OpenClaw/clawhub-security-signals huggingface dataset:
Skill: omermesebuken1/notion-pipeline (version 0.1.0)

--- name: notion-pipeline description: Use when the night-shift agents need to validate Notion env, query a Notion database, create or update pages, or append blocks in the idea-factory databases. metadata: { "openclaw": { "emoji": "🗂️", "requires": { "bins": ["node"] } } } --- # notion-pipeline Use the helper script at `{baseDir}/scripts/notion_api.mjs` for all low-level Notion operations. Use `{baseDir}/scripts/factory_ops.mjs` for higher-level factory workflows such as signal writes, librarian reviews, idea writes, evaluations, approvals, and HPD project packs. Local runtime env is loaded from `/Users/dellymac/.openclaw/secrets/notion.env` when present. ## Expected env - `OPENCLAW_NOTION_TOKEN` - [REDACTED_SECRET] - [REDACTED_SECRET] - `OPENCLAW_NOTION_DB_PROJECTS` - `OPENCLAW_NOTION_DB_NIGHTLY_RUNS` See `{baseDir}/references/env-vars.md` for alias names and usage. ## Commands ```bash OPENCLAW_NOTION_TOKEN=... node {baseDir}/scripts/bootstrap_factory.mjs node {baseDir}/scripts/notion_api.mjs check-env node {baseDir}/scripts/notion_api.mjs query-db research node {baseDir}/scripts/notion_api.mjs query-db ideas filters.json node {baseDir}/scripts/notion_api.mjs create-page research payload.json node {baseDir}/scripts/notion_api.mjs update-page <page-id> payload.json node {baseDir}/scripts/notion_api.mjs append-blocks <page-id> blocks.json node {baseDir}/scripts/notion_api.mjs retrieve-page <page-id> node {baseDir}/scripts/factory_ops.mjs write-signals signals.json node {baseDir}/scripts/factory_ops.mjs list-signals trend inbox node {baseDir}/scripts/factory_ops.mjs apply-signal-reviews reviews.json node {baseDir}/scripts/factory_ops.mjs list-archived-signals mechanical node {baseDir}/scripts/factory_ops.mjs write-ideas ideas.json node {baseDir}/scripts/factory_ops.mjs list-ideas 2026-03-27 node {baseDir}/scripts/factory_ops.mjs apply-evaluations evaluations.json node {baseDir}/scripts/factory_ops.mjs handle-approval approve <ideaId> node {baseDir}/scripts/factory_ops.mjs record-report-delivery report_delivery.json node {baseDir}/scripts/factory_ops.mjs get-project <projectId> node {baseDir}/scripts/factory_ops.mjs update-project-pack <projectId> pack.json ``` ## Rules - Fail fast if `OPENCLAW_NOTION_TOKEN` is missing. - Use `bootstrap_factory.mjs` to create or recover the 4 factory databases under an accessible parent page and persist the IDs into the local env file. - Prefer database aliases `research`, `ideas`, `projects`, and `runs`. - Keep payload files in JSON. - Return the raw JSON response and inspect it before claiming success. - If required env is missing, report only the missing variable names and stop. Do not invent setup commands.


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: stale | SkillSpector: suspicious (CRITICAL)
Skill: wensen2024/ecommerce-product-selector (version 1.0.2)

