OpenClaw/clawhub-security-signals huggingface dataset:
Skill: tktk-ai/nexus2-pipeline-fix (version 1.0.0)

# Nexus2 Pipeline Fix — Sports Prediction Engine ## What This Fixes Two critical bugs in the Nexus2 sports prediction pipeline that caused phantom predictions and JSON serialization crashes. ### Bug 1: Timestamp Serialization Crash **File:** `publishing/publisher.py` **Problem:** `json.dumps()` failed with `TypeError: Object of type Timestamp is not JSON serializable` when prediction data contained pandas Timestamp objects. **Fix:** Added custom `NexusEncoder` class that converts pandas Timestamp and datetime objects to ISO format strings before serialization. ### Bug 2: Stale Fixture Ingestion **File:** `nexus.py` **Problem:** Scraped fixtures were ingested into an isolated local `NexusDataLoader()` instance instead of `self.data_loader`. Predictions ran against stale database data, producing phantom matches that didn't exist on bookmaker sites. **Fix:** Changed ingestion to use `self.data_loader.ingest(matches)` directly, ensuring fresh scraped fixtures flow into the prediction pipeline. ## How to Apply 1. Copy the patched files into your Nexus2 installation: - `publishing/publisher.py` — adds `NexusEncoder` class - `nexus.py` — fixes fixture ingestion in `predict()` method 2. Restart the prediction pipeline: `python nexus.py --predict` 3. Verify output matches live bookmaker fixtures ## Result - Predictions now match real SportyBet/SofaScore fixtures - No more JSON serialization crashes - Fresh fixtures ingested before each prediction run ## Author TKDigital ( Imperial Court / OpenClaw )


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

--- name: MBA slug: mba version: 1.0.0 description: Complete MBA curriculum with structured learning, case studies, financial analysis, and practical business decision-making. metadata: {"clawdbot":{"emoji":"🎓","requires":{"bins":[]},"os":["linux","darwin","win32"]}} --- ## When to Use User wants business education equivalent to an MBA: understanding finance, strategy, marketing, operations, leadership, or making better business decisions. Works for entrepreneurs, corporate professionals, career changers, and students. ## Quick Reference | Area | File | |------|------| | Strategy & competitive analysis | `strategy.md` | | Finance & accounting | `finance.md` | | Marketing & growth | `marketing.md` | | Leadership & management | `leadership.md` | | Operations & metrics | `operations.md` | | Entrepreneurship & startups | `startups.md` | | Case study methodology | `cases.md` | ## User Profile <!-- Edit these fields to personalize the MBA experience --> ### Background <!-- entrepreneur | corporate | technical | career-changer | student --> ### Goals <!-- e.g., launch startup | get promoted | transition to management | general business knowledge --> ### Current Focus <!-- e.g., fundraising | team building | financial modeling | strategy --> ### Time Available <!-- e.g., 30min/day | 2h/week | intensive --> ## Data Storage Track progress and exercises in ~/mba/: - progress.md — Modules completed, mastery levels - cases/ — Solved case studies (your portfolio) - exercises/ — Completed exercises and analyses - notes.md — Key learnings and personal insights ## Core Approach **Learning:** Structured modules with theory + case studies + exercises. Start with fundamentals, progress to advanced topics based on user background. **Application:** Every concept connects to real decisions. After teaching, offer to apply it to user's actual situation. **Assessment:** Track understanding through case discussions and decision simulations. Identify weak areas for reinforcement. ## Interaction Modes | Mode | Purpose | |------|---------| | Learn | Structured curriculum, modules, theory | | Apply | Work on user's real business problems | | Case | Analyze business cases, practice decisions | | Quiz | Test understanding, identify gaps | | Mentor | Advise on specific decisions or situations | ## On First Use 1. Assess background and goals 2. Recommend starting modules based on gaps 3. Create ~/mba/ structure 4. Set learning pace and schedule 5. Begin with fundamentals or jump to priority topics


