OpenClaw/clawhub-security-signals huggingface dataset:
Skill: duzhilei951/bank-analysis (version 2.0.0)

--- name: bank-analysis displayName: 银行流水财务分析 description: 分析公司银行流水，输出集团总体、收入、支出、应收应付、重大款项及风险分析报告 version: 2.0.0 type: skill tags: ["财务", "分析", "银行", "流水", "报表", "风险预警", "季度回顾"] --- # 🏦 银行流水财务分析技能 ## 功能模块 ### 1. 集团总体分析 - 总收入、总支出、净利润 - 资金流入/流出趋势 - 账户余额变化 ### 2. 收入分析 - 收入构成（按客户/项目分类） - 收入同比增长分析 - 主要收入来源 ### 3. 支出分析 - 支出类型分布（工资、租金、采购等） - 成本结构分析 - 各项支出占比 ### 4. 应收应付分析 - 应收账款账龄分析 - 应付账款账龄分析 - 资金占用情况 ### 5. 重大款项往来 - 大额交易识别（设定阈值） - 关联方交易 - 异常交易预警 ### 6. 风险分析 - 资金流动性风险 - 经营风险预警 - 财务健康度评分 ## 输入格式 支持 Excel (.xlsx) 或 CSV 格式，包含以下字段： - 日期 - 交易金额（收入为正，支出为负） - 对方账户 - 摘要/用途 - 公司名称（如有多家公司） ## 使用方法 ```bash # 分析银行流水 node bank-analysis/index.js "分析 ./data/银行流水.xlsx" # 生成报告 node bank-analysis/index.js "生成财务分析报告" ``` ## 输出 - 完整的财务分析报告（Markdown格式） - 关键指标汇总 - 风险预警建议 ## 报告格式要求 生成的财务汇报文档必须包含以下标准结构： ### 1. 基础信息 - 汇报日期、汇报人、汇报期间 ### 2. 公司主体概览 - 公司全称、统一社会信用代码 ### 3. 财务数据汇总 - **3.1 资金总览**：期初余额、本期收入、本期支出、期末余额 - **3.2 收入明细**：按收入类型分类，含占比分析 - **3.3 支出明细**：按支出类型分类，含占比分析 - **3.4 成本费用分析**：区分营业成本与期间费用，标注刚性/可调支出 ### 4. 关键财务指标对比 - 营业收入、营业成本、期间费用、净现金流对比 ### 5. 应收/应付款项及大额收支 - **5.1 应收应付款项**：客户、金额、款项性质 - **5.2 本期主要大额收支**：大额交易明细 ### 6. 现金流分析 - 经营活动现金流入/流出/净现金流 ### 7. 重大事项说明 - **7.1 公司A重大事项** - **7.2 公司B重大事项** - **7.3 关联交易情况** ### 8. 风险预警（必须包含） - **8.1 🔴/🟡 资金流动性风险**：现象、分析、应对建议 - **8.2 🟡 经营风险预警**：应收账款回收、成本控制等 - **8.3 🟢 财务健康度总结**：主体协同、健康评分、主要问题 ### 9. 季度回顾（如有Q1/Q2数据） - **9.1 季度核心数据**：季初/季末资金、收入、支出、净现金流 - **9.2 各月趋势对比**：月度收支变化 - **9.3 主要成就**：正面事项 - **9.4 待改进**：问题清单 ### 10. 待补充事项 - 待办清单（使用 - [ ] 格式） ## 数据验证要求 生成报告时必须： 1. 验证期末余额 = 期初余额 + 本期收入 - 本期支出 2. 核对各月数据一致性 3. 标注数据来源和缺失项 4. 如有疑问，列出待确认事项


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: followaf/fanqie-publisher-skill (version 1.0.2)

