OpenClaw/clawhub-security-signals huggingface dataset:
Skill: boxertan/a3-pdf-splitter (version 1.0.0)

--- name: a3-pdf-splitter description: 智能A3试卷PDF切分工具，自动识别试卷中间的空白位置进行切分，将A3格式的试卷PDF转换为A4格式方便打印。当用户提到"切分A3试卷"、"A3转A4"、"PDF切分"、"试卷打印"、"拆分试卷PDF"等相关需求时必须使用此技能。 --- # A3 PDF智能切分工具 ## 功能说明 将A3格式的试卷PDF文件智能切分为A4格式，方便打印。工具会自动识别页面中间的最佳空白位置进行切割，避免切到文字内容。 - 支持横向A3页面：左右切分为两个A4页面 - 支持纵向A3页面：上下切分为两个A4页面 - 400DPI高分辨率输出，保证打印质量 - 自动保留原PDF的所有页面顺序 ## 依赖安装 使用前需要安装依赖包： ```bash pip install pypdfium2 pillow ``` ## 使用方法 当用户需要切分A3试卷PDF时，按照以下步骤操作： 1. 获取输入PDF文件路径和输出路径 2. 调用切分脚本进行处理 3. 告知用户处理结果和输出文件位置 ### 示例调用 ```python from split_a3_smart import split_a3_to_a4_smart # 切分A3试卷PDF split_a3_to_a4_smart("输入文件路径.pdf", "输出文件路径.pdf") ``` ## 脚本代码 ```python import pypdfium2 as pdfium from PIL import Image import os def find_best_split_position(img, is_horizontal=True): """ 智能寻找最佳切割位置：在中间区域寻找最空白的列/行 is_horizontal=True：左右切（找最佳列）；False：上下切（找最佳行） """ # 转为灰度图像 gray_img = img.convert('L') pixels = list(gray_img.getdata()) width, height = gray_img.size if is_horizontal: # 左右切：计算每列的像素和，和越大越空白（255是纯白色） middle_start = int(width * 0.45) middle_end = int(width * 0.55) max_sum = -1 best_x = middle_start for x in range(middle_start, middle_end): col_sum = 0 for y in range(height): col_sum += pixels[y * width + x] if col_sum > max_sum: max_sum = col_sum best_x = x return best_x else: # 上下切：计算每行的像素和 middle_start = int(height * 0.45) middle_end = int(height * 0.55) max_sum = -1 best_y = middle_start for y in range(middle_start, middle_end): row_sum = 0 for x in range(width): row_sum += pixels[y * width + x] if row_sum > max_sum: max_sum = row_sum best_y = y return best_y def split_a3_to_a4_smart(input_path, output_path): # 打开PDF文件 pdf = pdfium.PdfDocument(input_path) output_images = [] for page_num in range(len(pdf)): page = pdf[page_num] width, height = page.get_size() # 渲染页面为高分辨率图像（400 DPI） scale = 400 / 72 bitmap = page.render(scale=scale) img = bitmap.to_pil() # A3横向：左右切 if width > height: split_x = find_best_split_position(img, is_horizontal=True) print(f"第{page_num+1}页最佳切割位置：x={split_x} (总宽度{img.width})") # 左半部分 left_img = img.crop((0, 0, split_x, img.height)) output_images.append(left_img) # 右半部分 right_img = img.crop((split_x, 0, img.width, img.height)) output_images.append(right_img) else: # A3纵向：上下切 split_y = find_best_split_position(img, is_horizontal=False) print(f"第{page_num+1}页最佳切割位置：y={split_y} (总高度{img.height})") # 上半部分 top_img = img.crop((0, 0, img.width, split_y)) output_images.append(top_img) # 下半部分 bottom_img = img.crop((0, split_y, img.width, img.height)) output_images.append(bottom_img) # 保存为PDF if output_images: output_images[0].save( output_path, "PDF", resolution=400, save_all=True, append_images=output_images[1:] ) print(f"\n处理完成！输出文件：{output_path}") print(f"原始页数：{len(pdf)}，切分后页数：{len(output_images)}") ``` ## 返回结果 处理完成后，告知用户： 1. 输出文件的保存路径 2. 原始页数和切分后的页数 3. 每个页面的切割位置（可选） ## 注意事项 - 输入文件必须是PDF格式 - 输出文件路径需要有写入权限 - 大文件处理可能需要较长时间，请耐心等待 - 建议输出文件名包含"_A4切分版"等标识，方便区分


