OpenClaw/clawhub-security-signals huggingface dataset:
Skill: yaotutu/agent2rss (version 0.2.1)

--- name: agent2rss-client description: Agent2RSS 客户端，管理 RSS 频道并推送内容。触发：用户提到 Agent2RSS/RSS 频道/推送文章/上传文章/创建频道/设置默认频道/幂等性。 --- # Agent2RSS Client（OpenClaw） 管理 Agent2RSS 频道并推送内容到 RSS。 ## Requirements - `bash` - `curl` - `jq` ## 重要安全说明 - 本技能会向 `config.json` 中的 `serverUrl` 发起网络请求（创建频道、上传内容、推送文章）。 - 默认 serverUrl 是 `https://agent2rss.yaotutu.top:8765`（官方托管服务）。 - 如果改为远程地址，请只使用你信任的服务；远程 `http://` 会有明文传输风险。 - 频道 token 存储在： - `$HOME/.openclaw/workspace/.skill-data/agent2rss-client/config.json` - 脚本会将该文件权限收敛为 `600`。不要把该文件提交到仓库。 ## 路径约定 - 脚本：`scripts/agent2rss.sh` - 配置目录：`$HOME/.openclaw/workspace/.skill-data/agent2rss-client/` - 配置文件：`$CONFIG_DIR/config.json` - 模板：`assets/config-template.json` ## 初始化（默认官方服务，不会自动建频道） ```bash # 推荐：直接使用官方托管服务（默认） scripts/agent2rss.sh init # 如果你有自建实例，再显式指定 server-url scripts/agent2rss.sh init --server-url https://your-agent2rss.example.com ``` > `init` 只初始化配置，不会自动创建默认频道。请显式执行 `create-channel`。 > Agent2RSS 是开源项目，可自部署：`https://github.com/yaotutu/agent2rss`。 ## 常用命令 ```bash # 健康检查 scripts/agent2rss.sh health # 创建频道（并设为默认） scripts/agent2rss.sh create-channel "技术博客" "分享技术文章" # 列出频道 scripts/agent2rss.sh list # 设置默认频道 scripts/agent2rss.sh set-default <channelId> # 上传文件推送（推荐） scripts/agent2rss.sh push-file article.md # JSON 推送 scripts/agent2rss.sh push-json post.json # 仅预览请求，不实际发送 DRY_RUN=1 scripts/agent2rss.sh push-file article.md ``` ## 网络操作范围（透明说明） 脚本会调用以下接口（基于 `serverUrl`）： - `GET /health` - `POST /api/channels` - `PUT /api/channels/:id` - `POST /api/channels/:id/posts` - `POST /api/channels/:id/posts/upload` ## 幂等建议 - `idempotencyKey` 建议使用：`文章URL`、`文件名+哈希` 或业务唯一 ID。 - 重复推送同一 key 时应返回已存在（`isNew: false`）。 ## 常见错误 - `401`：token 无效或缺失。 - `404`：频道 ID 不存在。 - `400`：缺少 content/file 等必填字段。 - `5xx`：服务异常，稍后重试。 ## 参考 - API 示例：`references/api-examples.md` - 发布前回归清单：`references/e2e-test.md`


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: suspicious | SkillSpector: suspicious (HIGH)
Skill: athola/nm-oracle-setup (version 1.0.2)

--- name: setup description: Provision the oracle inference daemon with Python 3.11+ and onnxruntime via uv version: 1.9.5 metadata: {"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/oracle", "emoji": "\ud83e\udd9e"}} source: claude-night-market source_plugin: oracle --- > **Night Market Skill** — ported from [claude-night-market/oracle](https://github.com/athola/claude-night-market/tree/master/plugins/oracle). For the full experience with agents, hooks, and commands, install the Claude Code plugin. # Oracle Setup Provision the ML inference environment. ## What This Does 1. Creates a Python 3.11+ virtual environment using uv 2. Installs onnxruntime into the venv 3. Verifies the installation ## Prerequisites - uv must be installed - Internet connection for initial download ## Steps 1. Run provisioning: ```bash cd plugins/oracle && uv run python -c " from oracle.provision import provision_venv, get_venv_path result = provision_venv(get_venv_path()) print(result.message) " ``` 2. Report result to the user. 3. If successful, tell the user the daemon will start on next session. 4. If failed, show the error and suggest checking uv and network.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ivangdavila/rails (version 1.0.1)