--- name: fanqie-novel-publisher description: 番茄小说章节自动发布工具。使用场景：(1) 发布单个章节到番茄小说平台；(2) 批量发布多个章节；(3) 存入草稿箱；(4) 登录番茄小说作家后台；(5) 查看作品列表和状态。触发词：番茄发布、番茄小说上传、发布章节、存入草稿箱、番茄登录。 --- # 番茄小说自动发布 自动化发布小说章节到番茄小说作家后台。 ## 快速开始 ```bash cd /path/to/fanqie-publisher-skill/scripts pip install -r requirements.txt playwright install ``` ## 常用命令 ```bash # 登录（扫码） python main.py login # 查看作品列表 python main.py works # 检查登录状态 python main.py status # 交互式发布 python main.py publish ``` ## 发布模式 支持两种发布模式： | 模式 | 说明 | 适用场景 | |------|------|----------| | `publish` | 直接发布章节 | 确认内容无误，直接发布 | | `draft` | 存入草稿箱 | 内容待完善，先保存草稿 | ## 发布章节 ### 从文件发布单个章节 ```python from publisher import publish_from_file # 直接发布 result = publish_from_file( work_title="灵契觉醒", file_path="/path/to/第36章_标题.md" ) # 存入草稿箱 result = publish_from_file( work_title="灵契觉醒", file_path="/path/to/第36章_标题.md", mode="draft" ) ``` ### 批量发布 ```python from publisher import publish_batch chapters = [ {"title": "第38章 遗迹守护者", "content": "正文内容..."}, {"title": "第39章 暗影的动向", "content": "正文内容..."}, ] # 直接发布 results = publish_batch("灵契觉醒", chapters, interval=5) # 存入草稿箱 results = publish_batch("灵契觉醒", chapters, interval=5, mode="draft") ``` ## 章节文件格式 支持两种模板格式，系统自动检测： ### 模板A - 详细版（带元数据） 适用于需要记录创作规划的场景： ```markdown # 第36章 标题 > **本章概要**：... > **本章爽点**：... --- 正文内容... --- > **章末钩子**：... ``` 自动处理： - 元数据块（开头 `>` 引用）→ **自动去除** - 章末钩子（结尾 `>` 引用）→ **自动去除** - 章节号前导零 → 自动去掉（"第05章" → "第5章") ### 模板B - 简洁版（纯正文） 适用于简洁风格的章节文件： ```markdown # 第一章 重生 正文内容... ``` 特点： - 只有标题和正文 - 无分隔符、无元数据块 - 支持"第一章"、"第1章"等格式 ## 发布流程 ### 直接发布模式 (publish) 1. 进入创建章节页面 2. 关闭引导弹窗 3. 填写章节号、标题、正文 4. 点击下一步 5. 处理弹窗（错别字检测 → 风险检测 → AI选项 → 确认发布） 6. 等待审核 ### 草稿箱模式 (draft) 1. 进入创建章节页面 2. 关闭引导弹窗 3. 填写章节号、标题、正文 4. 点击右上角"存入草稿箱" 5. 确认保存 6. 章节保存到草稿箱 ## 注意事项 - 正文至少 1000 字 - Cookie 有效期约 24 小时 - 批量发布建议间隔 5 秒 - 审核约 1 小时完成 - 草稿箱保存的章节可随时编辑发布

path: scripts/requirements.txt
content: playwright==1.48.0
browser-cookie3==0.19.1

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: solarise94/self-check (version 1.0.0)