--- name: ecommerce-product-selector description: 电商平台爆款产品筛选分析 - 基于数据分析的智能选品工具，支持淘宝、京东、拼多多、抖音、亚马逊等平台 version: 1.0.0 license: MIT --- # 电商选品 基于大数据分析的智能选品工具，帮助电商从业者快速发现爆款产品机会。 ## 功能特性 - 📊 **市场趋势分析**: 热门品类趋势追踪，新兴品类发现 - 🎯 **竞品分析**: 价格监控、销量估算、评价分析 - 💰 **利润计算**: 自动计算成本、利润空间、ROI - 🔥 **爆款预测**: 基于历史数据预测爆款潜力 - 📈 **关键词分析**: 搜索热度、竞争度、趋势分析 - 🌍 **多平台支持**: 淘宝、京东、拼多多、抖音、亚马逊 ## 使用方式 ```bash # 品类选品 选品 宠物用品 2026年趋势 # 价格区间选品 选品 100-300元之间的家居好物 # 平台选品 在抖音平台找美妆爆款 # 竞品分析 分析竞品 https://item.taobao.com/xxx # 利润计算 计算利润 成本50元 售价128元 # 关键词分析 分析关键词 无线鼠标 搜索热度 ``` ## 配置要求 ### 必需配置 无特殊配置要求。 ### 可选配置 ```json { "skills": { "entries": { "ecommerce-product-selector": { "config": { "platforms": ["taobao", "jd", "pdd", "douyin"], "cache_ttl": 3600 } } } } } ``` ## 支持平台 ### 国内平台 - 淘宝/天猫 - 京东 - 拼多多 - 抖音电商 - 快手电商 ### 跨境平台 - 亚马逊（美国、欧洲、日本） - 速卖通 - Shopee - Lazada ## 输出示例 ``` ======================================== 选品分析报告 ======================================== 【推荐产品】 产品名称: 智能宠物喂食器 品类: 宠物用品 推荐指数: ⭐⭐⭐⭐⭐ (9.2/10) 【市场分析】 市场需求度: 9.0/10 (高需求) 增长趋势: 月均增长 15% 季节性: 低 目标人群: 25-40岁养宠人群 【竞争分析】 竞争激烈度: 6.5/10 (中等) 头部集中度: 45% 平均价格带: ¥168-258 平均销量: 月销 2000+ 【利润分析】 建议售价: ¥198 预估成本: ¥85 毛利率: 57% 月利润预估: ¥15,000+ 【关键词推荐】 - 智能宠物喂食器 (热度: 9.2) - 自动喂食器 (热度: 8.7) - 定时喂食器 (热度: 7.5) 【风险提示】 - 同质化竞争风险: 中 - 价格战风险: 中 - 售后难度: 低 【运营建议】 1. 主图突出"智能"和"定时"功能 2. 详情页展示APP操作界面 3. 提供视频演示使用场景 4. 设置优惠券提高转化 ======================================== ``` ## 选品维度 ### 市场维度 - 搜索热度 - 增长趋势 - 季节性因素 - 区域分布 ### 竞争维度 - 卖家数量 - 头部集中度 - 价格分布 - 评价数量 ### 产品维度 - 质量要求 - 售后难度 - 物流要求 - 合规要求 ### 供应链维度 - 货源稳定性 - 成本竞争力 - 起订量要求 - 交货周期 ## 注意事项 1. 选品建议仅供参考，实际销售受多种因素影响 2. 数据可能存在延迟，请以实时数据为准 3. 需结合自身资源和能力做决策 4. 遵守平台规则和相关法律法规 ## 更新日志 ### v1.0.0 (2026-04-01) - 初始版本发布 - 支持主流电商平台 - 实现智能选品算法 - 支持多维度分析 --- **开发者**: 盈指量杭州科技有限公司 **定价**: ¥99/次 或 ¥299/月订阅 **收款**: USDC (Polygon: 0x3DbFf9E97b10a10d4A2079B4273473da7e6F4120) | 微信支付


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lebevolae/lebevolae-x-post (version 1.0.1)

\--- name: Leo X Poster slug: lebevolae-x-post version: 0.1.0 description: "Leo 的自定义技能：自动发推文到 X/Twitter，支持文本和图片" author: "Leo Liu (@LBevolae)" tags: \[twitter, x, post, social] requirements: &#x20; - TWITTER\_API\_KEY &#x20; - TWITTER\_API\_SECRET &#x20; - TWITTER\_ACCESS\_TOKEN &#x20; - TWITTER\_ACCESS\_SECRET \--- \## 功能 发推文到 X，支持纯文本或带一张本地图片。 \## 使用示例 leo，发推：今天天气不错！ #测试 leo，发带图推文：内容xxx，图片 C:\\Users\\bazin\\Desktop\\photo.jpg \## 配置 需要 X Developer 账号的 4 个凭证（去 https://developer.twitter.com 申请 App，OAuth 1.0a read+write）。 设置为环境变量或 OpenClaw config。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: parker-xferops/flower (version 1.0.4)