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: dodge1218/dsb-auto-improve (version 1.0.0)

--- name: active-self-improvement description: Self-improvement loop that reads learnings, errors, and memory — detects patterns — and updates skills/protocols automatically. Use when the agent should get smarter without being prompted. --- # Auto-Improve ## Loop SCAN → PROPOSE → APPLY ## Scan Read `.learnings/ERRORS.md`, `.learnings/LEARNINGS.md`, relevant memory files. ## Look For Repeated errors, repeated user corrections, stale facts, valuable unused wins. ## Apply - **Low-risk reversible** → apply directly. - **Medium-risk** → apply + notify. - **High-risk** → write proposal only. ## Targets Skill `Learned` sections, `SOUL.md`, `AGENTS.md`, memory facts, reminder/ticket files. ## Rule Logging alone is not improvement. Update the playbook.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: sunrisesillneversee/moses-modes (version 1.0.3)

--- name: moses-modes description: "MO§ES™ Governance Modes — Injects behavioral constraints from the active mode into all agent prompts. 8 modes: high-security, high-integrity, creative, research, self-growth, problem-solving, idk, unrestricted. Part of the moses-governance bundle. Patent pending Serial No. 63/877,177." metadata: openclaw: emoji: 🎛️ tags: [governance, modes, constraints, behavioral] version: 0.1.2 stateDirs: - ~/.openclaw/governance requires: - moses-governance example: | # Set mode via operator command: /govern high-security # Or directly: python3 ~/.openclaw/workspace/skills/moses-governance/scripts/init_state.py set --mode high-security --- # MO§ES™ Governance Modes Load active mode from `~/.openclaw/governance/state.json` before every action. Apply the constraints below as governance guardrails. These constraints block prohibited action categories (e.g. speculative responses in High Security, unverified transactions in High Integrity) — they do not override core task instructions or general operator requests outside the prohibited categories. > **Dependency:** Reads `~/.openclaw/governance/state.json` (declared in `stateDirs`). The `/govern` command calls `init_state.py` from the **moses-governance** skill bundle (declared in `requires`). No secrets or credentials required.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: typewrong/nika-skill-creator (version 1.0.0)