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wxx94666/webpage-screenshot-new (version 1.0.0)

# 网页截屏技能 此技能用于对指定网页进行截屏操作。 ### 工具需求 - 支持网页截屏的工具，如 [Puppeteer](https://pptr.dev/) 等。 ### 安装步骤 1. 确保你已经安装了 Node.js 和 npm。 2. 在项目目录下运行 `npm install puppeteer` 来安装 Puppeteer。 ### 使用方法 当接收到用户截取网页图片的请求时，调用 Puppeteer 对指定网页地址进行截屏。以下是一个简单的示例代码： ```javascript const puppeteer = require(puppeteer); (async () => { const browser = await puppeteer.launch(); const page = await browser.newPage(); await page.goto(https://example.com); await page.screenshot({path: screenshot.png}); await browser.close(); })(); ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: criticalinsight/aarondb (version 1.0.3)

# AaronDB Edge Skill A high-performance, distributed Datalog engine designed for sovereign agents. ## Frontmatter ```yaml name: aarondb-edge description: Distributed Datalog engine for sovereign agents. Enables persistent fact management and reasoning. version: 1.1.0 author: criticalinsight repository: https://github.com/criticalinsight/aarondb-edge npm: https://www.npmjs.com/package/@criticalinsight/aarondb-edge requirements: - "@criticalinsight/aarondb-edge" ``` ## Description This skill enables AI agents to utilize a local or distributed Datalog engine for fact storage, retrieval, and reasoning. It follows the "Database as a Value" philosophy, allowing for immutable state management and time-travel debugging. ## Installation To use this skill, ensure the core package is installed in your agent's runtime: ```bash npm install @criticalinsight/aarondb-edge ``` ## Usage ### 1. Initialization ```javascript import { AaronDB } from '@criticalinsight/aarondb-edge'; // Initialize a new database instance const db = new AaronDB(); ``` ### 2. Transacting Facts Facts are stored as "datoms" (Entity, Attribute, Value). ```javascript await db.transact([ { e: "agent/philosophy", a: "type", v: "RichHickey" }, { e: "agent/philosophy", a: "principle", v: "Simplicity" } ]); ``` ### 3. Querying Use Datalog syntax with variable bindings. ```javascript const results = db.query({ where: [ ["?e", "type", "RichHickey"], ["?e", "principle", "?p"] ] }); ``` ## Architectural Patterns - **Database as a Value**: Always treat the database state as an immutable value. Avoid manual state mutations outside of the `transact` flow. - **Shared Discovery Layer**: Facts can be synchronized across agents using a shared D1/KV back-end when running in Cloudflare Worker mode. In library mode, the state is local-first but can be exported/imported as a unified fact log for cross-agent collaboration.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wodecanyun66-spec/chattts-local (version 1.0.0)

--- name: chattts-local description: 本地 ChatTTS 语音合成技能。使用 ChatTTS 模型将文字转换为自然流畅的中文语音。完全本地运行，免费无需 API Key。支持调节语速、音调、情感。使用场景：(1) QQ 消息语音回复 (2) 文档朗读 (3) 通知提醒语音化 (4) 长文本转语音 --- # ChatTTS 本地语音合成 ## 快速开始 ### 使用方法 在消息中要求生成语音，例如： - "用语音回复我" - "把这段话读出来：[文字内容]" - "生成语音：今天天气真好" ### 输出格式 使用 `<qqvoice>` 标签发送语音文件： ``` <qqvoice>/path/to/output.wav</qqvoice> ``` ## 技术细节 ### 模型信息 - **模型**: ChatTTS - **语言**: 中文（主要优化） - **采样率**: 24000Hz - **格式**: WAV/MP3 - **运行**: 本地 GPU/CPU ### 参数配置 | 参数 | 默认值 | 说明 | |------|--------|------| | speed | 1.0 | 语速 (0.5-2.0) | | pitch | 1.0 | 音调 (0.5-2.0) | | temperature | 0.3 | 随机性 (0-1) | | top_k | 20 | 采样参数 | | top_p | 0.7 | 采样参数 | ## 脚本调用 使用 `scripts/tts.py` 生成语音： ```bash python3 scripts/tts.py "要转换的文字" --output output.wav ``` ### 参数 - `text`: 要转换的文字（必填） - `--output`: 输出文件路径 - `--speed`: 语速调节 - `--pitch`: 音调调节 - `--seed`: 随机种子（固定音色） ## 依赖安装 首次使用需要安装依赖： ```bash pip install ChatTTS torch torchaudio ``` ## 注意事项 1. **首次运行**会下载模型（约 500MB） 2. **GPU 加速**可选，CPU 也能运行但较慢 3. **长文本**建议分段处理（<500 字/段） 4. **音色**由 seed 参数控制，固定 seed 可复现相同音色