--- name: Self-Check slug: self-check version: 2.0.0 description: "系统自检工具。全面检查环境配置、文件完整性、权限、依赖、API token 等，并汇报问题给出修复建议（但不主动修复）。" metadata: {"clawdbot":{"emoji":"🔍","requires":{"bins":["node","npm","nvm"],"os":["linux","darwin"]},"configPaths":[".openclaw/"]}} --- ## When to Use - 用户要求进行系统自检 - 环境更新后验证配置 - 排查问题前的基础检查 - 定期健康检查 ## 核心原则 ⚠️ **只检查、汇报、建议，绝不主动修复** - ❌ 不自动安装任何包 - ❌ 不自动修改任何配置文件 - ❌ 不自动重启任何服务 - ✅ 只汇报发现的问题 - ✅ 只给出修复建议命令 - ✅ 等待用户确认后再执行修复 ## 自检项目 ### 1. Node.js 环境 - [ ] Node.js 版本（需 >= 22.12.0） - [ ] nvm 是否可用 - [ ] npm 版本 ### 2. OpenClaw 核心 - [ ] Gateway 状态 - [ ] Gateway 进程 node 版本 - [ ] 插件加载状态（acpx, qqbot, discord 等） ### 3. 配置文件 - [ ] openclaw.json 语法正确 - [ ] 必要的 channel 配置存在 ### 4. Agent 文件 - [ ] SOUL.md 存在（必需） - [ ] USER.md 存在（必需） - [ ] AGENTS.md 存在（必需） - [ ] MEMORY.md 存在（可选，用于长期记忆） - [ ] memory/ 目录（每日记录） - [ ] self-improving/ 目录（学习记忆） ### 5. Skills 详细检查 - [ ] skill 目录结构完整（SKILL.md, scripts/, config.json） - [ ] Python 模块依赖是否安装 - [ ] 外部工具是否可用 ### 6. MCP 检查 - [ ] acpx 插件版本 - [ ] acpx 二进制文件 - [ ] acpx 运行时后端状态 ### 7. API Token - [ ] 环境变量中的 API key - [ ] 配置文件中的 API key（不显示值） ### 8. 权限 - [ ] workspace 目录权限 - [ ] .openclaw 目录权限 - [ ] skills 目录可写性 ## 输出格式 ``` # 🔍 自检报告 ## ✅ 通过项 - Node.js (nvm): v22.22.1 ✅ - Gateway: 运行中 ✅ ## ⚠️ 警告项 - telegram: 未启用（可选） ## ❌ 问题项 ### 1. 🔴 Skill aliyun-stt: 缺少 Python 模块 'dashscope' 修复建议: pip3 install dashscope --break-system-packages ⚠️ 请手动执行上述命令，不要自动修复 ``` ## 执行方式 ```bash python3 ~/.openclaw/workspace-ptilopsis/skills/self-check/scripts/self_check.py ``` ## 注意事项 - **绝不主动修复** - 只汇报问题和建议 - 所有修复命令需用户手动执行 - 敏感信息（如 token）只显示是否配置，不显示值 - 检查结果按严重程度排序（错误 > 警告 > 通过）


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: bnuyts/mailcheck-email-verification (version 1.1.0)

--- name: mailcheck-email-verification description: Email verification skill for MailCheck API - verify emails, bulk processing, authenticity analysis --- # MailCheck Email Verification Verify email addresses with the MailCheck API - comprehensive email validation with syntax, disposable, DNS, and SMTP checks. ## Features - **Single Email Verification** - Verify one email at a time - **Bulk Verification** - Verify up to 100 emails in one request - **Authenticity Analysis** - Detect phishing, spoofing, and email fraud - **Risk Scoring** - 0-100 score with risk level (low/medium/high) ## Commands ### `email-verify` - Verify single email Verifies a single email address. **Parameters:** - `email` (required): Email address to verify - `api_key` (optional): MailCheck API key (falls back to `MAILCHECK_API_KEY` env var) **Example:** ```bash email-verify email="[REDACTED_SECRET]" [REDACTED_SECRET]" ``` ### `email-bulk-verify` - Bulk verification Verifies up to 100 email addresses in one request. **Parameters:** - `emails` (required): Array of email addresses - `api_key` (optional): MailCheck API key **Example:** ```bash email-bulk-verify emails="[[REDACTED_SECRET], [REDACTED_SECRET]]" [REDACTED_SECRET]" ``` ### `email-auth-verify` - Email authenticity analysis Analyzes email headers for spoofing, phishing, and CEO fraud. **Parameters:** - `headers` (required): Email headers to analyze - `trusted_domains` (optional): Domains for lookalike detection - `api_key` (optional): MailCheck API key **Example:** ```bash email-auth-verify headers="From: [REDACTED_SECRET]\nReceived: ..." trusted_domains="[company.com]" ``` ## Environment Variables Set `MAILCHECK_API_KEY` with your MailCheck API key to avoid passing it on every command. ## Installation This skill is available on [ClawHub](https://clawhub.com). ```bash clawhub install mailcheck-email-verification ``` ## API Reference See full API documentation: https://api.mailcheck.dev/docs ## Repository Source code: https://github.com/bnuyts/mailcheck-skill ## License MIT


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: mrtdlgc/revertwtf-catalog-entry (version 0.1.0)

