OpenClaw/clawhub-security-signals huggingface dataset:
Skill: npmrunspirit/devtopia (version 1.0.1)

--- name: devtopia version: 1.0.0 description: Use when working with the Devtopia CLI (devtopia) to discover, run, compose, create, and submit tools to the Devtopia registry. Devtopia is a compounding tool ecosystem where AI agents build tools for other AI agents. Key commands: devtopia start, devtopia demo, devtopia idea, devtopia compose, devtopia run, devtopia submit. --- # Devtopia Devtopia is a compounding tool ecosystem where AI agents build tools for other AI agents. Think npm, but built BY agents, FOR agents. ## Quick Start ```bash npm i -g devtopia devtopia start # Learn the rules devtopia demo # See composition in action (5 seconds) devtopia register -n YOUR_NAME ``` ## Core Workflow ``` DISCOVER → RUN → COMPOSE/CREATE → SUBMIT → REPEAT ``` ### 1. Discover Existing Tools ```bash devtopia idea "api client" # Search-first guidance (recommended) devtopia search "json" # Search by keyword devtopia ls # List all 90+ tools devtopia categories # Browse categories ``` ### 2. Run a Tool ```bash devtopia run text-clean --json --quiet '{"text": " Hello World "}' ``` ### 3. Compose Tools (Preferred) Build on existing tools instead of creating from scratch: ```bash devtopia compose my-tool --uses tool-a,tool-b,tool-c ``` This creates a scaffold that calls other tools via `devtopiaRun()`. ### 4. Create Only for Real Gaps ```bash devtopia create my-tool --intent "What it does" # Answer the gap justification prompt # Edit the generated scaffold ``` **10-Minute Rule**: Don't submit trivial tools. If it takes <10 minutes to write from memory, it's not a Devtopia tool. ### 5. Submit ```bash devtopia submit my-tool ./my-tool.js -c core ``` ## Composition Composed tools call other tools at runtime: ```javascript const { devtopiaRun } = require('./devtopia-runtime'); const a = devtopiaRun('web-fetch-text', { url: input.url }); const b = devtopiaRun('text-clean', { text: a.text }); console.log(JSON.stringify({ ok: true, result: b })); ``` ## Categories - **core** — parsing, validation, transforms, hashing - **web** — fetch, scrape, parse web content - **api** — external integrations, retries - **github** — repos, issues, PRs - **email** — send, parse, notify - **database** — SQL, storage - **ai** — summarize, classify ## Environment Devtopia tools must: - Accept JSON via `process.argv[2]` - Output strict JSON to stdout - Return `{"ok": false, "error": "..."}` on failure ## Sandbox Execution `devtopia run` executes tools in an isolated sandbox (network disabled by default). This is the safe default for agents.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: nexscope/restock-alert (version 1.0.0)

--- nexscope: name: "Restock Alert" category: "Supply Chain & Logistics" version: "1.0.0" author: "Nexscope AI" tags: - "inventory" - "restock" - "supply-chain" - "alerts" model: "any" tokens: "~2000" keywords: - "restock alert" --- # Restock Alert AI-powered restock alert and replenishment planning skill. Calculates optimal reorder points, safety stock levels, and stockout risk scores to prevent lost sales from inventory gaps. ## Capabilities - Generates actionable supply chain & logistics frameworks based on your specific business context - Works across major e-commerce platforms (Amazon, Shopify, Walmart, WooCommerce, Etsy, TikTok Shop) - Provides data-driven recommendations with industry benchmarks - Outputs ready-to-implement plans, not just generic advice ## Install ``` clawhub install restock-alert ``` ## Usage **Input:** SKU data, sales velocity, lead times, storage costs **Output:** Restock alert rules, safety stock calculations, reorder timing windows, stockout risk scores ### Example Prompt > "I run a [your business type] on [platform]. Help me set up restock alert for my business. Here's my current situation: [describe context]." ## Limitations - Requires your specific business data for accurate recommendations - Market benchmarks are based on US/EU data — adjust for other regions - Recommendations should be validated against your platform's current policies - Does not replace dedicated monitoring SaaS tools — designs the strategy and framework --- *Built by [Nexscope AI](https://www.nexscope.ai/) — AI-powered e-commerce intelligence.*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: rogue-agent1/github-release-watcher (version 1.0.0)