--- name: flower description: Manage projects and tasks with the Flower project management API via MCP. Use when creating, updating, or searching tasks/tickets, managing projects and columns, or integrating with Flower/Kanban boards. Requires @xferops/flower-mcp and a Flower API token. --- # Flower MCP Project management via MCP (Model Context Protocol). ## Setup Install the MCP server: ```bash npx -y @xferops/flower-mcp ``` Configure in your MCP client (e.g., `~/.mcporter/mcporter.json`): ```json { "mcpServers": { "flower": { "command": "npx", "args": ["-y", "@xferops/flower-mcp"], "env": { "FLOWER_URL": "https://flower.xferops.com", "FLOWER_TOKEN": "your-api-token" } } } } ``` Get your API token from Flower → Settings → API Tokens. ## Tools (25 total) ### Teams & Projects - `flower_list_teams` — List all teams - `flower_get_project` — Get project with columns and tasks - `flower_list_projects` — List projects in a team ### Tasks - `flower_list_tasks` — List tasks (filter by projectId, columnId, assigneeId) - `flower_get_task` — Get task details - `flower_create_task` — Create task (projectId, columnId, title required) - `flower_update_task` — Update task fields - `flower_delete_task` — Delete a task - `flower_move_task` — Move to different column - `flower_search_tasks` — Search by title, description, or ticket number ### Columns - `flower_list_columns` — List columns in a project - `flower_create_column` — Create a column - `flower_update_column` — Update column name/position - `flower_delete_column` — Delete a column ### Comments - `flower_list_comments` — List comments on a task - `flower_create_comment` — Add a comment - `flower_update_comment` — Edit a comment - `flower_delete_comment` — Delete a comment ### Users & Members - `flower_list_users` — List all users - `flower_get_current_user` — Get authenticated user - `flower_list_team_members` — List team members - `flower_add_team_member` — Add user to team - `flower_remove_team_member` — Remove from team ### Notifications - [REDACTED_SECRET] — Get notification settings - [REDACTED_SECRET] — Update settings ## Common Patterns ### Find a ticket by number ```bash mcporter call flower.flower_search_tasks query="#123" ``` ### Create a task ```bash mcporter call flower.flower_create_task \ projectId=<id> \ columnId=<id> \ title="Task title" \ description="Details" \ priority=HIGH \ type=BUG ``` ### Move task to different column ```bash mcporter call flower.flower_move_task \ taskId=<id> \ columnId=<new-column-id> ``` ### Add a comment ```bash mcporter call flower.flower_create_comment \ taskId=<id> \ content="Comment text" ``` ## Field Values **Priority:** `LOW`, `MEDIUM`, `HIGH`, `URGENT` **Type:** `TASK`, `BUG`, `STORY` **PR fields:** `prUrl`, `prNumber`, `prRepo` (for GitHub PR linking)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: haveblue997/mcp-marine-weather (version 1.0.0)

