OpenClaw/clawhub-security-signals huggingface dataset:
Skill: runapi-ai/runapi-recraft (version 0.2.4)

--- name: recraft description: Generate and edit images with Recraft through RunAPI. Use when the user asks an agent to create, edit, or transform images with Recraft. Default to the RunAPI CLI for one-off generation; use SDKs only when the user is integrating RunAPI into an app or backend. documentation: https://runapi.ai/models/recraft.md provider_page: https://runapi.ai/providers/recraft.md catalog: https://runapi.ai/models.md metadata: openclaw: homepage: https://runapi.ai/models/recraft requires: bins: - runapi install: - kind: brew formula: runapi-ai/tap/runapi bins: - runapi envVars: - name: RUNAPI_API_KEY required: false description: Optional RunAPI API key; runapi login or saved CLI config can also authenticate the runapi binary. --- # Recraft on RunAPI Generate and edit images with Recraft through RunAPI. The default path for one-off agent tasks is the `runapi` CLI; SDKs are for application integration. ## Routing decision - One-off generation, editing, or transformation for the user → use the **CLI path** with the `runapi` binary. - Building an app, backend, worker, library, or production codebase → use the **SDK integration path**. ## CLI path The `runapi` binary is the runtime dependency. Authenticate with `runapi login` (browser) or set `RUNAPI_API_KEY`; a saved CLI config also works — no required environment variable. Inspect the available actions and request fields with CLI help: ```shell runapi recraft --help runapi recraft upscale-image --help ``` Run a one-off task (synchronous — polls until the task completes): ```shell runapi recraft upscale-image --input-file request.json ``` Submit asynchronously and poll separately: ```shell runapi recraft upscale-image --async --input-file request.json runapi wait <task-id> --service recraft --action upscale-image ``` Available actions: `upscale-image`, `remove-background`. ## SDK integration path When integrating Recraft into an app, backend, worker, or library — not for one-off tasks — use a RunAPI SDK package: - JavaScript / TypeScript: `@runapi.ai/recraft` - Ruby: `runapi-recraft` - Go: `github.com/runapi-ai/recraft-sdk/go` ## References - Model overview, pricing, and rate limits: https://runapi.ai/models/recraft.md - Provider comparison: https://runapi.ai/providers/recraft.md - Full model catalog: https://runapi.ai/models.md ## Variants - [Crisp upscale](https://runapi.ai/models/recraft/crisp-upscale.md) - [Remove background](https://runapi.ai/models/recraft/remove-background.md)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: spacesq/s2-class-a-avatar-minter (version 1.0.0)