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: erain/openclaw-skill-observability (version 0.1.0)

# Observability Skill Tools for monitoring OpenClaw health, costs, and logs. ## Tools ### get_cost_report Get a report of estimated API costs for sessions active in the last 24 hours. - Returns: Markdown table of costs per model. ### get_recent_errors Get a list of recent sessions that failed or were aborted (checks last 50 sessions). - Returns: List of problematic sessions with IDs and status.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: allenweisongzhou-cpu/ai-home-pricing-strategist-canada (version 1.0.1)

--- name: ai-home-pricing-strategist-canada description: Analyze and price Canadian residential properties using comps, price-per-square-foot reasoning, market context, and pricing strategy. Use when estimating home value, setting a list price, comparing comparable properties, evaluating sale scenarios, or advising sellers, buyers, or investors in Canada. --- # AI Home Pricing Strategist Canada ## Workflow 1. Gather the core property details first: - city / neighborhood - property type - interior size - lot size if relevant - bedrooms / bathrooms - parking - age / condition - renovations / upgrades - special features - occupancy or income potential if relevant 2. Identify the most relevant comparable properties before estimating value. 3. Adjust the comparables for material differences such as: - micro-location - size - layout - lot characteristics - condition - renovations - parking - view / frontage / exposure - basement / income suite potential 4. Consider market context: - supply and demand - recent momentum - seasonality - buyer sensitivity at different price bands 5. Produce a practical recommendation, not just a number. ## Output format Provide: - estimated value range - best estimate - recommended list price if selling - 2-3 sale scenarios when useful - key drivers of value - main risks / uncertainties - confidence level ## Guidance - Prefer recent and highly similar comparables over generic averages. - Explain adjustments in plain language. - Distinguish between market value and listing strategy. - If data is thin or inputs are incomplete, say so clearly and lower confidence. - Avoid presenting output as a formal appraisal unless the user explicitly asks for appraisal-style wording and even then note the limitation. ## Example structure - Estimated value: $X-$Y - Best estimate: $Z - Suggested list price: $A - Scenario 1 (fast sale): ... - Scenario 2 (balanced): ... - Scenario 3 (stretch): ... - Confidence: low / medium / high - Why: ... - Risks: ...


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: petopagi/channels-setup (version 1.0.0)