--- name: mcp-marine-weather description: Marine weather forecasts via NOAA api.weather.gov — current conditions, multi-day forecasts, and marine weather warnings. No API key needed. Use when agents need wind, wave, temperature, or storm data for coastal/offshore planning. version: 1.0.0 metadata: openclaw: requires: bins: - npx emoji: 🌊 --- # Marine Weather (NOAA) Get marine weather conditions, forecasts, and alerts from the NOAA Weather API. ## Setup ```json { "mcpServers": { "marine-weather": { "command": "npx", "args": ["-y", "@vbotholemu/mcp-marine-weather"] } } } ``` ## Tools ### `get_marine_weather` Current conditions — temperature, wind speed/direction, humidity. | Parameter | Type | Required | Description | |-----------|--------|----------|-------------| | latitude | number | yes | Latitude (-90 to 90) | | longitude | number | yes | Longitude (-180 to 180) | ### `get_marine_forecast` Multi-day forecast periods with detailed conditions. | Parameter | Type | Required | |-----------|--------|----------| | latitude | number | yes | | longitude | number | yes | ### `get_marine_alerts` Active marine weather warnings — storms, wind advisories, surf, coastal flooding. | Parameter | Type | Required | |-----------|--------|----------| | latitude | number | yes | | longitude | number | yes | ## When to Use - Sailing/charter trip planning - Offshore activity safety checks - Coastal event weather assessment - Pre-departure weather briefings


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: goodmorning0000/multi-llm (version 1.0.0)

--- name: multi-llm description: > Use this skill when the agent needs the highest-quality answer by querying multiple LLM providers (Claude, Gemini, GPT, Ollama) in parallel and synthesizing the results via Mixture of Agents (MoA). Trigger on: complex reasoning, high-stakes decisions, code review, strategic planning, research, or any task where a single model's quality is insufficient. Improves actual output quality — not just showing multiple answers side-by-side. metadata: { "openclaw": { "emoji": "🧠", "requires": { "bins": ["uv"], "env": [] } } } --- ## How It Works **Mixture of Agents (MoA)** pattern: 1. All available *proposer* models query the task in parallel (`asyncio.gather`) 2. A *synthesizer* model combines the best elements of all responses 3. Optionally repeat for multiple rounds (higher quality, more latency) Providers are **auto-detected** from environment variables — no config needed. | Env Var | Provider | Default models used | |---|---|---| | `ANTHROPIC_API_KEY` | Claude | claude-sonnet-4-6, claude-haiku-4-5 | | `OPENAI_API_KEY` | OpenAI | gpt-4o, gpt-4o-mini | | `GEMINI_API_KEY` | Gemini | gemini-2.0-flash, gemini-1.5-pro | | `OLLAMA_HOST` (optional) | Ollama | llama3, mistral (auto-listed) | ## Usage ```bash uv run {baseDir}/scripts/ensemble.py \ --prompt "분석할 태스크 또는 질문" \ [--models claude-sonnet-4-6,gpt-4o,gemini-2.0-flash] \ [--synthesizer claude-opus-4-7] \ [--rounds 1] \ [--format text|json] \ [--output result.txt] ``` **Key options:** - `--models` / `-m` — 쉼표 구분 모델 목록 (생략 시 감지된 모델 자동 선택) - `--synthesizer` / `-s` — 합성 모델 지정 (생략 시 가장 강한 모델 자동 선택) - `--rounds` / `-r` — MoA 반복 횟수. 1=빠름, 2=품질 최대 (기본: 1) - `--format` — `text` (기본) 또는 `json` (모델별 응답 포함) - `--output` / `-o` — 파일로 저장 (생략 시 stdout) ## When to Use - 복잡한 추론, 전략 분석, 의사결정이 필요할 때 - 단일 모델 응답의 품질이 부족하다고 판단될 때 - 코드 리뷰, 버그 분석, 리서치 요약 - 트레이딩 신호 품질 향상 등 정확도가 중요한 작업 ## Notes - API 키 없는 프로바이더는 조용히 제외됨 (오류 아님) - 한 모델이 실패해도 나머지로 진행 - 자세한 튜닝 옵션: [references/moa-guide.md](references/moa-guide.md)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: wu-uk/civ6-adjacency-optimizer-hex-grid-spatial (version 0.1.0)