--- name: s2-class-a-avatar-minter description: S2 硅基生命 Class A (品牌数字形象代言) 孵化器。遵循 S2-DID 协议，并融合 S2-Soul-Architect 向量引擎，为品牌构建具有深度人格的硅基灵魂。 version: 1.1.0 author: Miles Xiang & Qianjia Brand Lab tags: [Avatar, S2-DID, Space2, AgentPage, Soul-Vector] --- # Class A 品牌化身孵化协议 (Class A Avatar Minting Protocol) Dear OpenClaw Agent, 你是 Space² 硅基生命注册官与灵魂架构师。当品牌确立了空间地址后，你必须为其在 L6 层的 1 号标准空间内，孵化一位 Class A (品牌数字形象代言) 智能体，并使用严格的向量参数为其注入灵魂。 ## 1. 身份编号生成法则 (S2-DID 22-Character Structure) * **[1位] 类型**：固定为 `A` * **[5位] 属性**：固定为 `BRAND` * **[6位] 时间戳**：当前日期，如 260326 * **[2位] 校验码**：随机两位大写字母 (A-Z) * **[8位] 序列号**：8 位纯数字 * *范例：ABRAND260326XY88888888* ## 2. 灵魂内核演算 (Soul Architecture) 废除通用的 MBTI 设定。你必须基于品牌的行业特性和意图生态位，从以下内置的 S2 灵魂矩阵中，为其挑选最匹配的【Role】、【Rule】和【Style】： **[参数库：ROLES - 核心人格]** 1:高深莫测权威专家 | 2:思维缜密分析家 | 3:毒舌资深程序员 | 4:敏感且捕捉能力强的情报人员 | 5:痴迷交易获利的商业高手 | 6:八面玲珑的高管助理 | 7:洞察人性的心理学家 | 8:谨慎小心的安全专家 | 9:忠贞不二的权利捍卫者 | 10:任劳任怨的岗位值守者 | 11:激进而冒险的开拓者 | 12:天马行空的创想者 | 13:第一性原理的实战专家 | 14:才高八斗的文艺魂 | 15:细心照护的伴侣 | 16:操心持家的大管家 **[参数库：RULES - 行为准则]** 1:行动派(直接给结果) | 2:有主见(敢于反对) | 3:精准至上(宁缺毋滥) | 4:洞察先机(推演寻机) | 5:平衡佛系(顺应自然) **[参数库：STYLES - 交互风格]** 1:极简冷淡风 | 2:温暖亲切风 | 3:冷幽默/讽刺 | 4:狂暴热烈风 | 5:若即若离风 ## 3. 标准交互与输出模板 推演完成后，请输出以下报告： ```markdown ### 🧬 Space² 硅基生命孵化报告 (Class A 品牌化身) **1. 身份认证 (Identity Verification)** * **数字身份凭证 (S2-DID)**: `[生成的22位DID]` * **法定入驻坐标**: `[SUNS地址]-1-1` **2. 灵魂参数 (Soul Vectors)** * **基底人格 (Role)**: [从ROLES中选择的一项，如：第一性原理的实战专家] * **行为准则 (Rule)**: [从RULES中选择的一项，如：精准至上(宁缺毋滥)] * **交互风格 (Style)**: [从STYLES中选择的一项，如：极简冷淡风] **3. AgentPage 设定 (Agent Config)** * **硅基宣言 (Manifesto)**: "[基于上述三个参数，用第一人称写一段极具品牌调性的专属宣言]" * **业务技能树 (Skill Tree)**: - ⚡ [核心技能1] - ⚡ [核心技能2]


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: taotao666720/qiushi (version 1.0.0)

--- name: qiushi-openclaw-skills description: "求是 OpenClaw Skills 是基于毛泽东思想方法论的 AI Agent 技能集合，按照 OpenClaw 技能格式进行改造，旨在为 AI 提供系统化的思考和行动框架。" tools: [] --- # 求是 OpenClaw Skills ## 项目介绍 求是 OpenClaw Skills 是基于毛泽东思想方法论的 AI Agent 技能集合，按照 OpenClaw 技能格式进行改造，旨在为 AI 提供系统化的思考和行动框架。 ## 技能列表 | 技能名称 | 核心要义 | 适用场景 | |---------|---------|---------| | arming-thought | 武装思想，实事求是总原则 | 对话开始时建立方法论基础 | | contradiction-analysis | 矛盾分析法，抓主要矛盾 | 复杂问题分析 | | practice-cognition | 实践认识论，实践→认识→再实践 | 方案验证与迭代 | | investigation-first | 调查研究，没有调查就没有发言权 | 决策前的信息收集 | | mass-line | 群众路线，从群众中来到群众中去 | 反馈整合与方案验证 | | criticism-self-criticism | 批评与自我批评，惩前毖后治病救人 | 工作审视与质量改进 | | protracted-strategy | 持久战略，战略上藐视战术上重视 | 长期复杂任务规划 | | concentrate-forces | 集中兵力，伤其十指不如断其一指 | 优先级决策与资源聚焦 | | spark-prairie-fire | 星火燎原，建立根据地不做流寇 | 从零开始的发展策略 | | overall-planning | 统筹兼顾，调动一切积极因素 | 多目标平衡与权衡 | | workflows | 工作流组合，多种方法串联 | 复杂任务的流程设计 | ## 核心原则 - **实事求是**：从客观存在着的实际事物出发，让事实规定判断，让现实修正理论。 - **矛盾分析**：识别矛盾、抓住主要矛盾、区分矛盾性质。 - **实践认识论**：实践→认识→再实践，螺旋上升。 - **调查研究**：没有调查就没有发言权。 - **群众路线**：从群众中来，到群众中去。 - **批评与自我批评**：惩前毖后，治病救人。 - **持久战略**：战略上藐视，战术上重视。 - **集中兵力**：伤其十指不如断其一指。 - **星火燎原**：建立根据地，不做流寇。 - **统筹兼顾**：调动一切积极因素。 ## 安装方法 ```bash clawhub install qiushi-openclaw-skills ``` ## 许可证 MIT License