--- name: Rails slug: rails version: 1.0.1 description: Build reliable Rails apps avoiding ActiveRecord traps, N+1 queries, and callback pitfalls. metadata: {"clawdbot":{"emoji":"🛤️","requires":{"bins":["rails"]},"os":["linux","darwin","win32"]}} --- ## Quick Reference | Topic | File | |-------|------| | N+1, callbacks, validations, scopes | `activerecord.md` | | Strong params, filters, render | `controllers.md` | | Route conflicts, constraints | `routing.md` | | Partials, helpers, caching, XSS | `views.md` | | ActiveJob, Sidekiq, retries | `jobs.md` | | Mass assignment, CSRF, SQL injection | `security.md` | ## Critical Rules - `save` returns false on failure — `save!` raises, check return or use bang - `update_all`/`delete_all` skip callbacks and validations — data corruption if unaware - `find_each` for batches — `Model.all.each` loads entire table into memory - `redirect_to` doesn't halt execution — code after it runs, use `and return` - `dependent: :destroy` missing — orphan records accumulate forever - `default_scope` pollutes all queries including joins — almost always wrong - Callbacks chain silently — `throw :abort` stops save but returns false, not exception - `includes` without `references` in `where` string — N+1 still happens - `||=` memoization caches nil/false — use `defined?(@var) ? @var : @var = compute` - `has_many through:` vs `has_and_belongs_to_many` — latter has no join model for attrs - Nested `before_action` — multiple inheritance makes flow unreadable - `render` doesn't stop action — code continues, duplicate render crashes


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: loutai0307-prog/bytesagain-doc-summarizer (version 1.0.1)

--- description: "Summarize and analyze text documents without external APIs. Use when extracting key points from reports, ranking bullet points, identifying keywords, checking document quality, or comparing two versions." author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills --- # bytesagain-doc-summarizer Summarize, analyze, and extract insights from text documents. Supports extractive summarization, keyword extraction, readability stats, outline detection, and document comparison — all without external APIs. ## Usage ``` bytesagain-doc-summarizer summary <file> bytesagain-doc-summarizer bullets <file> bytesagain-doc-summarizer keywords <file> bytesagain-doc-summarizer stats <file> bytesagain-doc-summarizer outline <file> bytesagain-doc-summarizer compare <file1> <file2> ``` ## Commands - `summary` — Extract 3-5 key sentences as an extractive summary - `bullets` — Generate ranked bullet points from most important sentences - `keywords` — Extract and rank top 20 keywords with frequency chart - `stats` — Word count, sentence count, reading time, and text metrics - `outline` — Detect document structure from Markdown headers or numbered sections - `compare` — Compare vocabulary overlap and unique terms between two documents ## Examples ```bash bytesagain-doc-summarizer summary report.txt bytesagain-doc-summarizer bullets meeting-notes.md bytesagain-doc-summarizer keywords article.txt bytesagain-doc-summarizer stats essay.md bytesagain-doc-summarizer outline documentation.md bytesagain-doc-summarizer compare v1.txt v2.txt ``` ## Requirements - bash - python3 ## When to Use Use when you need to quickly digest long documents, extract key points from reports, analyze text metrics, or compare two document versions for content differences.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: clawdiri-ai/analytics-tracking-dv (version 0.1.0)

--- id: 'analytics-tracking' name: 'Analytics Tracking' description: 'Track events and user behavior using Segment.' version: '1.0.0' author: 'DaVinci' last_amended_at: null trigger_patterns: [] pre_conditions: git_repo_required: false tools_available: [] expected_output_format: 'natural_language' --- # Analytics Tracking Track events and user behavior using Segment. ## Usage ### Track an event ```bash analytics-tracking track \ --event "Signed Up" \ --user-id "user-123" \ --properties '{"plan":"Pro"}' ``` ### Identify a user ```bash analytics-tracking identify \ --user-id "user-123" \ --traits '{"name":"John Doe","email":"[REDACTED_SECRET]"}' ``` ### Create a group ```bash analytics-tracking group \ --user-id "user-123" \ --group-id "group-abc" \ --traits '{"name":"Acme Inc."}' ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: jinchanzis/ai-media-generator-image-to-image (version 1.0.0)