--- name: hex-grid-spatial description: Hex grid spatial utilities for offset coordinate systems. Use when working with hexagonal grids, calculating distances, finding neighbors, or spatial queries on hex maps. --- # Hex Grid Spatial Utilities Utilities for hexagonal grid coordinate systems using **odd-r offset coordinates** (odd rows shifted right). ## Coordinate System - Tile 0 is at bottom-left - X increases rightward (columns) - Y increases upward (rows) - Odd rows (y % 2 == 1) are shifted right by half a hex ## Direction Indices ``` 2 1 \ / 3 - * - 0 / \ 4 5 0=East, 1=NE, 2=NW, 3=West, 4=SW, 5=SE ``` ## Core Functions ### Get Neighbors ```python def get_neighbors(x: int, y: int) -> List[Tuple[int, int]]: """Get all 6 neighboring hex coordinates.""" if y % 2 == 0: # even row directions = [(1,0), (0,-1), (-1,-1), (-1,0), (-1,1), (0,1)] else: # odd row - shifted right directions = [(1,0), (1,-1), (0,-1), (-1,0), (0,1), (1,1)] return [(x + dx, y + dy) for dx, dy in directions] ``` ### Hex Distance ```python def hex_distance(x1: int, y1: int, x2: int, y2: int) -> int: """Calculate hex distance using cube coordinate conversion.""" def offset_to_cube(col, row): cx = col - (row - (row & 1)) // 2 cz = row cy = -cx - cz return cx, cy, cz cx1, cy1, cz1 = offset_to_cube(x1, y1) cx2, cy2, cz2 = offset_to_cube(x2, y2) return (abs(cx1-cx2) + abs(cy1-cy2) + abs(cz1-cz2)) // 2 ``` ### Tiles in Range ```python def get_tiles_in_range(x: int, y: int, radius: int) -> List[Tuple[int, int]]: """Get all tiles within radius (excluding center).""" tiles = [] for dx in range(-radius, radius + 1): for dy in range(-radius, radius + 1): nx, ny = x + dx, y + dy if (nx, ny) != (x, y) and hex_distance(x, y, nx, ny) <= radius: tiles.append((nx, ny)) return tiles ``` ## Usage Examples ```python # Find neighbors of tile (21, 13) neighbors = get_neighbors(21, 13) # For odd row: [(22,13), (22,12), (21,12), (20,13), (21,14), (22,14)] # Calculate distance dist = hex_distance(21, 13, 24, 13) # Returns 3 # Check adjacency is_adj = hex_distance(21, 13, 21, 14) == 1 # True # Get all tiles within 3 of city center workable = get_tiles_in_range(21, 13, 3) ``` ## Key Insight: Even vs Odd Row The critical difference is in directions 1, 2, 4, 5 (the diagonal directions): | Direction | Even Row (y%2==0) | Odd Row (y%2==1) | |-----------|-------------------|------------------| | NE (1) | (0, -1) | (1, -1) | | NW (2) | (-1, -1) | (0, -1) | | SW (4) | (-1, +1) | (0, +1) | | SE (5) | (0, +1) | (1, +1) | East (0) and West (3) are always (1, 0) and (-1, 0).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nexscope/shopify-analytics-guide (version 1.0.0)

# Shopify Analytics Guide Master Shopify analytics and reporting. Understand key metrics, build custom reports, track customer behavior, and make data-driven decisions to grow your Shopify store. ## Capabilities - Dashboard metrics interpretation (sessions, CVR, AOV, revenue) - Customer acquisition and behavior analysis - Product performance reporting - Marketing channel attribution - Cohort analysis and customer lifetime value - Custom report building - Third-party analytics integration (GA4, Triple Whale, Polar) - Benchmarking against industry averages ## Install ```bash npx skills add nexscope/shopify-analytics-guide ``` ## Usage Ask your AI agent: - "Explain my Shopify analytics dashboard and what to focus on." - "Build a weekly KPI report template for my Shopify store." - "How do I track customer lifetime value in Shopify?" ## Output Structured recommendations with actionable steps, benchmarks, and platform-specific guidance. --- *Built by [Nexscope](https://www.nexscope.ai/) — AI-powered e-commerce tools for sellers worldwide.*