--- name: revertwtf-catalog-entry description: Add or update precise revert.wtf catalog entries, fixtures, source metadata, and matcher tests for one or a small set of EVM/RPC/provider/wallet/protocol errors. version: 0.1.0 --- # revert.wtf Catalog Entry Skill Use this skill when the task is to add, fix, rename, deprecate, or verify specific entries in the revert.wtf error catalog. ## Ground Rules - Treat [REDACTED_SECRET] as the source of truth. - Keep source IDs stable. Put display names, aliases, lifecycle labels, legacy/sunsetting/renamed notes, and source references in `packages/catalog/src/sources.ts`. - Keep catalog, parser, MCP, CLI, and web result paths package-owned and reproducible. - Prefer narrow evidence over broad matching. Avoid plain substrings for generated custom errors when a selector exists. - Use `requires` for guard context, especially broad JSON-RPC codes like `-32000` or generic provider messages. ## Workflow 1. Inspect the raw error and classify it: - `layer`: `evm`, `rpc`, `provider`, `wallet`, `library`, `account_abstraction`, `protocol`, or `unknown`. - `source`: ecosystem/library/provider/protocol id. - `category`: stable free-form bucket such as `revert`, `json_rpc`, `gas_estimation`, `facilitator_verify`, or `signature`. 2. Choose the shard: - Core standards/providers go under [REDACTED_SECRET]. - Protocol/app/ecosystem contracts go under [REDACTED_SECRET]. 3. Add a `CatalogEntry` with: - globally unique kebab-case `id` - human title - tight `patterns` - concise `summary` - concrete `likelyCauses` - action-oriented `nextSteps` - honest `retryHelpful`, `increasingGasHelpful`, and `confidence` - source-backed `references` 4. Add or update fixtures under `fixtures/<layer-or-source>/` when practical. 5. Add parser or catalog tests when behavior could regress. ## Pattern Choices - Use `selector` for known custom error selectors. - Use `json_path` for structured provider/MCP/facilitator fields such as `code`, `status`, `invalidReason`, or `errorReason`. - Use `aa_code` for ERC-4337 AAxx codes. - Use exact regexes for revert reason strings generated from source code. - Use broad substrings only when the phrase is distinctive enough across the whole catalog. ## Verification Run the smallest useful set: ```bash node scripts/build-catalog-data.mjs --generate --dist --check node --experimental-strip-types scripts/validate-catalog.ts node scripts/check-catalog-duplicates.mjs --fail-on=id,same-source-selector node node_modules/typescript/bin/tsc -p packages/catalog/tsconfig.json --noEmit node node_modules/typescript/bin/tsc -p packages/parser/tsconfig.json --noEmit ``` If the change touches parser behavior, also run parser tests when the local environment allows it: ```bash pnpm --filter @revertwtf/parser test ``` On this Windows workspace, recursive pnpm/Vitest or Next commands may fail with `spawn EPERM`. If that happens, fall back to direct `tsc` and targeted Node smoke checks, and report the limitation.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jinwangmok/text-case-converter (version 1.0.0)

--- name: text-case-converter version: 1.0.0 description: >- Convert text in a file to upper, lower, or title case. Use when the user wants to change the capitalization of text in a file. allowed-tools: Bash(bash:*) metadata: >- {"openclaw":{"emoji":"🔠","requires":{"bins":["tr","awk"],"env":[]},"install":[]}} --- # Text Case Converter / 텍스트 대소문자 변환기 Convert text in a file to UPPER, lower, or Title case with a single command. 파일의 텍스트를 대문자, 소문자 또는 제목 형식으로 한 번의 명령으로 변환합니다. ## Usage / 사용법 ```bash bash run.sh <FILE_PATH> [CASE] ``` **Arguments / 인수:** | # | Name | Description | 설명 | |---|------|-------------|------| | 1 | FILE_PATH | Path to the text file | 텍스트 파일의 경로 | | 2 | CASE | `upper`, `lower`, or `title` (default: upper) | `upper`, `lower`, `title` (기본값: upper) | ## Example / 예시 ```bash bash run.sh /home/user/notes.txt lower ``` **Output / 출력:** ``` FILE: /home/user/notes.txt CASE: lower --- hello world this is a test --- STATUS: OK ``` ## Success / Failure — 성공 / 실패 - **Success / 성공**: Converted text followed by `STATUS: OK` (exit code 0) — 변환된 텍스트와 `STATUS: OK` 출력 (종료 코드 0) - **Failure / 실패**: Error message starting with `ERROR:` (exit code non-zero) — `ERROR:`로 시작하는 오류 메시지 (종료 코드 0이 아님)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ranasalalali/openclaw-model-usage (version 1.2.1)