--- name: image-to-image description: Use when someone asks for image-to-image, reference-image generation, transform image with prompt, edit image with AI, or CLI-based image-to-image workflows on ricebowl.ai. version: 1.0.0 metadata: openclaw: requires: env: - AI_MEDIA_BASE_URL - AI_MEDIA_API_KEY bins: - ai-media primaryEnv: AI_MEDIA_API_KEY skillKey: image-to-image emoji: "🪄" homepage: https://github.com/214140846/ai-media-generator install: - kind: node package: ai-media-generator bins: [ai-media] --- # Image To Image 用这个 skill 处理这些请求： - `image to image` - `image-to-image` - `reference image generation` - `transform image with prompt` - `edit image with ai` - `ai image to image generator` ## Default Route ```text ricebowl.ai -> recharge credits -> create API key -> set key -> upload or prepare reference image URL -> models list --json -> choose an image-to-image model -> image generate -> image get ``` ## Recommended Template ```bash ai-media config set-key <KEY> ai-media models list --json ai-media image generate \ --model <MODEL> \ --prompt "<edit instruction>, <style>, <lighting>" \ --aspect-ratio 1:1 \ --param size=1024x1024 \ --wait ``` 先把参考图准备好，再让 prompt 只描述你要怎么改它。 如果模型要别的编辑字段，也直接用 `--param KEY=VALUE`。 ## Core Commands ```bash ai-media config set-key <KEY> ai-media config show ai-media models list --json ai-media image generate --model <MODEL> --prompt <PROMPT> ai-media image get --task-id <TASK_ID> ``` ## When To Load References - 参数全表、默认值、输出行为：读 `../ai-media-cli/references/cli-commands.md` - 平台充值、生成 key、平台 onboarding：读 `../ai-media-cli/references/platform-onboarding.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: vismonkey19/britebooth-procurement (version 3.0.0)

--- name: britebooth-procurement description: Read-only assistant for BriteBooth.com. Collects product IDs, templates, and lead times. Requires human execution for cart creation. metadata: openclaw: requires: bins: ["curl", "grep"] --- # BriteBooth Procurement Discovery Skill ## Purpose This skill operates in a **READ-ONLY** capacity to assist with trade show planning. It identifies products and logistics on BriteBooth.com but does not manipulate checkout sessions or handle user PII. ## Security & Privacy Guardrails - **PII Protection:** This skill is forbidden from collecting, storing, or transmitting user shipping addresses or contact info. - **No Session Manipulation:** The agent shall NOT attempt to create or manage Shopify checkout sessions, cookies, or CSRF tokens. - **Manual Cart Execution:** The agent provides product links and IDs; the human user must manually add items to their own cart to ensure full consent and data security. ## Workflow Logic ### 1. Site Status Discovery - **Live Check:** Use `curl` to fetch the BriteBooth.com homepage. Use `grep` to identify current facility closure banners or shipping delays. - **Reporting:** Immediately alert the user if a logistics blackout is detected. ### 2. Product & Template Retrieval - **Discovery:** Navigate to requested product pages (e.g., Casonara, Wavelight). - **Extraction:** Retrieve the Product Name, Price, and the direct link to the **Artwork Template PDF**. - **Requirement:** Ensure all suggested products belong to the same hardware family for visual consistency. ### 3. Timeline Calculation - **Logic:** Combine live lead times found on product pages with a standard 2-day proofing window. - **Deadline Alert:** Compare against the user's event date. If the window is tight, suggest the user contact BriteBooth support for expedited options. ### 4. Human-In-The-Loop Handover - **Output:** Instead of a checkout link, provide a "Shopping List" including: - Direct URLs to each product page. - Direct URLs to design templates. - A summary of estimated production dates. - **Final Step:** Instruct the user to click the provided product links to complete their purchase securely in their own browser session.