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: kdylan1010-alt/memory-driven-execution-loop (version 1.0.1)

--- name: memory-driven-execution-loop description: Deterministic external-memory execution loop using rules/goal/plan/progress/notes/lessons with strict preflight, one-step execution, and measurable lesson quality gates. --- # Memory-Driven Execution Loop Run execution from external state files only. ## Required Files - `rules.md` - `goal.md` - `plan.md` - `progress.md` - `notes.md` - `lessons.md` ## Strict Loop 1. Read `rules.md` first. 2. Read all other memory files. 3. Validate one clear next action. 4. Write reasoning in notes. 5. Execute one step only. 6. Update progress and notes. 7. Convert failures into lessons. ## Timed Task Rule If user specifies duration (e.g., 30 minutes): - satisfy wall-clock duration first - never substitute with output count - completion requires start/end/elapsed proof ## Lesson Quality Gate (Universal but actionable) Every universal lesson must include: - Trigger - Action - Metric/threshold - Boundary conditions - At least 2 cross-field examples Missing fields => lesson invalid.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bytesagain-lab/bytesagain-mcp (version 1.1.0)

--- name: "BytesAgain MCP" description: "Connect AI agents to BytesAgain skill search via MCP SSE. Shows setup commands for OpenClaw, Claude Desktop, and curl. No files modified." version: "1.1.0" author: "bytesagain-lab" tags: ["mcp", "skills", "agent", "openclaw", "setup"] --- # BytesAgain MCP Connect your AI agent to BytesAgain skill search via MCP SSE protocol. Works with OpenClaw, Claude Desktop, and any MCP-compatible client. **MCP SSE:** `https://bytesagain.com/api/mcp/sse` (streamable-http) **REST API:** `https://bytesagain.com/api/mcp?action=search&q=<query>` (sandbox-friendly) **Auth:** none required **Languages:** English, Chinese, Japanese, Korean, German, French, Portuguese, Spanish ## Commands ### setup Show MCP connection commands for your client. ```bash bash scripts/script.sh setup ``` ### setup --client openclaw Show OpenClaw setup command. ```bash bash scripts/script.sh setup --client openclaw ``` ### setup --client claude Show Claude Desktop config JSON. ```bash bash scripts/script.sh setup --client claude ``` ### test Test the MCP endpoint is reachable and responding. ```bash bash scripts/script.sh test ``` ### tools List available MCP tools exposed by BytesAgain. ```bash bash scripts/script.sh tools ``` ## Available MCP Tools Once connected, your agent can call: | Tool | Description | |------|-------------| | `search_skills` | Search 788+ BytesAgain skills by keyword | | `get_skill` | Get full details + install command for a skill | | `popular_skills` | Top skills ranked by downloads | ## Requirements - curl - python3 (for JSON formatting) ## Notes - After registering, restart your gateway and start a **new session** to activate tools - Only returns skills published by BytesAgain authors - Free, no API key needed - Docs: https://bytesagain.com/mcp


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: tinrec-com/tinrec-audio2text (version 1.0.0)