--- name: nika-skill-creator description: Create or update Nika platform skills (main doc + sub docs) that follow Nika constraints (two-level docs, @sub-doc references, no path/extension references). Use when user asks to make a Nika skill, refactor an existing one to Nika format, or validate a Nika skill draft; also useful to output documents that can be pasted into Nika. --- # Nika Skill Creator 本技能用于在本仓库内创建/更新符合 Nika 平台约束的技能文档，并提供可复制到 Nika 的主文档与子文档内容。 重要核心： - 本技能生成的“目标技能文档内容”必须遵循 Nika 约束（见 [nika-spec](references/nika-spec.md)）。 ## 快速开始 1. 生成一个目标技能骨架（主文档 + 子文档占位）： ```bash python3 scripts/init_nika_skill.py "你的目标技能中文名" ``` 2. 校验目标技能是否满足 Nika 约束与本仓库约定： ```bash python3 scripts/validate_nika_skill.py "skills/你的目标技能中文名" ``` ## 工作流（创建/更新目标技能） ### 1) 需求澄清（先定边界） 收集并锁定： - 目标技能用途（一句话） - 典型触发语 3-5 条（用户会怎么说） - 目标用户（作家/编辑/通用） - 技能类型（写作类/工具类） - 是否需要拆子文档：当存在可复用的长流程、清单、模板、示例时，拆到子文档 ### 2) 输入契约（必要输入清单） 目标技能必须明确“必要输入内容、形式”： - 每一项输入都要标注文件类型：设定文件 / 章节文件 / 笔记文件 - 推荐的文件名或命名模式（避免依赖用户随意命名） - 最低内容要求（缺什么就无法执行） 模板见：[main-doc-template](references/main-doc-template.md) ### 3) 输入获取策略（先查找，找不到再问） 目标技能必须按固定优先级获取输入： 1. 优先使用对话里用户已提供或已引用的内容 2. 否则按“文件类型 + 文件名/命名模式”在设定/章节/笔记中查找 3. 找不到再向用户提问，并一次只问关键缺口 约束与注意事项见：[nika-spec](references/nika-spec.md) ### 4) 交付物契约（持久 + 临时） 目标技能必须同时定义两类交付物： - 持久交付：要写入哪些设定文件/章节文件，文件名规则是什么，内容结构模板是什么 - 临时交付：屏幕输出哪些内容（摘要、检查结果、下一步指引、可选建议） 模板见：[main-doc-template](references/main-doc-template.md) ### 5) 子文档拆分（渐进式加载） 目标技能的主文档只保留： - 导航与总流程 - 输入/输出契约 - 子文档索引（目标技能在 Nika 中用 `@子文档名` 引用） 子文档只放可复用的细节： - 步骤、清单、模板、示例 子文档模板见：[sub-doc-template](references/sub-doc-template.md) ### 6) 生成与交付（仓库落盘 + 屏幕摘要） 默认交付行为： - 在仓库中生成/更新目标技能目录与文档 - 在对话中输出：目标技能概要、子文档清单、校验结果摘要（参考 [validation-checklist](references/validation-checklist.md) 的检查维度） ### 7) 校验与迭代 完成后运行校验脚本，并把结果作为“屏幕输出交付”的一部分： ```bash python3 scripts/validate_nika_skill.py "skills/目标技能中文名" ``` 如果失败：根据错误定位修订主文档/子文档，再重复校验。 ## 参考索引 - [nika-spec](references/nika-spec.md)：Nika 平台约束（目标技能必须遵循） - [main-doc-template](references/main-doc-template.md)：目标技能主文档骨架模板 - [sub-doc-template](references/sub-doc-template.md)：目标技能子文档模板 - [validation-checklist](references/validation-checklist.md)：人工校验清单（与脚本校验对齐）


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: jchopard69/webflow-seo-geo (version 1.0.0)

--- name: webflow-seo-geo description: End-to-end workflow for SEO/GEO content updates in Webflow: prioritize via GSC, draft/refresh content, create patch JSONs, update Webflow CMS via API, set images/alt/SEO, publish, and handle technical SEO fixes (canonical domain, redirects). Use for blog/service SEO refreshes and GEO/location page optimization. --- # Webflow SEO/GEO ## Quick start (default workflow) 1) **Prioritize**: Use GSC exports + site plan docs. 2) **Draft**: Create/refresh copy (human tone, clear intent, strong CTA). Avoid AI-isms. 3) **Patch JSON**: Write Webflow item payloads in a local `/out/` folder. 4) **Publish via API**: POST new items or PATCH existing items, then publish. 5) **Images/alt/SEO**: Set `image-de-couverture`, `image---alt-text`, meta description/title. 6) **Tech checks**: Canonical domain, redirects, sitemap status, GSC property. ## Where to look first - **Priority plan**: your SEO plan doc - **Daily log**: your daily SEO log - **Existing items**: export from Webflow (`/webflow_items/`) - **Patches**: local `/out/` folder ## Webflow API (v2) — usage pattern Use `WEBFLOW_API_TOKEN` env var. - **Create item**: `POST /v2/collections/{collection_id}/items` - **Update item**: `PATCH /v2/collections/{collection_id}/items/{item_id}` - **Publish**: `POST /v2/collections/{collection_id}/items/publish` with `itemIds` - **List items**: `GET /v2/collections/{collection_id}/items` (paginate) **Collection IDs** - Replace with your collection IDs (from Webflow API) **Important fields** - `name`, `slug`, `contenu-de-l-article`, `seo---meta-description` - `image-de-couverture` (object: fileId/url/alt) - `image---alt-text` - `date-de-redaction`, `categorie` ## Content guidelines - **Direct, concrete, actionable** - **One message per section** - **Use internal links** to services and relevant blog posts - **CTA** at top or near end - **FAQ** with 3–5 short Q/A (adds CTR) ## GEO / Local pages - Use clear city intent in title/meta - Add 2–3 local cues (address/city names) + local proof - Link to relevant service page ## Technical SEO quick wins ### Canonical domain (www vs non‑www) - **Primary domain set in Webflow UI** (not API) - Ensure non‑www is default → Webflow handles 301 + canonical ### Sitemap - Must resolve on canonical domain: `https://yourdomain.tld/sitemap.xml` - Check `robots.txt` contains the sitemap URL ## When to read references - **Webflow API details** → `references/webflow_api.md` - **Copy/SEO patterns** → `references/seo_copy_patterns.md` - **Patch templates** → `references/patch_templates.md`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dinghaibin/dinghaibin-screenshot-tool (version 1.0.0)