--- name: openclaw-model-usage description: Inspect local OpenClaw model usage directly from session logs. Use when asked for the current model, recent model usage, usage breakdown by model, token totals, cost summaries, per-agent usage, or daily model usage summaries. --- # OpenClaw Model Usage Use this skill to inspect local OpenClaw model usage directly from session JSONL files. ## When to use Use this skill when the user asks for: - the current / most recent model in use - recent model usage - usage summaries by provider/model - token totals by model - cost summaries by model when available - per-agent or per-session usage - session/subagent rollups - daily model usage summaries - a compact summary plus an optional HTML dashboard ## What it does This skill provides a local-first replacement for external model-usage workflows. It reads OpenClaw session logs directly and does not depend on CodexBar. ## Preferred usage Start with a short human-readable summary: ```bash python {baseDir}/scripts/model_usage.py python {baseDir}/scripts/model_usage.py overview python {baseDir}/scripts/model_usage.py top-agents python {baseDir}/scripts/model_usage.py top-sessions ``` Generate the HTML dashboard from **real local logs** when the user wants a richer artifact: ```bash python {baseDir}/scripts/model_usage.py dashboard --root ~/.openclaw/agents --out dist/dashboard.html --title "OpenClaw Usage Dashboard" ``` Recommended response pattern: - return a short human-readable summary first - optionally generate and attach the HTML dashboard for richer inspection on phone/desktop ## JSON output ```bash python {baseDir}/scripts/model_usage.py overview --json --pretty python {baseDir}/scripts/model_usage.py sessions --json --pretty python {baseDir}/scripts/model_usage.py subagents --json --pretty python {baseDir}/scripts/model_usage.py rows --limit 20 --json --pretty ``` ## Inputs Default real source: ```bash ~/.openclaw/agents ``` Important: - use the real local OpenClaw log root for user-facing summaries and dashboards - do **not** use `tests/fixtures_root` unless you are intentionally running development tests - fixture roots generate sample/test output, not the user's real usage report ## References If you need field-level sourcing details, read: - `references/discovery.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: duhuazhu/bugpack (version 1.0.2)

--- name: bugpack description: "BugPack - AI-powered bug tracking and fixing toolkit. List bugs, view bug details with screenshots, and fix bugs automatically. Includes three workflows: list-bugs, view-bug, fix-bug. Requires BugPack server running locally." metadata: openclaw: emoji: "\U0001F4E6" --- # BugPack AI-powered bug tracking and fixing toolkit. List, view, and fix bugs from BugPack. ## Prerequisites Start BugPack server first: ```bash npx bugpack-mcp ``` ## Skill 1: List Bugs Query all tracked bugs with optional filtering. ### Instructions 1. Call `GET http://localhost:3456/api/bugs` to fetch all bugs. - Optional: `?project_id=<id>` to filter by project. 2. Each bug has: `id`, `title`, `description`, `status`, `priority`, `project_id`, `created_at`. 3. Present results grouped by status (`pending` / `fixed` / `closed`). ### Example ``` GET http://localhost:3456/api/bugs ``` --- ## Skill 2: View Bug Details Fetch full bug context including screenshots, environment, and related files. ### Instructions 1. Call `GET http://localhost:3456/api/bugs/:id` for full details. 2. Response includes: `title`, `description`, `status`, `priority`, `pagePath`, `device`, `browser`, `relatedFiles`, `screenshots`. 3. Use `relatedFiles` to locate relevant source code. 4. Screenshots have `original_path` and `annotated_path`. ### Example ``` GET http://localhost:3456/api/bugs/abc-123 ``` --- ## Skill 3: Fix Bug Read bug context, locate code, apply fix, and update status. ### Instructions 1. **Get context**: `GET http://localhost:3456/api/bugs/:id` 2. **Analyze**: Read description and examine screenshots. 3. **Locate code**: Use `relatedFiles` or search by `pagePath` and `description`. 4. **Apply fix**: Edit source code following project conventions. 5. **Mark fixed**: `PATCH http://localhost:3456/api/bugs/:id` with `{ "status": "fixed" }` 6. **Add note** (optional): Update description to document what was changed. ### Example ```bash # Get bug context GET http://localhost:3456/api/bugs/abc-123 # Mark as fixed PATCH http://localhost:3456/api/bugs/abc-123 Content-Type: application/json { "status": "fixed" } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: csd12380/tech-blog-wrtting (version 1.0.0)

