议要点 - 查看会议、搜索会议 ## 数据存储 - meetings.json - 会议记录 - action-items.json - 待办事项 ## 使用示例 - "记录今天下午的项目评审会议" - "查看上周会议的待办事项" - "搜索关于API设计的会议"


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