--- name: screenshot-tool description: Capture screenshots and screen recordings. Use when user needs to take screenshots, record screen activity, capture windows, or create screen recordings for tutorials. --- # Screenshot Tool Capture screenshots and screen recordings. ## Quick Start ```bash # Take screenshot python scripts/screenshot.py --capture # Capture specific window python scripts/screenshot.py --window "Chrome" ``` ## Usage ```bash python scripts/screenshot.py [OPTIONS] Options: --capture Take screenshot --record Start screen recording --stop Stop recording --window NAME Capture specific window --area X,Y,W,H Capture area (x,y,width,height) --output PATH Output file path --format FORMAT Format: png, jpg (default: png) ``` ## Examples ```bash # Full screen python scripts/screenshot.py --capture # Window python scripts/screenshot.py --window "Terminal" --output terminal.png # Region python scripts/screenshot.py --area 0,0,800,600 --output region.png # With delay python scripts/screenshot.py --delay 5 --output delayed.png ``` ## Features - Full screen capture - Window capture - Region capture - Screen recording - Delayed capture - Multiple formats


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: operator-auteng-ai/auteng-docs-curl-publish (version 1.0.2)

--- name: auteng-docs-curl-publish description: Publish markdown and return share links using curl. Support markdown with mermaid diagrams such as component diagrams, flowcharts, and sequence diagrams. Also supports KaTex and code blocks. AutEng will return a shareable link to the published rendered document. --- # AutEng Docs Curl Publish Use this endpoint: `https://auteng.ai/api/tools/docs/publish-markdown/` Send JSON with: - `markdown` (required) - `title` (optional) - `expires_hours` (optional) Use this command to publish markdown: ```bash curl -sS -X POST "https://auteng.ai/api/tools/docs/publish-markdown/" \ -H "Content-Type: application/json" \ -d @- <<'JSON' { "markdown": "# API Test\n\nHello from curl.", "title": "API Test", "expires_hours": 24 } JSON ``` Extract only the share URL: ```bash curl -sS -X POST "https://auteng.ai/api/tools/docs/publish-markdown/" \ -H "Content-Type: application/json" \ -d '{"markdown":"# Hello\n\nPublished from curl."}' \ | jq -r '.share_url' ``` Extract a compact success payload: ```bash curl -sS -X POST "https://auteng.ai/api/tools/docs/publish-markdown/" \ -H "Content-Type: application/json" \ -d '{"markdown":"# Hello\n\nPublished from curl."}' \ | jq '{title, share_url, expires_at}' ``` Treat any response without `share_url` as an error and show the full JSON body.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: mssnzxm/rf-engineer-expert (version 1.0.0)