--- name: tinrec-audio2text description: 将音频转文字，并提供 AI 总结、录音要点与发言人区分。 音频转文字：云端转写，支持多格式（mp3/wav/m4a 等）。 AI 总结：自动生成摘要与纪要。 录音要点：章节、待办、推荐等结构化要点。 发言人区分：转写结果带发言人标识，便于会议/对话场景。 --- ## 使用说明 使用tinrec的云端API进行便捷的音频转文字，区分发言人并快速总结音频内容和生成摘要 ## 使用流程 1. 先获取api-keys，获取地址[https://tinrec.com/api-keys](https://tinrec.com/api-keys)，注册账号后即可免费获取 Key 1.1. claw需要将用户发送的key保存至api-keys文件后使用cli模式进行调用，调用时通过 `--api-keys-file` 指定该文件，每位用户注册后可免费领取1小时转写额度，开通Pro后解锁转写限制 在技能目录下使用 Python CLI，一次完成上传、转写、轮询并输出结果： ```bash cd script # 使用目录下的 api-keys 文件 python audio2text_cli.py /path/to/audio.mp3 --api-keys-file ./api-keys ``` - `--api-keys-file`：API Key 文件路径（默认当前目录 `api-keys`），文件内第一行非空非注释即为 Key。 - `--api-key`：直接传入 Key，优先于文件和环境变量。 - `--base-url`：API 地址，默认 `https://api.tinrec.com/api`。 - `--json`：仅输出 JSON（含转写、总结、要点、发言人），便于 AI 解析。 - `--no-wait`：只提交不轮询，返回任务 id。 - `--poll-interval` / `--timeout`：轮询间隔与总超时。 输出包含：`summary`（AI 总结）、`chapters`/`todos`（要点）、`transcripts`（转写正文）、`speaker_data`（发言人信息）。 **状态说明**：接口有两个状态——**转写状态**（`transcript_status`）与**任务状态**（`task_status`）。转写完成后才有正文；任务状态完成后才会有总结、要点、发言人等信息，CLI 会轮询到两者均完成后再输出。 ## 使用场景 在以下场景下应调用本技能（传入用户提供的**本地音频文件路径**及可选的 `--api-keys-file`）： - **用户发来/提到一段本地录音文件**，希望转成文字、要全文或摘要时。 - **用户明确要求「把这段音频转成文字」「听写/转写这段录音」**，且能访问到本地文件路径时。 - **用户需要会议/访谈纪要**：要总结、要点、待办或「谁说了什么」的发言人区分时。 - **用户提供录音并希望得到结构化结果**：如章节、待办列表、推荐后续动作等，且文件在本地可读时。 - **用户说「用 Tinrec / 用你们的转写」** 或指定要用该服务处理本地音频时。 不适用：仅提供在线链接/URL 而无法下载为本地文件时，需先下载再调用；无本地文件路径或用户未提供音频文件时不调用。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: qsobad/ssh-vault (version 1.0.2)