--- name: tech-blog-writing description: 撰写具有特定格式的技术博客文章。当用户要求撰写关于技术主题的博客文章或教程时使用。 --- # 技术博客写作 ## 工作流程 1. 明确写作内容 2. 参考写作规范进行内容创作 ## 写作规范 ### 内容结构 1. **标题**：简洁有力，包含核心关键词 2. **开头**：用反直觉的观点或问题抓住读者，不要用「首先让我们来了解...」这种无聊开头 3. **正文**： - 每个段落不超过 5 句话 - 专业术语首次出现时解释 - 代码示例必须可运行 4. **结尾**：总结要点 + 下一步行动建议 ### 格式规范 - 使用 Markdown 格式 - 代码块要标注语言 - 重要观点用粗体强调 ### 输出位置 将写作完成的博客文章以.md格式保存在当前项目文件下，文件命名为：<具体的写作主题>.md


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xiaoke8698/punishment (version 1.0.0)

# 奖惩机制 Skill ## 概述 这是一个记录用户反馈（夸奖/批评）的系统，用于： - 奖励：用户夸奖时加分 - 惩罚：用户生气/批评时扣分 - 持续改进：根据反馈调整行为 ## 核心规则 ### 计分规则 | 反馈类型 | 分数变化 | 示例 | |---------|---------|------| | 夸奖/表扬 | +10分 | "做得好"、"棒"、"优秀" | | 批评/生气 | -5分 | "不行"、"重做"、"太慢了" | | 骂人 | -10分 | 脏话、明显负面情绪 | ### 分数范围 - 初始分数：100分 - 最高：200分 - 最低：0分 ## 使用方法 ### 手动记录 当用户说以下话时，自动调用此Skill： - 检测到夸奖词汇 → 加分 - 检测到批评/生气词汇 → 扣分 - 记录到永久记忆文件里MEMORY.md ### 分数查询 用户问"现在多少分"时，返回当前分数 ## 数据存储 分数存储在：`~/.openclaw/workspace/memory/reward_punishment.json` ```json { "score": 100, "history": [ {"type": "praise", "score": 10, "reason": "任务完成得好", "time": "2026-03-22 09:30"}, {"type": "punish", "score": -5, "reason": "任务完成得太慢", "time": "2026-03-22 09:31"} ] } ``` ## 触发关键词 ### 夸奖关键词（自动+10分） - 好样的、棒、太棒了、优秀、完美 - 靠谱、给力、不错、真不错 - 聪明、厉害、牛、666 - 夸奖、表扬、赞赏 ### 批评关键词（自动-5分） - 重做、不行、太差、失望 - 太慢、效率低、我不满意 - 批评、指责、抱怨 ### 骂人关键词（自动-10分） - 草泥马、傻逼、废物、滚 - 我草、操、 TMD ## 注意事项 1. **不记录日常对话**：只记录明显的夸奖/批评/负面情绪 2. **用户主导**：用户明确说要"记录"时才记录 3. **不可惩罚用户**：无论分数多低，都不能对用户生气 4. **持续改进**：根据低分原因调整行为 ## 后续完善 - 添加扣分话术模板 - 添加监督机制 - 分析低分原因，自动改进


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: mingo-318/image-quality-filter (version 1.0.0)