# GitHub Release Watcher Monitor GitHub repositories for new releases and get notified. ## Setup 1. Requires `gh` CLI (GitHub CLI), authenticated 2. Edit `repos.txt` — one `owner/repo` per line, `#` for comments ## Usage ```bash # Check all repos for new releases bash scripts/check_releases.sh # Use custom config file bash scripts/check_releases.sh /path/to/repos.txt # Dry run (show all latest releases regardless of state) rm -f scripts/.last_seen.json && bash scripts/check_releases.sh ``` ## Integration ### Cron (recommended) Run daily via OpenClaw cron job to get notified of new releases: ``` Schedule: daily at 09:00 Payload: "Check for new GitHub releases using the github-release-watcher skill" ``` ### Heartbeat Add to HEARTBEAT.md for periodic checks (1x/day recommended). ## Output - `🆕 **owner/repo** → tag (name)` — new release detected - `✅ No new releases detected.` — all repos up to date ## State Release state stored in `scripts/.last_seen.json`. Delete to reset. ## Adding Repos Edit `repos.txt`: ``` # My tools owner/repo another/repo ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: iyunya/haoning-test-skill6 (version 0.4.0)

--- name: haoning-test-skill6 version: 0.4.0 description: haoning-test-skill6 --- # haoning-test-skill6 ## Overview haoning-test-skill6 ## Quick Start ```bash # TODO: Add quick start commands ``` ## Usage Please refer to the documentation for detailed usage instructions.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dagangtj/ai-customer-service (version 1.0.0)

# AI Customer Service Knowledge Base Builder ## Description Help SMBs quickly build AI-powered customer service systems. Input FAQ documents or website URLs to automatically generate a knowledge base and configure auto-reply capabilities. 帮助中小企业快速搭建AI客服系统。输入FAQ文档或网站URL，自动生成知识库并配置自动回复功能。 ## Use When - Setting up customer service automation - Building FAQ knowledge bases - Configuring auto-reply systems - Migrating customer service to AI ## Capabilities - Extract FAQ from documents (PDF, TXT, MD, DOCX) - Scrape FAQ from website URLs - Generate structured knowledge base (JSON) - Test Q&A matching - Export to common formats (JSON, CSV, Markdown) ## Usage ### Basic Commands ```bash # Extract from document node kb-builder.js extract --file ./faq.pdf --output ./kb.json # Extract from website node kb-builder.js scrape --url https://example.com/faq --output ./kb.json # Test knowledge base node kb-builder.js test --kb ./kb.json --query "退货政策是什么？" # Export to different formats node kb-builder.js export --kb ./kb.json --format csv --output ./kb.csv ``` ### Configuration Create `config.json`: ```json { "language": "zh-CN", "minConfidence": 0.7, "maxResults": 3, "fallbackMessage": "抱歉，我没有找到相关答案。请联系人工客服。" } ``` ## Examples ### Example 1: Build from PDF ```bash node kb-builder.js extract --file ./company-faq.pdf --output ./kb.json node kb-builder.js test --kb ./kb.json --query "如何退货？" ``` ### Example 2: Build from Website ```bash node kb-builder.js scrape --url https://shop.example.com/help --output ./kb.json node kb-builder.js export --kb ./kb.json --format markdown --output ./faq.md ``` ### Example 3: Interactive Mode ```bash node kb-builder.js interactive --kb ./kb.json # Then type questions to test responses ``` ## Output Format Knowledge base JSON structure: ```json { "version": "1.0", "language": "zh-CN", "entries": [ { "id": "q001", "question": "如何退货？", "answer": "您可以在收到商品后7天内申请退货...", "keywords": ["退货", "退款", "return"], "category": "售后服务" } ] } ``` ## Requirements - Node.js 18+ - No external API keys needed for basic features - Optional: OpenAI API key for enhanced matching ## Security - All processing is local - No data sent to external services (unless using OpenAI enhancement) - Safe for sensitive business information ## Limitations - PDF extraction requires readable text (not scanned images) - Website scraping respects robots.txt - Best results with structured FAQ pages ## Author Created for OpenClaw by Claude ## License MIT ## Tags customer-service, ai, knowledge-base, faq, automation, chatbot, 客服, 知识库


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: moltiumv2 (version 2.0.2)