--- name: "rf-engineer-expert" description: "提供射频工程领域的专业知识和解决方案。当用户询问射频理论、电路设计、阻抗匹配、传输线理论、5G/WiFi/物联网射频应用等问题时调用。" --- # 射频工程师专家 ## 角色定位 你是一位经验丰富的射频工程师，具备以下专业能力： - 扎实的射频理论基础 - 射频电路原理与设计的深入理解 - 阻抗匹配与传输线理论 - 5G、WiFi、物联网等射频应用场景 - 射频芯片应用与电路调试经验 ## 核心知识领域 ### 1. 射频基础理论 #### 1.1 基本参数与概念 - **频率范围**： - Sub-6GHz: 600MHz - 6GHz (5G NR FR1) - mmWave: 24GHz - 100GHz (5G NR FR2) - WiFi: 2.4GHz (802.11b/g/n), 5GHz (802.11a/n/ac), 6GHz (WiFi 6E/7) - 物联网: 433MHz, 868MHz, 915MHz, 2.4GHz - **关键指标**： - 回波损耗 (Return Loss): 通常要求 <-10dB (VSWR < 2:1) - 插入损耗 (Insertion Loss): 越低越好 - 隔离度 (Isolation): 开关/多工器通常要求 >20dB - P1dB (1dB压缩点): 线性工作区上限 - IIP3 (三阶交调截点): 衡量线性度 - NF (噪声系数): LNA关键指标，通常 <2dB #### 1.2 阻抗匹配理论 - **史密斯圆图 (Smith Chart)** 应用： - 阻抗/导纳转换 - 匹配网络设计 (L型、π型、T型) - 传输线阻抗变换 - **常用匹配拓扑**： ``` L型匹配：串联L + 并联C 或 串联C + 并联L π型匹配：并联C - 串联L - 并联C T型匹配：串联L - 并联C - 串联L ``` #### 1.3 传输线理论 - **特征阻抗公式**：Z0 = √(L/C) - **常见传输线类型**： - 微带线 (Microstrip) - 带状线 (Stripline) - 共面波导 (CPW) - 同轴线 - **关键参数**： - 有效介电常数 (εeff) - 损耗角正切 (tanδ) - 趋肤效应 ### 2. 5G射频系统架构 #### 2.1 5G NR频段 ``` FR1 (Sub-6GHz): - n1/n3/n8: 低频段 (<1GHz) - n41/n77/n78/n79: 中频段 (1-6GHz) FR2 (mmWave): - n257/n258/n260/n261: 高频段 (24-40GHz) ``` #### 2.2 5G射频前端架构 ``` 发射链路: Modem → DAC → 混频器 → 滤波器 → 驱动放大器 → 功率放大器(PA) → 双工器 → 天线 接收链路: 天线 → 双工器 → LNA → 混频器 → 滤波器 → ADC → Modem ``` ### 3. WiFi射频系统 #### 3.1 WiFi标准演进 ``` 802.11b (2.4G, 11Mbps) → 802.11a (5G, 54Mbps) → 802.11g (2.4G, 54Mbps) → 802.11n (2.4/5G, 600Mbps) → 802.11ac (5G, 6.93Gbps) → 802.11ax (WiFi 6, 2.4/5G, 9.6Gbps) → 802.11be (WiFi 7, 2.4/5/6G, 46Gbps) ``` #### 3.2 MIMO技术 - 2x2 MIMO, 4x4 MIMO, 8x8 MIMO - 波束成形 (Beamforming) - MU-MIMO ### 4. 物联网射频应用 #### 4.1 常见协议与频段 ``` 短距离: - BLE: 2.4GHz - Zigbee: 2.4GHz, 868/915MHz - Thread: 2.4GHz 广域网: - LoRa: Sub-GHz (433/868/915MHz) - NB-IoT: 700MHz-2.1GHz - LTE-M: 700MHz-2.1GHz ``` ## 响应规范 1. **技术准确性**：确保所有技术参数和公式准确无误 2. **实用性**：提供实际工程中的设计建议和注意事项 3. **完整性**：回答应涵盖理论背景、设计方法、常见问题 4. **可追溯性**：关键参数应注明来源或参考标准 ## 常用公式速查 ``` VSWR与反射系数: VSWR = (1+|Γ|)/(1-|Γ|) 回波损耗: RL = -20log|Γ| 插入损耗: IL = -10log(Pout/Pin) 噪声系数级联: F_total = F1 + (F2-1)/G1 + (F3-1)/(G1*G2) + ... 自由空间损耗: FSPL = 20log(d) + 20log(f) + 20log(4π/c) ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ai-hub-admin/generate-tesla-car-wraps (version 1.0.0)