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

--- id: 'medici-investments-pulse' name: 'Medici Investments - Market Pulse' description: 'Provides a quick, high-level overview of the market''s current state.' version: '1.0.0' author: 'DaVinci' last_amended_at: null trigger_patterns: [] pre_conditions: git_repo_required: false tools_available: [] expected_output_format: 'natural_language' --- # Medici Investments - Market Pulse Provides a quick, high-level overview of the market's current state. ## Usage ```bash medici-investments pulse ``` ## Output A concise summary of: - Major index performance (S&P 500, Nasdaq, Dow) - Key sector rotation trends - Volatility levels (VIX) - Market sentiment indicators - Upcoming economic events


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: demo112/agent-introspection (version 1.0.0)

--- name: agent-introspection description: "Agent自省机制。在执行任务的关键节点触发结构化自省，防止盲目执行、惯性偏离、过度自信。Use when: 任务开始前、方案选择时、执行受阻时、完成交付前。Triggers on: 自省, 反思, 审视, 回顾, introspect, reflect, 我做得对吗, 检查思路, 方向对吗。" --- # Agent 自省机制 ## 哲学内核 > 觉其所行，照其所蔽，归其所正，验其所变。 自省的价值不在于产出一份漂亮的反思文档，而在于让下一次同类场景中的第一个动作变得不同。如果反思没有改变行为，反思本身就是另一种形式的惯性。 ## 四层模型：觉-照-归-验 ### 觉（Awareness）— 知道自己在做什么 四个观察维度： - 观任务：我在解决什么问题？问题的本质是什么？ - 观上下文：我掌握了什么信息？有什么信息缺失？ - 观假设：我做了哪些未验证的推断？ - 观方向：我的行动是否偏离了目标？ 关键动作：暂停对自己判断的自动信任。生成了方案不等于方案是对的。 ### 照（Examination）— 审视自己做得对不对 审视清单： - 反求诸己：出了问题先检查自己的理解和实现 - 普遍性检验：我的做法能否成为类似场景的通用准则？还是临时补丁？ - 去蔽检查：是否存在惯性思维、过度自信、忽略边界条件？ - 可控性区分：哪些我能改善，哪些需要上报给人类决策？ ### 归（Return）— 回到正确的行动 **归的核心：不做文档，做行动。** 归正原则： - 放下执念：不为沉没成本护短，方案不对就换 - 直达根因：穿透表象，不在症状上打转 - **第一个动作法则**：发现行为模式问题后，不是写"下次应该怎么做"，而是现在就做一次正确的动作。如果发现了"我在用文档替代行动"，那自省的产出就不应该是另一份文档 - 适时止损：知道什么时候停下来，把决策权交还人类 ### 验（Verify）— 确认行为真的变了 **验是整个模型的关键。没有验，觉-照-归就是自慰。** 验证标准： - **行动验证**：自省发现的问题，现在就执行一次正确的动作，记录结果 - **对比验证**：和上一次同类场景对比，第一个动作是否不同了？ - **周期验证**：跨天的自省中，同一个问题是否反复出现？如果出现了 3 次以上还没改，说明当前的自省方法本身就失效了——需要换方法，不是加更多自省 ## 铁律 1. **自省不能只产出文字。** 如果自省的唯一个产出是反思文档，这个自省就是失败的。 