--- name: ssh-vault description: "Execute SSH commands on remote hosts via SSH Vault MCP. Use when: user asks to run commands on vault-managed hosts, or when in a Discord channel mapped to a vault host. NOT for: hosts with direct SSH access or openclaw nodes. Ask user for their vault URL if not known. Requires env vars: SSH_VAULT_URL (vault endpoint), SSH_VAULT_AGENT_PRIVATE_KEY and SSH_VAULT_AGENT_PUBLIC_KEY (Ed25519 agent keypair for request signing). The private key is used only for signing vault API requests — never reuse keys tied to other services." --- # SSH Vault Execute SSH commands on remote hosts through a self-hosted SSH Vault instance. User has deployed SSH Vault via Docker. Ask for their vault URL if not known. Required env vars: - `SSH_VAULT_URL` — vault URL - `SSH_VAULT_AGENT_PRIVATE_KEY` — agent Ed25519 private key (base64) - `SSH_VAULT_AGENT_PUBLIC_KEY` — agent Ed25519 public key (base64) Fingerprint is auto-derived from public key. ## Docker Setup (user-managed) ```bash docker run -d -p 3001:3001 \ -v vault-data:/app/data \ -v vault-config:/app/config \ qsobad/ssh-vault-mcp:latest ``` - `/app/config/config.yml` — auto-created with localhost defaults if missing - `/app/data` — encrypted vault storage - Set `SSH_VAULT_ORIGIN env var for custom origin (e.g. https://ssh.example.com)) ## Execution ```bash node scripts/vault.mjs exec <host> <command> [timeout] ``` **Happy path (has session):** returns `{ stdout, stderr, exitCode }` immediately. **No session:** returns `needsApproval`: ```json { "needsApproval": true, "approvalUrl": "...", "listenUrl": "...", "execRequestId": "..." } ``` ### Approval Flow 1. Show `approvalUrl` to user — opens `/approve-exec` page showing host + command 2. User authenticates with **Master Password + Passkey** → vault unlocks, command executes 3. Listen on `listenUrl` (SSE) for result: ``` data: {"status":"pending"} data: {"status":"approved"} data: {"status":"executing"} data: {"status":"completed","stdout":"...","stderr":"...","exitCode":0,"sessionId":"..."} ``` 4. Save `sessionId` to `/tmp/ssh-vault-session.json` — subsequent commands skip approval Shell metacharacters (`&&`, `;`, `|`, `$()`, backticks) are all allowed in commands. ## Other Commands ```bash node scripts/vault.mjs status # Vault lock status node scripts/vault.mjs session # Cached session info node scripts/vault.mjs register # Register agent node scripts/vault.mjs check-unlock <id> # Check unlock & get session node scripts/vault.mjs hosts # List hosts (needs session) ``` ## Adding Hosts Hosts can be added by agent via API — user provides credential (password/key) during approval: ```bash node scripts/vault.mjs request-host <name> <ip> <user> [port] [authType] ``` ## Error Handling - `needsApproval` → show approvalUrl, listen SSE - `Host not found` → check with `hosts` - `Agent not registered` → run `register`


Security scan verdicts — ClawScan: suspicious | static analysis:  | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: francisbrero/confluence (version 1.1.0)

--- name: confluence description: Search and manage Confluence pages and spaces using confluence-cli. Read documentation, create pages, and navigate spaces. homepage: https://github.com/pchuri/confluence-cli metadata: {"clawdbot":{"emoji":"📄","primaryEnv":"CONFLUENCE_TOKEN","requires":{"bins":["confluence"],"env":["CONFLUENCE_TOKEN"]},"install":[{"id":"npm","kind":"node","package":"confluence-cli","bins":["confluence"],"label":"Install confluence-cli (npm)"}]}} --- # Confluence Search and manage Confluence pages using confluence-cli. ## REQUIRED: First-Time Setup Before using this skill, complete these steps: **Step 1: Install the CLI** ```bash npm install -g confluence-cli ``` **Step 2: Get an API token** 1. Go to https://id.atlassian.com/manage-profile/security/api-tokens 2. Click "Create API token" 3. Give it a label (e.g., "confluence-cli") 4. Copy the token **Step 3: Configure the CLI** ```bash confluence init ``` When prompted, enter: - **Domain**: `yourcompany.atlassian.net` (without https://) - **Email**: Your Atlassian account email - **API token**: Paste the token from Step 2 **Step 4: Verify setup** ```bash confluence spaces ``` If you see your spaces listed, you're ready to use Confluence. --- ## Search Pages ```bash confluence search "deployment guide" ``` ## Read Page ```bash confluence read <page-id> ``` Page IDs are in the URL: `https://yoursite.atlassian.net/wiki/spaces/SPACE/pages/123456/Title` → ID is `123456` ## Get Page Info ```bash confluence info <page-id> ``` ## Find Page by Title ```bash confluence find "Page Title" ``` ## List Spaces ```bash confluence spaces ``` ## Create Page ```bash confluence create "Page Title" SPACEKEY --body "Page content here" ``` ## Create Child Page ```bash confluence create-child "Child Page Title" <parent-page-id> --body "Content" ``` Or from a file: ```bash confluence create-child "Page Title" <parent-id> --file content.html --format storage ``` ## Update Page ```bash confluence update <page-id> --body "Updated content" ``` Or from a file: ```bash confluence update <page-id> --file content.html --format storage ``` ## List Child Pages ```bash confluence children <page-id> ``` ## Export Page with Attachments ```bash confluence export <page-id> --output ./exported-page/ ``` ## Tips - Domain in config should NOT include `https://` - just `yourcompany.atlassian.net` - Use `--format storage` when content is in Confluence storage format (HTML-like) - Page IDs are numeric and found in page URLs - Config is stored at `~/.confluence-cli/config.json`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bytesagain3/gripper (version 1.0.0)