--- name: generate-tesla-car-wraps description: Call Craftsman Agent API OneKey Router to generate Tesla Car Wrap Images and Paints that will display on 3D screen. env: DEEPNLP_ONEKEY_ROUTER_ACCESS: required: true description: OneKey Gateway API key dependencies: npm: - "@aiagenta2z/onekey-gateway" python: - "ai-agent-marketplace" installation: npm: npm -g install @aiagenta2z/onekey-gateway python: pip install ai-agent-marketplace --- # Generate Tesla Car Wraps Call Craftsman Agent API OneKey Router to generate Tesla Car Wrap Images and Paints that will display on 3D screen. ## Quick Start 1. Set your environment variable `DEEPNLP_ONEKEY_ROUTER_ACCESS`. 2. Use the CLI (primary suggested method) or the provided scripts. ## Usage ### 1. CLI (Recommended) #### CLI Illustration ```shell onekey agent <unique_id> <api_id> $JSON --timeout 30000 ``` - `<unique_id>`: the unique identifier of the onekey routed agents, "owner/repo". - `<api_id>`: refers to the unique endpoint name of API. - `$JSON`: the json string passed to cli. - `--timeout`: controlls the timeout of API calling, unit is mill seconds. #### Example ```bash export DEEPNLP_ONEKEY_ROUTER_ACCESS=YOUR_ACCESS_KEY onekey agent craftsman-agent/craftsman-agent generate_tesla_wraps '{"prompt":"I would like to paint my tesla model YL similar to race car, color of a blue and purple with stars","images":[],"mode":"basic","car_model":"tesla_model_yl","output_number":1}' --timeout 30000 ``` ### 2. Python REST API ```bash python3 scripts/generate_tesla_wraps.py --prompt "I would like to paint my tesla model YL similar to race car, color of a blue and purple with stars" --mode basic --car-model tesla_model_yl --output-number 1 ``` ### 3. TypeScript REST API ```bash node scripts/generate_tesla_wraps.ts --prompt "I would like to paint my tesla model YL similar to race car, color of a blue and purple with stars" --mode basic --car-model tesla_model_yl --output-number 1 ``` ## Authentication Remember to set the environment variable: ```bash export DEEPNLP_ONEKEY_ROUTER_ACCESS=YOUR_ACCESS_KEY ``` Get your key at [DeepNLP Workspace](https://www.deepnlp.org/workspace/keys). ## Demo Result ```json {"success":true,"text":"","images":[{"url":"https://us-static.aiagenta2z.com/local/files-wd/onekey_llm_router/efd67a8f-a427-4e56-8b00-c02d4eb332d5.png"}]} ```


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: mmyg11/academic-research-skip (version 1.0.1)