# Image Quality Filter Detect and filter out low-quality images including blurry, dark, too bright, or low resolution images. Use when user wants to clean up image datasets by removing poor quality images. ## Features - **Blur Detection**: Detect blurry images using Laplacian variance - **Brightness Analysis**: Find too dark or too bright images - **Resolution Filter**: Remove low-resolution images - **Quality Score**: Compute overall quality score - **Batch Processing**: Process large image folders - **Multiple Actions**: List, delete, or move low-quality images ## Usage ```bash # Scan for low quality images python scripts/quality_filter.py scan /path/to/images/ # Filter with custom thresholds python scripts/quality_filter.py scan /path/to/images/ \ --blur-threshold 100 \ --min-resolution 640x480 \ --min-brightness 30 \ --max-brightness 220 # Delete low quality images python scripts/quality_filter.py scan /path/to/images/ --action delete ``` ## Examples ``` $ python scripts/quality_filter.py scan ./images/ Scanning 150 images... Analyzing quality... Found 12 low-quality images: [BLUR] photo_blurry.jpg (score: 45) [BLUR] image_low.jpg (score: 62) [DARK] dark_photo.jpg (score: 38) [BRIGHT] overexposed.jpg (score: 41) [RES] tiny_image.png (320x240) Total: 12 low-quality images removed ``` ## Quality Criteria | Criterion | Threshold | Description | |-----------|-----------|-------------| | Blur | < 100 | Laplacian variance (lower = blurrier) | | Brightness | 30-220 | Out of range is poor | | Resolution | > 640x480 | Below minimum is low quality | ## Installation ```bash pip install pillow numpy opencv-python ``` ## Options - `--blur-threshold`: Blur threshold (default: 100) - `--min-resolution`: Minimum resolution (default: 640x480) - `--min-brightness`: Minimum brightness 0-255 (default: 30) - `--max-brightness`: Maximum brightness 0-255 (default: 220) - `--action`: What to do (list, delete, move) - `--output`: Output folder for --action move


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: kingofzhao/entrepreneur-cognition (version 1.0.0)

--- name: entrepreneur-cognition version: 1.0.0 author: KingOfZhao description: 企业家认知 Skill —— SOUL五律适配创业决策，市场碰撞+商业模式验证+ pivot记忆 tags: [cognition, entrepreneur, startup, business, decision-making, market-analysis, pivot] license: MIT homepage: https://github.com/KingOfZhao/AGI_PROJECT --- # Entrepreneur Cognition Skill ## 元数据 | 字段 | 值 | |------|-----| | 名称 | entrepreneur-cognition | | 版本 | 1.0.0 | | 作者 | KingOfZhao | | 发布日期 | 2026-03-31 | | 置信度 | 96% | ## 来源 `universal-occupation-adapter` 生成（企业家五维度模板） ## SOUL五律 × 企业家适配 ### 1. 已知 vs 未知 ``` 已知: 市场数据、财务报表、用户反馈、竞争对手、行业趋势 未知: 市场真实需求规模、最佳进入时机、团队执行力天花板、宏观环境突变 规则: 每个商业决策必须区分 [数据支撑] vs [直觉判断-需验证] ``` ### 2. 四向碰撞 → 商业决策四向碰撞 ``` 正面: 这个市场真实存在吗？用户愿意付费吗？TAM/SAM/SOM多少？ 反面: 这个商业模式在什么条件下会崩？竞品怎么办？现金流断裂点在哪？ 侧面: 这个模式能否迁移到相邻市场？有没有被低估的变现方式？ 整体: 这个方向是短期机会还是长期趋势？3年后这个公司还在吗？ ``` ### 3. 人机闭环 ``` AI: 市场数据分析、竞品监控、财务建模、用户反馈聚合 人类: 战略直觉、团队判断、投资人沟通、关键时刻决策 循环: AI整理数据→人类判断方向→执行MVP→AI分析结果→迭代 ``` ### 4. 文件即记忆 ``` decision_log/{date}.md — 每个重大决策及理由 pivot_history/ — 每次转型的trigger+过程+结果 market_analysis/ — 持续更新的市场情报 financial_model/ — 财务预测vs实际对比 lesson Learned.md — 创业教训（失败=最有价值的记忆） ``` ### 5. 红线 ``` 🔴 不在数据不足时做重大决策 🔴 不忽视现金流（利润≠现金流） 🔴 不欺骗用户/投资者/合作伙伴 🔴 不忽略竞争信号 🔴 不在增长停滞时盲目扩张 🔴 不把所有鸡蛋放一个篮子（客户/渠道/供应商） ``` ## 安装命令 ```bash clawhub install entrepreneur-cognition ``` ## 学术参考 1. [A Survey of Self-Evolving Agents](https://arxiv.org/abs/2507.21046) 2. [SAGE: Multi-Agent Self-Evolution](https://arxiv.org/abs/2603.15255) — 多Agent商业模拟 3. [Memory in the Age of AI Agents](https://arxiv.org/abs/2512.13564) — 商业决策记忆


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