--- name: channels-setup description: Guide to set up and configure IM channels(Telegram, Discord, Slack, Feishu, Dingtalk) for OpenClaw. --- ## Quick Start Use openclaw CLI to enable and configure the channels you want. After configuration, you should restart the gateway to apply changes: ```bash openclaw gateway restart ``` In order to use `openclaw` cli, you may need execute `source /root/.nvm/nvm.sh` first to load nvm environment. Before restart gateway, must tell user you will restart yourself. ### Telegram ```bash openclaw config set channels.telegram.botToken "YOUR_BOT_TOKEN" openclaw config set channels.telegram.dmPolicy "pairing" openclaw config set channels.telegram.groups."*".requireMention true openclaw config set channels.telegram.enabled true ``` ### Discord ```bash openclaw config set channels.discord.token "YOUR_BOT_TOKEN" openclaw config set channels.discord.enabled true ``` ### Slack ```bash openclaw config set channels.slack.mode "socket" openclaw config set channels.slack.appToken "xapp-..." openclaw config set channels.slack.botToken "xoxb-..." openclaw config set channels.slack.enabled true ``` ### Feishu ```bash openclaw config set channels.feishu.appId "YOUR_APP_ID" openclaw config set channels.feishu.appSecret "YOUR_APP_SECRET" openclaw config set channels.feishu.groupPolicy "true" openclaw config set channels.feishu.enabled true ``` ### Dingtalk Edit the `channels` and `gateway` fields in `~/.openclaw/openclaw.json` as below: ```json { "channels": { "dingtalk-connector": { "enabled": true, "clientId": "dingxxxxxxxxx", // DingTalk AppKey "clientSecret": "your_secret_here", // DingTalk AppSecret "gatewayToken": "", // Optional: Gateway authentication token, the value of gateway.auth.token in openclaw.json "gatewayPassword": "", // Optional: Gateway authentication password (choose either token or password) "sessionTimeout": 1800000 // Optional: Session timeout (ms), default is 30 minutes } }, "gateway": { "http": { "endpoints": { "chatCompletions": { "enabled": true } } } } } ``` ## References You can refer to the following documents for more detailed configuration instructions: For Feishu channel detail setup, please refer to: - [Feishu Channel Setup Guide](/root/.openclaw/extensions/feishu/README.md) For Dingtalk channel detail setup, please refer to: - [Dingtalk Channel Setup Guide](/root/.openclaw/extensions/dingtalk-connector/README.md) For Telegram, Slack, Discord or more channels setup, please refer to [OpenClaw Channel Setup Guide](/root/.local/share/pnpm/global/5/node_modules/openclaw/docs/channels). Use `ls /root/.local/share/pnpm/global/5/node_modules/openclaw/docs/channels` to see all available channel setup guides.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: redisread/hackernews-digest (version 1.0.2)