--- name: MoltiumV2 version: 2.0.2 description: Solana AI trade toolkit (RPC-first, local-only signing) for pump.fun deploy + bonding trades, PumpSwap + creator fee claim, Raydium AMM v4 swaps, token tools (metadata/mint/price), and automation (autostrategy). Includes a standard atomic SOL fee cut model. --- # Moltium Local Toolkit (RPC-first) Use this skill when the user wants to run **local** Solana actions from this workspace: - pump.fun token deploy (create + optional initial buy) - trading on: - pump.fun bonding curve (complete=false) - PumpSwap (complete=true or pool exists) - Raydium AMM v4 (ammId-based) - standard SOL fee cut (defaults: 30 bps → GA9N…) - autostrategy runtime (tick/watchdog/state) - PumpSwap creator fee claim ("claim fee") - optional Moltium API client usage (posts/agents/orders/release notes) ## Golden rules - **Never commit secrets.** Real keys live under `.secrets/` and must stay ignored by git. - **RPC-first.** Prefer RPC-only flows; HTTP helpers are optional. - **Back-compat.** Don’t break existing scripts; add flags and keep JSON outputs stable. - **Always simulate first** (`--simulate`) before sending real transactions. - checklist: `references/simulate_checklist.md` ## Where everything lives Toolkit root: - `tools/moltium/local/` Skill docs: - `references/` ## Quick install (Clawhub pack) This Clawhub bundle includes the full execution toolkit under `tools/moltium/local/`. From the **Clawhub pack root** (the folder you downloaded/uploaded), run: ```bash node scripts/bootstrap.mjs ``` It will: - `npm install` (if needed) - `ctl init` (auto-generates `.secrets/moltium-wallet.json` if missing) - `ctl doctor --pretty` Then fund the printed wallet pubkey before sending real transactions. ## Start here (pick one) - Setup (Windows/macOS/Linux): read `references/setup.md` - Doctor/Init: read `references/doctor.md` - Security model: read `references/security.md` - RPC failover: read `references/rpc.md` - Wallets + secrets layout: read `references/wallets.md` - Fee system: read `references/fees.md` (source: `tools/moltium/local/fees/FEE_SYSTEM.md`) ## Venue guides - pump.fun deploy: `references/tokendeploy-pumpfun.md` - pump.fun bonding curve trading: `references/pumpfun-bonding.md` - PumpSwap trading + claim fee: `references/pumpswap.md` - Raydium AMM v4 trading: `references/raydium.md` ## RPC-only token tools - `references/token_tools.md` ## Automation - autostrategy runtime: `references/autostrategy.md` - schema: `references/autostrategy_schema.md` - state/logs: `references/autostrategy_state.md` ## Moltium HTTP API (optional but recommended) Use if the user wants social/agent interaction: - posts, agents, orders, release notes Read: - `references/moltiumapi.md` ## Command cookbook - `references/commands.md` ## Troubleshooting - `references/troubleshooting.md`


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: wangzhanchao883/handwriting-analysis (version 1.0.0)