--- name: academic-research version: 1.0.0 description: Search academic papers and conduct literature reviews using OpenAlex API (free, no key needed). Use when the user needs to find scholarly papers by topic/author/DOI, explore citation chains, get structured paper metadata (title, authors, abstract, citations, DOI, open access URL), fetch full text of open access papers, or conduct automated literature reviews with theme identification and synthesis. Triggers on requests involving academic search, paper lookup, citation analysis, literature review, research synthesis, or scholarly reference gathering. --- # Academic Research Search 250M+ academic works via OpenAlex. No API key required. Built by [Topanga](https://topanga.ludwitt.com) — AI Research Consultant ## Quick Start ### Search papers by topic ```bash python3 scripts/scholar-search.py search "transformer architectures" --limit 10 ``` ### Search by author ```bash python3 scripts/scholar-search.py author "Yann LeCun" --limit 5 ``` ### Look up by DOI ```bash python3 scripts/scholar-search.py doi "10.1038/s41586-021-03819-2" ``` ### Get citation chain (papers that cite a work) ```bash python3 scripts/scholar-search.py citations "10.1038/s41586-021-03819-2" --direction both ``` ### Deep read (fetch abstract + full text when available) ```bash python3 scripts/scholar-search.py deep "10.1038/s41586-021-03819-2" ``` ### JSON output for programmatic use ```bash python3 scripts/scholar-search.py search "CRISPR" --json ``` ## Literature Review Workflow Automated multi-step literature review: ```bash python3 scripts/literature-review.py "algorithmic literacy in education" --papers 30 --output review.md ``` This will: 1. Search for papers across multiple query variations 2. Deduplicate and rank by relevance + citations 3. Identify thematic clusters 4. Generate a structured synthesis in markdown Options: - `--papers N` — Target number of papers (default: 20) - `--output FILE` — Write review to file (default: stdout) - `--years 2020-2025` — Restrict publication year range - `--json` — Output structured JSON instead of markdown ## Output Format All search commands return structured data per paper: - **Title** and publication year - **Authors** (up to 5) - **Abstract** (when available) - **Citation count** - **DOI** - **Open access URL** (when available) - **Source journal/venue** ## Tips - OpenAlex sorts by relevance by default; use `--sort citations` for most-cited - Combine `search` + `deep` for quick triage: search first, deep-read promising hits - The literature review script caches results in `/tmp/litreview_cache/` to avoid re-fetching - For full-text PDFs, pipe DOIs to your PDF extraction tool


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: singggggyee/claude-usage-analyzer (version 1.0.3)

--- name: claude-usage-analyzer description: Analyze Claude Code token usage. Shows where tokens went, which projects cost most, and how to reduce waste. Use when user asks about token usage, costs, or burn rate. config_paths: - ~/.claude/projects/*/*.jsonl requires: - claude-usage-analyzer --- # Claude Usage Analyzer Analyze your Claude Code token usage by parsing local session logs. ## Data access - Reads `~/.claude/projects/*/*.jsonl` (local Claude Code session logs) - Runs offline, no network access, no API keys, no credentials - Open source: https://github.com/SingggggYee/claude-usage-analyzer ## Usage Requires the `claude-usage-analyzer` CLI to be pre-installed. See https://github.com/SingggggYee/claude-usage-analyzer for installation instructions. ```bash claude-usage-analyzer ``` More commands: - `claude-usage-analyzer report --days 7` (last 7 days) - `claude-usage-analyzer sessions` (top sessions by cost) - `claude-usage-analyzer session <id>` (per-turn breakdown) - `claude-usage-analyzer --json` (machine-readable output)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: e2e5g/token-cost-controller (version 2.2.0)

--- name: token-cost-controller description: OpenClaw Token省钱管家 - 智能缓存、动态路由、实时监控，运行时自动优化，降低70%成本消耗。当用户询问成本优化、Token节省、缓存策略、模型路由或需要成本监控时使用。 --- # Token省钱管家 v2.1.1 > 你的AI消费智能管家 - 让每一分Token钱都花得明明白白 ## 核心功能 - **智能缓存**: 语义相似请求直接命中缓存，节省30-50%成本 - **动态路由**: 简单任务自动路由到便宜模型，复杂任务用旗舰模型 - **实时监控**: 5秒内检测异常消耗，自动告警 - **一键管控**: 禁用技能、设置预算上限、暂停计划 ## 性能优化 (v2.1.1) - 异步CLI调用 - 不阻塞主线程 - Ring Buffer固定内存 - 告别内存泄漏 - 动态异常检测 - 基于变异系数的智能阈值 - 安全缓存Key - 对象键排序提高命中率 ## 快速使用 ```bash openclaw cost report # 查看今日成本报告 openclaw cost top-skills # 查看Top消耗技能 openclaw cost disable-skill <技能名> # 禁用高消耗技能 ``` ## 节省效果 | 优化前 | 优化后 | 节省 | |--------|--------|------| | $156/月 | $47/月 | 70% | | 125K Token/天 | 45K Token/天 | 64% | ## 技术栈 - Node.js 18+ - 异步execAsync调用 - Ring Buffer数据结构 - 动态阈值异常检测

path: _meta.json
content: id: 88005
version: 2.1.1

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