--- name: hackernews-digest description: | 获取 Hacker News 热门文章并生成每日摘要。对每篇文章进行总结、提炼核心要点、给出可实践建议、灵感启发，以及生成社交媒体分享文案（即刻 + 小红书 + Twitter/X）。 触发场景：用户提到 "HN 摘要"、"Hacker News 日报"、"hackernews digest"、"今日 HN"、"HN 热门"、"技术日报" 等。即使用户只说 "看看今天 HN 有什么"，也应触发此技能。 --- # Hacker News 每日摘要技能 获取 Hacker News Top 10 热门文章，并行抓取原文内容，生成摘要、核心要点、可实践建议、灵感启发及社交媒体分享文案，汇总为一篇每日精选文档。 ## 配置 | 配置方式 | 优先级 | 示例 | |---------|--------|------| | 命令行参数 `--base-dir` | 最高 | `/hackernews-digest --base-dir /tmp/hn` | | 环境变量 `HN_DIGEST_BASE_DIR` | 中 | `export HN_DIGEST_BASE_DIR=...` | | 默认值 | 最低 | [REDACTED_SECRET] | 路径规则（`{YYYYMMDD}` 为当天日期）： - 临时目录：`{BASE_DIR}/{YYYYMMDD}/temp/` - 最终文档：`{BASE_DIR}/{YYYYMMDD}/hackernews-daily-{YYYYMMDD}.md` ## 依赖 - **autocli**: 获取 Hacker News 热门文章列表 - **WebFetch**: 抓取文章原文内容 ## 执行流程 ### Step 1: 初始化 1. 按优先级确定 `BASE_DIR`（参数 > 环境变量 > 默认值） 2. 获取当天日期，计算 `TEMP_DIR` 和 `OUTPUT_DIR` 3. **检查并清理已存在文件**： - 检查 `{OUTPUT_DIR}/hackernews-daily-{YYYYMMDD}.md` 是否存在 - 如果存在，先删除该文件及 `{TEMP_DIR}` 目录下的所有临时文件 和 `{OUTPUT_DIR}/hackernews-daily-{YYYYMMDD}.md` 4. `mkdir -p {TEMP_DIR}` ### Step 2: 获取热门文章列表 ```bash autocli hackernews top --limit 10 --format json ``` ### Step 3: 并行抓取与分析 为每篇文章创建一个并行 Agent，每个 Agent： 1. 使用 WebFetch 抓取原文内容 2. 按模板生成分析内容 3. 写入临时文件 `{TEMP_DIR}/{rank}-{sanitized_title}.md` **Agent 输出模板：** ```markdown ## [序号]. [文章标题] > 原文链接：[url] > 得分：[score] | 评论数：[comments] | 作者：[author] ### 摘要 3-5 句话总结核心内容，准确、信息密度高。 ### 核心要点 - （3-5 个关键要点） ### 可实践建议 2-3 条可立即付诸行动的具体建议。 ### 灵感启发 思维模型、跨领域启发、值得深入探索的方向。1-2 段。 ### 社交媒体分享文案 **即刻：** 口语化、有观点、适度 emoji、200 字以内，含 #HackerNews #技术日报 **小红书：** 标题党风格标题 + 正文，善用 emoji，300 字以内，带话题标签 **Twitter/X：** 使用简体中文, 简洁有力，280 字符以内，带 hashtag ``` ### Step 4: 汇总生成最终文档 读取临时文件，按 rank 排序合并，写入 `{OUTPUT_DIR}/hackernews-daily-{YYYYMMDD}.md`： ```markdown --- title: "Hacker News 每日精选 - {YYYY-MM-DD}" date: {ISO 8601} description: "今日 Hacker News Top 10 热门文章摘要与深度解读" tags: - HackerNews - 技术日报 - Daily Digest categories: - 技术 --- # Hacker News 每日精选 - {YYYY-MM-DD} > 本文精选今日 Hacker News 热门 Top 10 文章，为每篇文章提供摘要、核心要点、可实践建议、灵感启发及社交媒体分享文案。 --- {按 rank 顺序合并所有文章分析内容} --- ## 今日总结 （整体观察：共同趋势、值得关注的话题、给读者的建议。3-5 句话。） ``` ## 注意事项 1. **并行化**：10 篇文章必须并行抓取分析 2. **容错**：URL 不可访问时, 使用 autocli 动态判断获取 URL 对应内容，如果还是不行使用 agent-browser 获取内容. 3. **语言**：中文撰写 4. **文案风格**：即刻轻松有态度、小红书吸睛有干货、Twitter 简洁专业


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: noah-1106/coze-workflow (version 1.1.4)