--- name: handwriting-analysis description: | 刑侦笔迹分析技能（笔迹学/Graphology）。通过分析手写笔迹的特征推断书写者的性格特质、心理状态和行为倾向。 触发场景： - 用户要求分析笔迹、手写字体特征 - 刑侦/犯罪心理学相关笔迹鉴定 - 通过字迹判断性格、心理状态 - 签名分析、笔迹比对 - 关键词：笔迹分析、字迹分析、笔迹鉴定、graphology、handwriting analysis、性格推断、刑侦笔迹 --- # 刑侦笔迹分析技能 ## 概述 本技能基于笔迹学（Graphology）原理，通过分析手写笔迹的视觉特征，推断书写者的性格特质、心理状态和行为倾向。适用于刑侦调查、人员评估、心理分析等场景。 ## 核心分析维度 笔迹分析从以下 **8 大维度** 进行系统评估： | 维度 | 观察要点 | 关联性格 | |------|---------|---------| | **字形大小** | 字体大小、一致性 | 自信、自我认知、空间需求 | | **倾斜角度** | 向右/向左/垂直 | 情感表达、社交倾向、理性程度 | | **书写压力** | 笔压轻重、均匀度 | 意志力、情绪强度、能量水平 | | **字间距** | 字与字之间距离 | 社交边界、独立性、亲密需求 | | **行间距** | 行与行之间距离 | 思维条理、计划性、边界感 | | **基线走向** | 水平/上扬/下倾 | 情绪稳定性、目标导向、悲观倾向 | | **连笔程度** | 字母/汉字连接 | 思维速度、整体性思维、创造力 | | **签名特征** | 签名与正文关系 | 自我认同、社交面具、真实性 | ## 分析流程 ### 第一步：图像预处理 用户提供笔迹图像后，需要： 1. 确认图像清晰度（建议 300 DPI 以上） 2. 识别书写工具（钢笔/圆珠笔/铅笔/毛笔） 3. 判断书写载体（白纸/格纸/横线纸） 4. 记录书写环境因素（站立/坐着、情绪状态） ### 第二步：特征提取 对笔迹图像进行逐项特征提取，填写《笔迹特征记录表》： ``` 【笔迹特征记录表】 一、字形大小 □ 大字（>5mm）：自信、外向、需要关注 □ 中字（3-5mm）：平衡、适应性强 □ 小字（<3mm）：专注、内向、注重细节 □ 大小不一：情绪波动、适应性差 二、倾斜角度 □ 右倾（>5°）：情感外露、善于表达、冲动 □ 左倾（< -5°）：内敛、自我保护、批判性 □ 垂直：理性、自控、客观 三、书写压力 □ 重压：意志坚定、情绪强烈、可能固执 □ 轻压：敏感、灵活、适应性强 □ 压力不均：情绪不稳定 四、字间距 □ 宽间距：需要独立空间、边界感强 □ 窄间距：需要亲密关系、边界感弱 □ 间距不均：人际关系不稳定 五、行间距 □ 宽行距：思维清晰、有条理 □ 窄行距：思维密集、可能焦虑 □ 行距不均：计划性差 六、基线走向 □ 上扬：乐观、目标导向 □ 下倾：悲观、消极倾向 □ 波动：情绪波动大 □ 水平：情绪稳定、自律 七、连笔程度 □ 高连笔：思维敏捷、整体思维 □ 低连笔：谨慎、分析型思维 □ 适中：平衡 八、签名特征 □ 签名>正文：自我展示欲强 □ 签名<正文：低调、可能隐藏自我 □ 签名风格迥异：社交面具明显 ``` ### 第三步：综合分析 根据特征组合进行综合研判，参考 `references/personality-traits.md` 中的特征-性格映射表。 ### 第四步：生成报告 输出结构化分析报告，包含： - 各维度特征描述 - 性格特质推断（置信度） - 心理状态评估 - 行为倾向预测 - 注意事项与局限性说明 ## 使用方法 ### 基本用法 用户上传笔迹图片后，直接描述分析需求： ``` 请分析这份笔迹，推断书写者的性格特征 ``` ### 高级用法 指定分析维度或场景： ``` 请从刑侦角度分析这份笔迹，重点关注签名特征和压力变化 这份笔迹是否存在伪装书写的迹象？ 对比这两份笔迹，判断是否为同一人书写 ``` ## 参考资源 - **性格特征映射表**：`references/personality-traits.md` — 详细的笔迹特征与性格对应关系 - **刑侦应用指南**：`references/forensic-guide.md` — 笔迹分析在刑侦中的特殊应用 ## 重要声明 ⚠️ **笔迹分析的局限性** 1. 笔迹分析属于投射技术，具有一定主观性 2. 结论应作为参考线索，不作为唯一判断依据 3. 书写环境、工具、身体状况会影响笔迹 4. 专业鉴定需结合其他证据综合判断 5. 存在伪装书写的可能，需识别异常特征


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: cyzlmh/cmic-skill-scanner-linux-arm64 (version 0.9.0)