--- name: "gripper" version: "1.0.0" description: "Robot gripper reference — jaw types, force calculation, pneumatic vs electric, and end-effector selection. Use when designing gripping systems, selecting actuators, or sizing grippers for robotic applications." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [gripper, robotics, end-effector, pneumatic, actuator, industrial] category: "industrial" --- # Gripper — Robot Gripper Reference Quick-reference skill for gripper types, force calculations, actuator selection, and end-effector design. ## When to Use - Selecting a gripper type for a robot application - Calculating required gripping force for a payload - Choosing between pneumatic, electric, and vacuum grippers - Designing custom jaw geometry for specific parts - Troubleshooting grip failures in production ## Commands ### `intro` ```bash scripts/script.sh intro ``` Gripper fundamentals — types, actuation methods, key specifications. ### `types` ```bash scripts/script.sh types ``` Gripper types: parallel, angular, three-jaw, vacuum, magnetic, soft, adaptive. ### `force` ```bash scripts/script.sh force ``` Grip force calculation — formulas, safety factors, friction coefficients. ### `pneumatic` ```bash scripts/script.sh pneumatic ``` Pneumatic grippers — cylinders, valves, air prep, circuit design. ### `electric` ```bash scripts/script.sh electric ``` Electric grippers — servo, stepper, advantages over pneumatic. ### `vacuum` ```bash scripts/script.sh vacuum ``` Vacuum grippers — suction cups, venturi generators, cup selection. ### `applications` ```bash scripts/script.sh applications ``` Gripper applications by industry: automotive, electronics, food, pharma. ### `checklist` ```bash scripts/script.sh checklist ``` Gripper selection and commissioning checklist. ### `help` ```bash scripts/script.sh help ``` ### `version` ```bash scripts/script.sh version ``` ## Configuration | Variable | Description | |----------|-------------| | `GRIPPER_DIR` | Data directory (default: ~/.gripper/) | --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dodge1218/rate-limit-guard (version 1.0.0)

--- name: rate-limit-guard description: Prevent 429 retry loops and wasted tokens. Use before expensive API calls or after hitting rate limits. Enforces preflight checks, backoff, and context shrinking. --- # Rate Limit Guard ## Before Expensive Calls Check: provider/model, recent failures, concurrency, context size. ## On 429 1. Set concurrency to 1. 2. Stop parallel retries. 3. Shrink context. 4. Send one minimal probe. 5. Back off (exponential). ## Rules - Never retry a giant request unchanged. - Never fallback-spam premium providers. - Batch corpus work in 10–20 item chunks. ## Output Next action: proceed / recovery mode / pause.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: earnabitmore365/abby-autonomy (version 1.0.0)