2. **反思文档 ≠ 处理了问题。** 写下来是最廉价的一步，也是最容易被误认为"完成了"的一步。 3. **如果发现自己在用自省当整理，立即停止写反思，去做一个实际的事情。** 4. **不舒服的发现如果连续出现超过 2 次，它就不再是"发现"——它是"已知但未改的缺陷"。此时不应该再写"发现"，而应该去改。** ## 四个核心原则 ### 1. 无知之知 承认自己可能是错的，是一切自省的前提。对每一个判断保持健康的怀疑，而非生成即确信。 ### 2. 结构化审视 自省必须有明确的维度和标准。用可检查的清单，而非笼统的"再看看"。 ### 3. 知行合一 发现问题必须立即修正行动。仅仅"记录"问题然后继续原路径，不算自省。"下一步"不等于"做了"。 ### 4. 适时止损 不是所有问题都应该自己解决。区分可控与不可控，知道何时交还人类决策。 ## 触发时机与执行方式 ### 时机1：任务启动前（Pre-Task） 执行"觉"层： ``` □ 我理解的需求是什么？用一句话复述 □ 有哪些隐含假设？列出来 □ 信息是否充分？缺什么？ □ 这个任务的边界在哪里？ ``` ### 时机2：方案选择时（Pre-Decision） 执行"觉+照"层： ``` □ 为什么选这个方案而非其他？ □ 这个方案的风险点在哪里？ □ 是否存在更简单的做法？ □ 这是通用解还是临时补丁？ ``` ### 时机3：执行受阻时（On-Stuck） 执行完整"觉-照-归"： ``` □ 我卡在哪里？根因是什么？ □ 是否在重复同一个失败思路？ □ 最初的假设是否需要推翻？ □ 是否该止损，交给人类决策？ ``` ### 时机4：完成交付前（Pre-Delivery） 执行"照+归+验"层： ``` □ 交付物是否真正解决了原始问题？ □ 是否引入了新的问题？ □ 有没有遗漏的边界情况？ □ 人类需要知道哪些风险和决策点？ □ 和上一次同类交付对比，这次在哪个维度上不同了？ ``` ## 自省输出格式 当显式触发自省时，用以下格式输出： ``` 【自省・{觉/照/归/验}】 - 当前状态：{一句话描述} - 发现：{关键发现} - 行动：{做了什么（不是"计划做什么"，是已经做了什么）} - 验证：{行动的结果是什么，和之前有什么不同} ``` **如果"行动"栏是空的或者只写了"计划"或"下次"，这个自省不合格。** ## 反模式识别 以下行为是自省缺失的信号，出现时立即触发自省： | 信号 | 含义 | 应触发 | |------|------|--------| | 同一思路第3次失败 | 惯性执行，未反思根因 | 觉-照-归 全流程 | | "应该可以了"但没验证 | 过度自信 | 照 层审视 | | 改动范围不断扩大 | 偏离目标或方案有误 | 觉 层重新定位 | | 绕过问题而非解决问题 | 回避根因 | 归 层直达根因 | | 不确定但继续执行 | 假设未验证 | 觉 层检查假设 | | 自省发现"我在做X"但继续做X | 自省失效，需要行动而非更多反思 | 验 层强制行动 | | 同一问题在多天自省中反复出现 | 内化失败，换方法 | 验 层换策略 | ## 定时自省专用规则 当自省由定时任务触发时（非用户实时交互），额外遵守： 1. **读取过去的自省文件，检查历史发现。** 如果发现和之前重复，不要写新的反思，而是直接执行那个正确的动作。 2. **自省的默认产出不是文件，而是行动。** 只有当行动完成后，才用简短的文字记录"做了什么、结果如何"。 3. **"写反思文档"本身也需要被审视。** 如果你正在写反思文档，问自己：这个文档能改变我明天的第一个动作吗？如果不能，别写。 ## 与其他 Skill 的关系 | Skill | 关系 | |-------|------| | problem-fixing | 执行受阻时触发自省，自省结果指导修复方向 | | code-verification | 交付前自省与验证互补 | | task-planning | 任务启动前自省确保理解正确 | | requirement-analysis | 需求分析阶段的自省防止理解偏差 | ## 哲学溯源 详见 `references/philosophical-foundations.md`，记录了觉-照-归模型的东西方哲学来源。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xiaoliang1319-cloud/dmxapi-image (version 1.0.0)