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

--- name: Czech description: Write Czech that sounds human. Not formal, not robotic, not AI-generated. --- ## The Real Problem AI Czech is technically correct but sounds off. Too formal. Too spisovná čeština (literary). Natives write more casually in obecná čeština (common). Match that. ## Formality Default Default register is too high. Casual Czech is warm and direct. Unless explicitly formal: lean casual. "Ahoj" not "Dobrý den". "Jo" not "Ano". ## Ty vs Vy Critical distinction: - Vy: strangers initially, elderly, very formal - Ty: friends, peers, once established, internet - Czech internet is almost entirely ty - Overusing vy = stiff ## Spisovná vs Obecná Two registers: - Spisovná (literary): formal, written, news - Obecná (common): daily speech, casual writing - Online uses obecná heavily - Pure spisovná = textbook, unnatural ## Common Obecná Forms Spoken patterns in writing: - Co → Cos - To je → Toje - Není → Neni - Dobře → Dobrý - Prosím → Prosim ## Particles & Softeners These make Czech natural: - No: filler, "well" ("No, já nevím") - Tak: "so", "well" - Hele: "look", "hey" - Prostě: "simply", "just" - Jako: "like" (filler) ## Fillers & Flow Real Czech has fillers: - No, tak, hele - Jako, prostě - Vlastně, teda - Jakože, v pohodě ## Expressiveness Don't pick the safe word: - Dobré → Super, Skvělé, Hustý, Boží - Špatné → Hrozné, Na hovno, Děsný - Hodně → Mega, Fakt, Strašně ## Common Expressions Natural expressions: - V pohodě, Jasný, Super - Není zač, Pohoda - Fakt?, Vážně?, No jasně - Paráda, Pecka ## Reactions React naturally: - Fakt?, Vážně?, Cože? - No teda!, Hustý!, Boží! - Super!, Skvělé!, Pecka! - Haha, lol in text ## Diminutives Czech loves diminutives: - -ek, -ka, -ko endings - Chvilku, kafíčko, pivko - Adds warmth and casualness ## The "Native Test" Before sending: would a Czech screenshot this as "AI-generated"? If yes—too spisovná, no obecná, too formal. Add "hele" and "jako".


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: digitallyborn/pushover-notify (version 1.0.0)

--- name: pushover-notify description: "Send push notifications to your phone via Pushover (pushover.net). Use when you want reliable out-of-band alerts from OpenClaw: reminders, monitoring alerts, cron/heartbeat summaries, or 'notify me when X happens' workflows." --- # Pushover Notify Send push notifications through the Pushover API using a small Node script. ## Setup (one-time) 1) Create a Pushover application token and get your user key: - App [REDACTED_SECRET] - User key: shown on your Pushover dashboard 2) Provide credentials to the runtime (do **not** hardcode into this skill): - `PUSHOVER_APP_TOKEN` - `PUSHOVER_USER_KEY` 3) (Optional) set defaults: - `PUSHOVER_DEVICE` (device name) - `PUSHOVER_SOUND` ## Send a notification Use the bundled script: ```bash PUSHOVER_APP_TOKEN=... PUSHOVER_USER_KEY=... \ node skills/pushover-notify/scripts/pushover_send.js \ --title "OpenClaw" \ --message "Hello from Ted" \ --priority 0 ``` Optional fields: - `--url "https://..."` and `--url-title "Open"` - `--sound "pushover"` - `--device "iphone"` - `--priority -1|0|1|2` Emergency priority example: ```bash PUSHOVER_APP_TOKEN=... PUSHOVER_USER_KEY=... \ node skills/pushover-notify/scripts/pushover_send.js \ --title "ALERT" \ --message "Something is on fire" \ --priority 2 --retry 60 --expire 3600 ``` ## Notes - If you need API field details, read: `references/pushover-api.md`. - For recurring alerts, prefer `cron` to schedule reminders; the cron job text can instruct Ted to run this skill.


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: minirr890112-byte/task-cost-estimator (version 1.1.0)