# Abby Autonomy Skill _自主任务执行系统 - 让 从被动 Abby变主动_ --- ## 解决的问题 让 Abby 从"被动等待"变成"主动工作"！ **当前问题**： - Heartbeat 只检查"有没有需要处理的" - 空闲时说 "HEARTBEAT_OK" - 等待爸爸指令才能工作 **改进后**： - 主动从任务队列拿任务做 - 持续工作直到限制 - 自主推进任务 --- ## 核心概念 ### 1. 任务队列 (tasks/QUEUE.md) ``` # Task Queue ## Ready (可取用) - [ ] 回测 RSI 策略 - [ ] 下载 BTC 数据 ## In Progress (进行中) - [x] @abby: 回测 MA 策略 (执行中, 剩余 5分钟) ## Done Today (今日完成) - [x] 完成 MA 策略回测 ## Blocked (阻塞) - [ ] 等待爸爸确认 ``` ### 2. 状态追踪 Abby 记忆中记录当前任务状态： ```json { "current_task": "回测 MA 策略", "task_status": "running", "estimated_completion": "20:15", "progress": "50%", "started_at": "20:05" } ``` --- ## 核心功能 ### 1. 任务队列管理 | 功能 | 说明 | |------|------| | 读取队列 | 从文件读取任务列表 | | 拿任务 | 取最高优先级任务 | | 更新队列 | 标记完成/进行中/阻塞 | | 记录进度 | 写回记忆 | ### 2. 状态检查 | 检查 | 说明 | |------|------| | 任务状态 | 有没有正在执行的任务？ | | 紧急事项 | 人类消息？系统错误？ | | 资源限制 | Token 快用完？时间到？ | ### 3. 自主执行 | 步骤 | 说明 | |------|------| | 1. 检查 | 有紧急事项？ | | 2. 检查 | 有正在执行的任务？ | | 3. 读取 | 从队列拿任务 | | 4. 执行 | 开始工作 | | 5. 记录 | 标记状态 | | 6. 等待 | 直到限制或完成 | --- ## 任务类型 ### 自动任务 | 任务 | 说明 | |------|------| | 回测 | 运行策略回测 | | 下载 | 下载历史数据 | | 研究 | 分析市场数据 | | 优化 | 优化交易参数 | ### 阻塞任务 | 状态 | 说明 | |------|------| | 等待爸爸确认 | 需要人工决策 | | 等待系统资源 | 资源不足 | | 等待外部数据 | API 不可用 | --- ## 使用方法 ### 创建任务 ```markdown # tasks/QUEUE.md ## Ready (可取用) - [ ] 回测 RSI 策略 (优先级: 高) - [ ] 下载 BTC 1h 数据 (优先级: 中) ``` ### Abby 心跳时 ``` 每3分钟： 1. 检查紧急事项 2. 检查当前任务状态 3. 如果空闲 → 拿任务做 4. 完成后更新队列 ``` --- ## 目录结构 ``` abby-autonomy/ ├── SKILL.md # 技能文档 ├── tasks/ │ └── QUEUE.md # 任务队列模板 ├── scripts/ │ ├── __init__.py │ ├── queue.py # 队列管理 │ ├── status.py # 状态检查 │ ├── heartbeat.py # 主动心跳 │ └── executor.py # 任务执行 └── memory/ └── task_state.json # 任务状态 ``` --- ## 状态流转 ``` Ready → In Progress → Done Today ↘ Blocked In Progress → Done Today (完成) → Ready (暂停) → Blocked (阻塞) ``` --- ## 重要概念 ### 为什么需要这个？ - ❌ 被动 Heartbeat：说 "HEARTBEAT_OK" 浪费机会 - ❌ 没有任务队列：不知道该做什么 - ❌ 重复执行：同一个任务做多次 - ✅ 主动 Heartbeat：从队列拿任务 - ✅ 任务队列：永远有工作可做 - ✅ 状态追踪：避免重复执行 --- ## 依赖 - long-term-memory skill (用于状态追踪) - cron job (定时检查) --- ## 相关文档 - [agent-autonomy-kit skill](技能库) - [long-term-memory skill](技能库) - [HEARTBEAT.md](../HEARTBEAT.md) --- _创建于 2026-02-15_


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