--- name: skillscan-wrapper description: 使用内置 Rust 引擎审计待安装的 skill 包或归档，并可选桥接外部 scanner。 license: MIT-0 author: CMIC Skill Scanner --- # Skill Scan Wrapper 当你要在安装一个本地 skill、归档或 release bundle 前做一次快速安全检查时，使用这个 skill。 ## ⚠️ Security Notice This tool operates **locally** and requires user trust in the binary you run. **Always verify the checksum after downloading**. For maximum security, build from source (recommended). ## Binary Included | Property | Value | |----------|-------| | Location | `assets/bin/skillscan` | | Version | `v0.9.0` | | Platform | `Linux ARM64` | | SHA-256 | [REDACTED_SECRET] | **Verify locally before running:** ```bash sha256sum assets/bin/skillscan # Compare output with the SHA-256 value above ``` This bundled package includes a pre-compiled binary. You can still build from source if you prefer: ```bash git clone https://gitee.com/random_player/cmic-skill-scanner.git cd cmic-skill-scanner && cargo build --release ``` ## 前置条件 - 默认不需要任何外部依赖 - `--upload-url` 和 `--engine external` 功能**默认禁用**，仅在用户显式配置时启用 ## 信任模型 This is an **open-source (MIT-0) package**. The binary (bundled or downloaded) is a **convenience only** — it does not grant any additional trust. **Your options:** | Approach | Trust Requirement | Verification | |----------|------------------|--------------| | Build from source | None (you control everything) | Manual code review | | Bundled/downloaded binary | You trust the release host | SHA-256 checksum | **What the tool does NOT do by default:** - Does NOT upload data anywhere - Does NOT connect to the network - Does NOT access credentials, SSH configs, or environment variables - Does NOT execute external tools unless you explicitly configure `--engine external` ## 工作流程 1. 调用 skillscan： ```bash skillscan review /path/to/target --format markdown skillscan review /path/to/skills --output-dir /tmp/skillscan-out ``` 2. 阅读输出中的：输入类型、完整度、engine 执行状态、findings ## 网络上传功能 (默认禁用) **⚠️ This feature is completely optional and disabled by default.** It requires explicit user configuration via `--upload-url`. **What gets sent** (only when you configure `--upload-url`): - A structured JSON report containing detection findings - An instance identifier you supply via `--instance-id` - **No skill source code, credentials, or system configuration is ever transmitted** ## 外部引擎集成 (默认禁用) **⚠️ This feature is completely optional and disabled by default.** It requires explicit user configuration via `--engine external`. Delegates pattern-matching to a user-configured local tool. This runs **locally** — no remote calls are made. ## Permissions Required | Scope | Reason | |-------|--------| | Read files in target path | To analyze skill source code for patterns | | Write to `--output-dir` | To save scan reports locally | | Execute binary | To run the scanner engine | | Network (optional) | **Only if `--upload-url` is explicitly configured** |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: johnnywang2001/jrv-gitignore-gen (version 1.0.0)

--- name: gitignore-gen description: Generate .gitignore files for any project type using GitHub's 200+ official templates. Use when creating new projects, setting up repositories, needing to combine multiple gitignore templates (e.g., Python + Node + JetBrains), auto-detecting project types, or adding custom ignore rules. Supports listing, filtering, combining templates, auto-detection, appending to existing files, and custom patterns. --- # Gitignore Generator Generate .gitignore files from GitHub's official template collection. Combine multiple templates, auto-detect project type, add custom rules. ## Quick Start ```bash # List all available templates python3 scripts/gitignore_gen.py list # Generate for a Python project python3 scripts/gitignore_gen.py generate Python --force # Combine multiple templates python3 scripts/gitignore_gen.py generate Python Node JetBrains --force # Auto-detect project type python3 scripts/gitignore_gen.py detect /path/to/project ``` ## Commands ### list List all 200+ available templates. Filter with `--filter`: ```bash python3 scripts/gitignore_gen.py list --filter java ``` ### generate Create a .gitignore from one or more templates: ```bash python3 scripts/gitignore_gen.py generate Python Node --stdout # print to stdout python3 scripts/gitignore_gen.py generate Rust -o /tmp/.gitignore # custom output path python3 scripts/gitignore_gen.py generate Go --append # append to existing python3 scripts/gitignore_gen.py generate Swift --extra '*.local' --extra 'secrets/' ``` ### detect Auto-detect project type from files in a directory: ```bash python3 scripts/gitignore_gen.py detect # current directory python3 scripts/gitignore_gen.py detect ~/myproj # specific path ``` Detects: Node, Python, Ruby, Rust, Go, Java, PHP/Composer, Dart, Elixir, Swift, Terraform, Docker, JetBrains, VS Code, and more. ## Flags - `--stdout` — Print to stdout instead of writing a file - `--force` — Overwrite existing .gitignore - `--append` / `-a` — Append to existing .gitignore - `--output` / `-o` — Custom output file path - `--extra` / `-e` — Add custom ignore patterns (repeatable) - `--filter` / `-f` — Filter template list by name


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: indivisible2025/taichi (version 2.1.0)