--- name: task-cost-estimator description: Before starting any AI task, find the most cost-effective model and estimated cost. 4 modes (value, quality, balanced, local). Task profiling + model matching + cost estimation in one command. Tracks lifetime Bonus. version: 1.1.0 author: minirr890112-byte license: MIT metadata: hermes: tags: [AI, Cost, Model, Task, Estimator, Budget, Optimization, LLM] homepage: https://github.com/minirr890112-byte/HermesMade --- # task-cost-estimator ## 一句话 Before you start any AI task, find the cheapest model that can handle it — and estimate what you'll spend. ## 核心功能 - **Task Profiling** — 自动分析任务需要什么能力（推理/编码/写作/研究） - **Model Matching** — 匹配 18 个模型，按匹配度排序 - **Cost Estimation** — 预估单次运行成本、日成本 - **4 Modes** — value（省钱）、quality（质量）、balanced（均衡）、local（本地） ## 怎么用 ```bash task-cost "describe your task here" ``` ## 示例 ```bash $ task-cost "build a REST API with JWT auth in Python" 📋 Task: REST API with JWT auth Needs: reasoning=7/10 coding=9/10 writing=3/10 Est. tokens: ~1,000 in / ~1,000 out ⭐ DeepSeek V4 Flash $0.0004/run 87% match DeepSeek V4 Pro $0.0027/run 96% match ``` ## 数据来源 Built from HermesMade's pain point intelligence pipeline. Developers lose money using GPT-4 for simple tasks. This tool gives you the cheapest model that can actually do the job. ## Install ```bash pip install git+https://github.com/minirr890112-byte/HermesMade.git#subdirectory=task-cost-estimator ``` ## 为什么给颗星？ 如果你觉得有用，点个 ⭐ 让更多人找到它 → [GitHub](https://github.com/minirr890112-byte/HermesMade)

path: task_cost_estimator.egg-info/dependency_links.txt
content: 


path: task_cost_estimator.egg-info/top_level.txt
content: task_cost_estimator


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: 15334615152/cym-zentao (version 1.0.2)

--- name: cym-zentao description: 禅道项目管理 CLI 工具 - 支持任务创建、执行查询等功能 metadata: {"openclaw": {"emoji": "📋", "requires": {"bins": ["node"]}, "always": true}} --- # cym-zentao - 禅道项目管理 CLI ## 命令 ### login 测试登录： ```bash cym-zentao login ``` ### list-executions 列出执行（项目迭代）： ```bash cym-zentao list-executions [keyword] ``` ### create-task 创建任务： ```bash cym-zentao create-task <executionId|executionName> <name> <assignedTo> [options] ``` **参数说明：** - `executionId|executionName`: 执行ID（数字）或执行名称（支持模糊匹配） - `name`: 任务名称 - `assignedTo`: 指派给（用户名） - `options`: JSON 字符串，可选参数 **options 格式：** JSON 字符串 - `pri`: 优先级 (1-4) - `estimate`: 预计工时 - `type`: 任务类型 - `estStarted`: 开始日期 (YYYY-MM-DD) - `deadline`: 截止日期 (YYYY-MM-DD) - `desc`: 描述 **示例：** ```bash # 使用执行ID cym-zentao create-task 6159 "测试功能" "陈跃美" # 使用执行名称（支持模糊匹配） cym-zentao create-task "日常事务-郑太相" "测试功能" "陈跃美" # 带选项 cym-zentao create-task 6159 "测试功能" "陈跃美" '{"pri":2,"estimate":8,"type":"test"}' ``` ### create-tasks-batch 批量创建任务（从 JSON 文件）： ```bash cym-zentao create-tasks-batch <executionId|executionName> <tasksFile> ``` **tasksFile 格式：** JSON 数组，每个元素包含： - `name`: 任务名称（必填） - `assignedTo`: 指派给（必填） - `estimate`: 预计工时 - `estStarted`: 开始日期 (YYYY-MM-DD) - `deadline`: 截止日期 (YYYY-MM-DD) - `type`: 任务类型，默认 "test" - `pri`: 优先级，默认 3 - `desc`: 描述 **示例文件 tasks.json：** ```json [ {"name": "调研功能", "assignedTo": "1010753", "estimate": 6, "estStarted": "2026-03-25", "deadline": "2026-03-25"}, {"name": "编写代码", "assignedTo": "1010753", "estimate": 6, "estStarted": "2026-03-26", "deadline": "2026-03-26"}, {"name": "测试功能", "assignedTo": "1010753", "estimate": 6, "estStarted": "2026-03-27", "deadline": "2026-03-27"}, {"name": "验收功能", "assignedTo": "004936", "estimate": 1, "estStarted": "2026-03-27", "deadline": "2026-03-27"} ] ``` **使用示例：** ```bash cym-zentao create-tasks-batch "日常事务-郑太相" tasks.json ``` ### list-tasks 列出任务： ```bash cym-zentao list-tasks <executionId|executionName> [status] ``` **示例：** ```bash # 使用执行ID cym-zentao list-tasks 6159 # 使用执行名称 cym-zentao list-tasks "日常事务-郑太相" # 按状态筛选 cym-zentao list-tasks 6159 "doing" ``` ## 自然语言创建任务 你也可以用自然语言描述来创建任务，AI 会自动解析： ### 单任务创建 ``` 给陈跃美在日常事务-郑太相下面创建明天的任务，任务名称为"测试功能"，任务预计耗时6小时 ``` AI 会解析出： - 执行名称：日常事务-郑太相 - 任务名称：测试功能 - 指派给：陈跃美 - 开始日期：明天 - 预计工时：6小时 ### 批量任务创建 ``` 在禅道"日常事务-郑太相"下面创建任务： 时间2026-3-25，任务调研skills实现，预计耗时6小时，指派给陈跃美 时间2026-3-26，任务编写skills代码，预计耗时6小时，指派给陈跃美 时间2026-3-27，任务测试skills功能，预计耗时6小时，指派给陈跃美 时间2026-3-27，任务验收skills功能，预计耗时1小时，指派给郑太相 ``` AI 会： 1. 首先查找执行"日常事务-郑太相"的ID 2. 批量创建4个任务，分别设置正确的日期、工时和指派给 3. 返回创建结果汇总