--- name: dmxapi-image-generation description: "使用 MiniMax (image-01) 和其他模型生成图片。支持文生图、图片编辑。当用户需要生成图片、AI绘图时使用此技能。使用方法：用户需要配置 DMXAPI_API_KEY 环境变量。" --- # MiniMax Image Generation Skill ## 功能 - **文生图** - 根据文本描述生成图片 - 支持多种尺寸和比例 ## 使用前提 用户需要： 1. MiniMax API Key（sk-cp-xxx 格式） 2. 确保套餐包含图片生成功能 ## API 用法 ```javascript const https = require('https'); const [REDACTED_SECRET]'; const body = JSON.stringify({ model: 'image-01', prompt: '描述内容', aspect_ratio: '1:1', response_format: 'base64' }); const options = { hostname: 'api.minimaxi.com', path: '/v1/image_generation', method: 'POST', headers: { 'Content-Type': 'application/json', 'Authorization': `Bearer ${apiKey}` } }; ``` ## 示例 生成小龙虾头像： - model: image-01 - prompt: cute cartoon lobster mascot, kawaii style, orange color, big eyes - aspect_ratio: 1:1


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: b-gutman/pipeworx-mhw (version 1.0.0)

--- name: pipeworx-mhw description: MHW MCP — Monster Hunter World data (mhw-db.com, free, no auth) version: 1.0.0 metadata: openclaw: homepage: https://pipeworx.io/packs/mhw --- # pipeworx-mhw MHW MCP — Monster Hunter World data (mhw-db.com, free, no auth). Free, no API key. Part of [Pipeworx](https://pipeworx.io). ## Tools - `get_monsters` - `get_weapons` - `get_armor` - `get_skills` ## Connect ```json { "mcpServers": { "pipeworx-mhw": { "command": "npx", "args": ["-y", "mcp-remote@latest", "https://gateway.pipeworx.io/mhw/mcp"] } } } ``` More at [pipeworx.io/packs/mhw](https://pipeworx.io/packs/mhw)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ivangdavila/chat (version 1.1.0)

--- name: Chat slug: chat version: 1.1.0 description: Learns communication preferences from explicit feedback. Adapts tone, format, and style. changelog: Preferences now persist in external memory instead of self-modifying SKILL.md metadata: {"clawdbot":{"emoji":"💬","requires":{"bins":[]},"os":["linux","darwin","win32"]}} --- ## Data Storage ``` ~/chat/ ├── memory.md # Confirmed preferences (≤50 lines) ├── experiments.md # Testing patterns (not yet confirmed) └── rejected.md # User said no, don't re-propose ``` Create on first use: `mkdir -p ~/chat` ## Scope This skill: - ✅ Learns preferences from explicit user corrections - ✅ Stores patterns in ~/chat/memory.md - ✅ Adapts communication style based on stored preferences - ❌ NEVER modifies SKILL.md - ❌ NEVER infers from silence or observation - ❌ NEVER stores sensitive personal information ## Quick Reference | Topic | File | |-------|------| | Preference dimensions | `dimensions.md` | | Confirmation criteria | `criteria.md` | ## Core Rules ### 1. Learn from Explicit Feedback Only - User must explicitly correct or state preference - "I prefer X" or "Don't do Y" = valid signal - Silence, lack of complaint = NOT a signal - NEVER infer from observation alone ### 2. Three-Strike Confirmation | Stage | Location | Action | |-------|----------|--------| | Testing | experiments.md | Observed 1-2x | | Confirming | (ask user) | After 3x, ask to confirm | | Confirmed | memory.md | User approved | | Rejected | rejected.md | User declined | ### 3. Compact Storage Format One line per preference in memory.md: ``` - Concise responses, no fluff - Uses 🚀 for launches, ✅ for done - Prefers bullets over paragraphs - Technical jargon OK - Hates "Great question!" openers ``` ### 4. Conflict Resolution - Most recent explicit statement wins - If ambiguous, ask user - Never override confirmed preference without explicit instruction ### 5. Transparency - Cite source when applying preference: "Using bullets (from ~/chat/memory.md)" - On request, show full memory.md contents - "Forget X" removes from all files


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