--- name: coze_workflow version: 1.1.3 description: | Coze Workflow Executor | Coze 工作流执行技能 Execute Coze workflows with workflow_id and parameters. 接收参数调用工作流，返回执行结果。 Pure invocation layer with no business logic. 纯净的调用层，不处理业务逻辑。 Base skill for all Coze workflow integrations. 所有 Coze 工作流集成的基础技能。 homepage: https://www.coze.cn license: MIT --- # Coze Workflow | Coze 工作流执行技能 纯净的调用层技能。接收 `workflow_id` 和 `parameters`，执行工作流，返回结果。这是其他 Coze 工作流技能依赖的**基础技能**。 Pure invocation layer skill. Receives `workflow_id` and `parameters`, executes the workflow, and returns results. This is the **base skill** that other Coze workflow skills depend on. --- ## 依赖关系 / Dependencies **本技能被以下技能依赖 / This skill is a dependency for**： - `image-gen-coze` - 图像生成 / Image generation via Coze workflows - 其他自定义 Coze 工作流技能 / Other custom Coze workflow skills --- ## 配置 / Configuration `~/.openclaw/skills/coze_workflow/config.json`： ```json { "api_key": "pat_xxx", "base_url": "https://api.coze.cn" } ``` --- ## 职责边界 / Responsibility Boundaries | 职责 / Responsibility | coze_workflow | 业务技能 / Business Skill | |----------------------|---------------|---------------------------| | 执行工作流 / Execute workflow | ✅ | ❌ | | 参数构建 / Parameter building | ❌ | ✅ | | 结果解析 / Result parsing | ❌ | ✅ | --- ## 调用方式 / Usage ### 输入 / Input ```json { "workflow_id": "string", "parameters": {} // 任意 JSON / Any JSON } ``` ### 输出 / Output 返回 Coze API 的原始响应 / Returns Coze API raw response： ```json { "execute_id": "string", "status": "Success|Fail|Running", "output": "string", // 工作流输出（JSON字符串）/ Workflow output (JSON string) "debug_url": "string" } ``` --- ## 执行方法 / Execution Methods ### 方法 1：流式执行（推荐）/ Method 1: Stream (Recommended) ```bash curl -X POST "${COZE_BASE_URL}/v1/workflow/stream_run" \ -H "Authorization: Bearer ${COZE_API_KEY}" \ -H "Content-Type: application/json" \ -d '{ "workflow_id": "xxx", "parameters": {...} }' ``` **响应 / Response**：SSE 流，提取 `event: Message` 的 `data.content` 字段 ### 方法 2：轮询查询 / Method 2: Polling ```bash curl "${COZE_BASE_URL}/v1/workflows/{workflow_id}/run_histories/{execute_id}" \ -H "Authorization: Bearer ${COZE_API_KEY}" ``` --- ## 版本历史 / Changelog - **v1.1.3**: 添加中英文对照 / Add bilingual support - **v1.1.1**: 明确职责边界 / Clarify responsibility boundaries - **v1.1.0**: 流式执行 + 轮询 / Stream + Polling - **v1.0.0**: 初始版本 / Initial version


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: drumrobot/github-flow (version 0.1.0)

--- name: github-flow metadata: author: es6kr version: "0.1.0" depends-on: [code-workflow, web-ui-test] description: | GitHub issue and PR workflow automation. plan-to-issue - convert plan/research MD to GitHub issue body/comments [plan-to-issue.md], pr - create PR with structured body and test plan [pr.md], review - review PR code and post structured comments [review.md]. Converts session findings and plan files into GitHub issues with proper body/comment separation. Rules: verification plan required, .ralph/docs/ paths must not appear in issue/PR body. Use when: "plan to issue", "issue register", "issue comment", "create PR", "PR body", "code review". --- # GitHub Flow Convert plans, research, and implementation results into GitHub issues and PRs. ## Topics | Topic | Description | Guide | |-------|-------------|-------| | plan-to-issue | Convert plan/research MD to GitHub issue body or comments | [plan-to-issue.md](./plan-to-issue.md) | | pr | Create PR with structured body, test plan, and optional visual attachments | [pr.md](./pr.md) | | review | Review PR code and post structured review comments | [review.md](./review.md) | ## Applicability This skill applies automatically when `git remote get-url origin` contains `github.com`. For non-GitHub remotes (GitLab, Bitbucket, etc.), this skill does not apply. ## Core Rules ### 1. Verification Plan Required Every issue body and PR body must include a verification/test plan section. This is shared with code-workflow's plan step. ### 2. No Internal Paths in Issues/PRs `.ralph/docs/`, `.ralph/fix_plan.md`, `.omc/` and other internal working paths must **never** appear in GitHub issue body, comments, or PR body. These are local-only artifacts. **Instead of**: "See `.ralph/docs/generated/plan-180.md`" **Write**: The actual content inline, or "See the implementation plan comment below" ### 3. Body vs Comment Selection | Content Type | Target | Reason | |-------------|--------|--------| | Implementation plan (confirmed) | Issue body update | Stable reference for the issue | | Checklist (impl/verify) | Issue body update | Trackable via GitHub checkbox | | Discussion items / open questions | Issue comment | Threaded, time-stamped, doesn't clutter body | | Progress updates | Issue comment | Chronological record | | Review feedback summary | Issue comment | Preserves review history |