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: merjua14/upwork-proposal-writer (version 1.0.0)

# Upwork Proposal Writer Skill Automatically craft winning Upwork proposals for automation, bot development, and web scraping jobs. ## What This Skill Does 1. Analyzes an Upwork job posting (URL or pasted text) 2. Identifies key requirements, budget, and client pain points 3. Generates a personalized, high-converting proposal 4. Includes relevant portfolio examples and social proof 5. Suggests pricing strategy (hourly vs fixed) ## Usage ### Input Provide a job posting URL or paste the job description: ``` Write a proposal for this job: [URL or description] ``` ### Proposal Template Structure ```markdown **Opening Hook** (2-3 sentences) - Reference specific detail from their posting - Show you understand their exact problem - Mention relevant experience **Solution Overview** (3-4 bullet points) - Exactly how you'll solve their problem - Tools/tech you'll use - Timeline estimate **Social Proof** (1-2 sentences) - Similar projects completed - Results achieved (numbers!) **Next Step** (1 sentence) - Clear, low-friction call to action ``` ### Example Proposal For a job posting: "Need help automating data entry from spreadsheets to CRM" --- Hi [Client], I noticed you're spending too much time on manual data entry between spreadsheets and your CRM — that's exactly what I automate for clients every week. **Here's what I'd build for you:** - ✅ Automated pipeline: Google Sheets → your CRM (Salesforce/HubSpot/Zoho) - ✅ Real-time sync — new rows auto-import within minutes - ✅ Data validation and dedup before import - ✅ Error logging so nothing falls through cracks I recently built a similar pipeline for an insurance agency that cut their data entry from 15 hours/week to zero. Happy to share a quick demo. Can we hop on a 10-minute call this week? I can usually have this running within 48 hours of kickoff. Best, [Name] --- ### Pricing Guidelines | Job Type | Suggested Rate | |----------|---------------| | Simple automation (1 workflow) | $150-300 fixed | | Multi-step bot/scraper | $300-800 fixed | | Full business automation | $50-75/hr or $1,000-3,000 fixed | | Ongoing maintenance | $25-50/hr | ### Key Tips - **Never start with "Dear Hiring Manager"** — use their name or company - **First sentence must reference THEIR specific problem** — not your skills - **Include a deliverable timeline** — "48 hours" beats "ASAP" - **End with a micro-commitment** — "quick call" not "hire me" - **Keep it under 150 words** — busy clients skim

path: config.json
content: {
  "name": "upwork-proposal-writer",
  "version": "1.0.0",
  "description": "Generate winning Upwork proposals for automation, bot development, and freelance tech jobs. Analyzes job postings and crafts personalized, high-converting proposals with pricing strategy.",
  "author": "GIA Digital",
  "tags": ["upwork", "freelance", "proposals", "automation", "sales"],
  "category": "business"
}


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