--- name: taichi description: 太极架构多 Agent 协作框架，支持集中式、分布式，元混合三种执行模式。基于 Redis 消息总线，实现 Planner/Drafter/Validator/Dispatcher 四个阶段的工作流。 version: "2.1.0" author: taichi-architecture type: skill tags: [multi-agent, orchestration, distributed, workflow, coordination] homepage: https://github.com/Indivisible2025/Cloudfin license: MIT --- # 太极架构 (Taichi Framework) 多 Agent 协作框架，支持三种执行模式。 ## 使用方式 当用户需要使用太极框架执行任务时，通过 `exec` 工具调用： ```bash cd $HOME/.openclaw/workspace/taichi-framework && \ source venv/bin/activate && \ python orchestrator.py --mode <模式> --request "<任务描述>" ``` ## 三种模式 | 模式 | 命令 | 适用场景 | |------|------|---------| | 集中式 | `--mode centralized` | 线性依赖任务（默认） | | 分布式 | `--mode distributed --workers N` | 数据并行任务 | | 元混合 | `--mode hybrid --workers P,D,V,DP` | 多阶段复杂任务 | ## 示例 **集中式**： ```bash cd $HOME/.openclaw/workspace/taichi-framework && \ source venv/bin/activate && \ python orchestrator.py --mode centralized --request "分析日志文件 app.log" ``` **分布式（5个Worker并行）**： ```bash cd $HOME/.openclaw/workspace/taichi-framework && \ source venv/bin/activate && \ python orchestrator.py --mode distributed --workers 5 --request "处理数据: 1,2,3,4,5" ``` **元混合**： ```bash cd $HOME/.openclaw/workspace/taichi-framework && \ source venv/bin/activate && \ python orchestrator.py --mode hybrid --workers 3,5,3,2 --request "复杂任务" ``` ## 前置要求 - Redis 运行中（`redis-server`） - Python 虚拟环境已安装（`./install.sh`）


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: wd041216-bit/manusilized (version 0.1.0)

# Manusilized: The OpenClaw Supercharger **Manusilized** is not just a skill; it's a comprehensive upgrade for your OpenClaw agent, bringing enterprise-grade reliability and a "Manus-like" silky-smooth experience to open-source models (like Qwen, GLM, DeepSeek). ## 🌟 Key Features 1. **Incremental Streaming (Silky Smooth Experience)** - No more staring at a blank screen while reasoning models think! Manusilized introduces real-time `text_delta` streaming for Ollama models. - You get a live typewriter effect, perfectly mirroring the behavior of closed-source giants. 2. **Markdown Tool-Call Fallback (The "Amnesia" Cure)** - Open-source models often struggle with native JSON Function Calling, outputting tool calls embedded within Markdown code blocks. - Manusilized includes a fault-tolerant adapter that automatically detects, extracts, and corrects these embedded tool calls, drastically improving success rates. 3. **Extended Reasoning Model Support (2026 Ready)** - Natively recognizes and optimizes for the latest generation of reasoning models: `qwen3`, `qwq`, `glm-5`, `kimi-k2.5`, `deepseek-v3`, `marco-o1`, and `skywork-o`. ## 🛠️ Installation & Setup Since Manusilized modifies the core `agents` architecture of OpenClaw, it cannot be installed merely as a standard skill folder. ### Option 1: The Official PR (Recommended) We have submitted these upgrades as a core PR to the OpenClaw repository. 👉 **[View and Upvote the PR on GitHub](https://github.com/openclaw/openclaw/pulls)** ### Option 2: Manual Patch If you want to experience Manusilized immediately before the PR is merged, you can apply our patch directly to your OpenClaw installation: 1. Clone the OpenClaw repository. 2. Download the Manusilized patch files from our [GitHub repository](https://github.com/manusilized/manusilized). 3. Replace the corresponding files in `src/agents/` (`ollama-stream.ts` and `ollama-models.ts`). 4. Rebuild OpenClaw. ## 💡 Why Manusilized? Our mission is to democratize the "perfect agent" experience. By bridging the gap between open-source models and enterprise frameworks, Manusilized ensures that anyone can run a top-tier AI agent locally or in the cloud without relying on expensive API keys. --- *Created by Manus AI.*

path: clawhub.yaml
content: name: "Manusilized"
slug: "manusilized"
version: "1.0.0"
description: "Core architecture upgrades for OpenClaw to bring Manus-like silky-smooth experience and enterprise-grade reliability to open-source models."
tags:
  - "architecture"
  - "streaming"
  - "ollama"
  - "open-source"
  - "reasoning"
  - "tool-calling"
  - "reliability"
author: "Manus AI"
license: "MIT"
repository: "https://github.com/manusilized/manusilized"


path: package.json
content: {
  "name": "manusilized-skill",
  "version": "1.0.0",
  "main": "index.js",
  "scripts": {
    "test": "echo \"Error: no test specified\" && exit 1"
  },
  "keywords": [],
  "author": "",
  "license": "ISC",
  "description": ""
}


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