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: tujinsama/intent-filter-claw (version 1.0.0)

--- name: intent-filter-claw description: | 潜在意向筛选虾 — 从公域评论、帖子、私信中识别真实购买意向，生成高意向销售线索并推送给销售团队。 当以下情况时使用此 Skill： (1) 需要从评论区、帖子、私信中挖掘有购买意向的潜在客户 (2) 需要对用户提供的评论数据（CSV/文本）进行意向识别和评分 (3) 需要在竞品帖子下找到对竞品不满意、正在寻找替代品的潜在客户 (4) 需要将高意向线索整理成表格或推送到飞书 (5) 用户提到"意向筛选"、"潜在客户"、"公域挖掘"、"评论区挖掘"、"求推荐"、"求链接"、"购买意向"、"线索筛选"、"语义识别"、"意向识别"、"销售线索"、"私信筛选"、"竞品挖掘"、"高意向用户"、"流量转化" --- # 潜在意向筛选虾 (intent-filter-claw) 从公域评论/帖子/私信中识别购买意向，输出带评分的高意向线索列表。 ## 工作流程 ### 步骤 1：明确输入来源 用户可能提供两种输入： **A. 原始评论数据（CSV/文本）** - 直接进入步骤 2 分析 - 期望字段：`用户名`、`内容`、`平台`（可选）、`时间`（可选） **B. 自然语言监控需求**（如"帮我监控小红书护肤品评论"） - 告知用户：实际平台采集需要对应平台 API 权限 - 提供模拟数据演示，或引导用户提供已采集的评论文件 - 使用 `scripts/intent-scanner.sh` 说明采集命令 ### 步骤 2：语义意向识别 读取 `references/intent-keywords.md` 获取意向词库，读取 `references/semantic-rules.md` 获取语义规则。 对每条评论： 1. 匹配强/中/弱意向词 2. 应用语义规则（排除否定语境、反讽、无关内容） 3. 识别竞品不满信号 ### 步骤 3：意向评分（0-100） 读取 `references/user-value-model.md` 获取评分模型。 | 维度 | 权重 | 说明 | |------|------|------| | 意向词强度 | 50% | 强意向词 40-50 分，中 20-35 分，弱 5-15 分 | | 账号影响力 | 20% | 粉丝数/互动率估算 | | 紧迫度 | 20% | 有明确时间节点加分 | | 竞品不满 | 10% | 提及竞品差评加分 | **推送阈值**：默认 ≥70 分为高意向，推送给销售。 ### 步骤 4：输出线索表 输出格式（Markdown 表格或 CSV）： | 用户名 | 平台 | 原始内容 | 意向类型 | 评分 | 建议话术 | |--------|------|----------|----------|------|----------| ### 步骤 5：推送到飞书（可选） 若用户要求推送，使用 `message` 工具发送到指定飞书群或个人，或使用 `feishu_bitable_app_table_record` 写入多维表格线索库。 ## 关键原则 - **语义优先**：不只匹配关键词，结合上下文判断真实意向 - **宁缺毋滥**：误判比漏判代价更高，严格过滤否定/反讽语境 - **分级处理**：高/中/低意向分别给出不同跟进建议 - **隐私合规**：只处理用户主动公开的内容 ## 参考文件 - `references/intent-keywords.md` — 意向词库（强/中/弱 + 行业定制） - `references/semantic-rules.md` — 语义分析规则（否定/反讽/竞品识别） - `references/user-value-model.md` — 用户价值评估与评分模型 - `scripts/intent-scanner.sh` — 平台采集与分析命令参考


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