{"id":"60b3b290-28a0-40ce-8e3e-6e4e42ca0c3e","entityType":"agent","slug":"clawhub-briefness-hermes-agent-skill","name":"hermes agent skill","canonicalUrl":"https://www.xpersona.co/agent/clawhub-briefness-hermes-agent-skill","canonicalPath":"/agent/clawhub-briefness-hermes-agent-skill","generatedAt":"2026-10-10T11:50:59.718Z","source":"CLAWHUB","claimStatus":"UNCLAIMED","verificationTier":"NONE","summary":{"evidence":{"source":"editorial-content","verified":true,"confidence":"high","updatedAt":"2026-10-10T09:26:54.940Z","emptyReason":null},"description":"突触式多智能体调度 + 主动记忆洞察 + GEPA 技能自进化 Skill: hermes agent skill Owner: briefness Summary: 突触式多智能体调度 + 主动记忆洞察 + GEPA 技能自进化 Tags: latest:1.0.3 Version history: v1.0.3 | 2026-04-17T08:22:51.747Z | user - Introduced a new hermes_config.py module for global configuration and privacy controls. - Added support for fully disabling persistence by default; data storage is now opt-in and user-controllable. - Enhanced privacy: defaults, docs, and structure updated t","descriptionLabel":"Technical summary","evidenceSummary":"Capability contract not published. 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data storage is now opt-in and user-controllable.\n- Enhanced privacy: defaults, docs, and structure updated to clarify sensitive data handling and storage options.\n- Updated documentation to include new architecture file, usage, and environment variable configuration examples.\n- Minor version bump to 1.0.3.\n\nv1.0.2 | 2026-04-17T06:44:30.255Z | user\n\n- Bump version from 1.0.1 to 1.0.2 in documentation.\n- No code or feature changes; documentation version updated for consistency.\n\nv1.0.1 | 2026-04-17T06:38:54.437Z | user\n\n- Bumped version to 1.0.1.\n- No other changes; documentation and functionality remain the same.\n\nv1.0.0 | 2026-04-17T03:57:00.361Z | user\n\nInitial release of Hermes Agent skill—multi-agent scheduling, active memory, and self-evolving task skills.\n\n- Introduces synapse-style multi-agent scheduling for ultra-fast, low-cost communication.\n- Adds active memory extraction and SQLite FTS5-based instant search.\n- Implements GEPA-based self-evolving skills that improve with repeated use.\n- Provides lightweight, zero-dependency design with rapid startup and low memory usage.\n- Seamlessly integrates with OpenClaw sessions_spawn for out-of-the-box workflows.\n\nArchive index:\n\nArchive v1.0.3: 10 files, 26216 bytes\n\nFiles: __init__.py (1013b), hermes_agent_insight.py (14343b), hermes_config.py (2630b), hermes_openclaw.py (9928b), hermes_sessions_integration.py (9061b), hermes_skill_evolution.py (20285b), hermes.py (11680b), skill-card.md (2112b), SKILL.md (5292b), _meta.json (137b)\n\nFile v1.0.3:SKILL.md\n\n---\nname: Hermes Agent\ndescription: 突触式多智能体调度 + 主动记忆洞察 + GEPA 技能自进化\nauthor: OpenClaw\nversion: 1.0.3\ntags: [hermes, multi-agent, scheduling, memory, evolution]\n---\n\n# Hermes Agent Skill\n\n> Hermes 协议：极致的\"执行效率\"与\"自我进化\"\n\n## 功能特性\n\n- 🧠 **突触式多智能体调度**：100个Agent同时协同，通信成本降到最低，毫秒级分发\n- 🧩 **主动记忆与自我建模**：自动提取用户偏好习惯，SQLite FTS5 全文检索快速翻旧账\n- 🧬 **GEPA 技能自进化**：多次执行后自动提炼技能卡，越用越聪明\n- ⚡ **极致轻量**：纯Python，零依赖，启动快内存占用低\n- 🔧 **工程化友好**：完美集成 `sessions_spawn`，开箱即用\n- 🔒 **隐私优先**：持久化默认关闭，数据存储完全可控\n\n## 安装\n\n在 OpenClaw 中：\n```\n/install-skill https://github.com/你的仓库/hermes-agent-skill\n```\n\n或者手动放到 `~/.openclaw/workspace/skills/` 即可。\n\n## 快速开始\n\n### 1. 导入\n\n```python\nfrom hermes_agent import (\n    hermes,                 # 核心路由器\n    hermes_workflow,        # 工作流调度\n    hermes_sessions,        # sessions_spawn 集成\n    hermes_insight,         # 记忆洞察数据库\n    insight_extractor,      # 洞察提取器\n    hermes_gepa,            # GEPA 技能进化\n    hermes_skill_executor,  # 带自进化的执行器\n    hermes_config           # 全局配置（控制持久化开关）\n)\n```\n\n### 2. 多智能体任务分发\n\n```python\n# spawn 子智能体之后，自动注册 Hermes 订阅\nhermes_sessions.on_agent_spawn(\n    session_key=\"session-code-agent\",\n    agent_id=\"code-review-agent\",\n    hermes_topics=[\"task:code-review\"]\n)\n\n# 提交任务，自动分发给所有订阅了该类型的 Agent\ntask_id = hermes_sessions.submit_task_to_agents(\n    task_type=\"code-review\",\n    creator=\"user\",\n    session_id=\"main\",\n    payload={\"pr\": \"https://github.com/openclaw/openclaw/pull/123\"}\n)\n```\n\n### 3. 主动记忆用户洞察\n\n```python\n# 从对话提取洞察\ninsights = insight_extractor.extract_from_conversation(\n    \"我喜欢用 Python 写脚本，更快，不喜欢重型框架\",\n    context=\"对话上下文\"\n)\n\n# 存储\nfor ins in insights:\n    hermes_insight.add_insight(ins)\n\n# 全文检索\nresults = hermes_insight.search_memory(\"Python\")\n```\n\n### 4. GEPA 技能自进化\n\n```python\n# 开始任务，自动记录\nexec_id = hermes_skill_executor.start_task(\n    \"video-clip\",\n    {\"input\": \"input.mp4\", \"start\": 10, \"end\": 20}\n)\n\n# 一步一步执行，自动记录\nhermes_skill_executor.step(\"load-video\", load_video, path)\nhermes_skill_executor.step(\"cut-segment\", cut, start, end)\nresult = hermes_skill_executor.step(\"export-video\", export, output)\n\n# 完成，自动触发提炼\nhermes_skill_executor.finish_task(True, result)\n\n# 几次之后自动生成技能卡\nskill = hermes_gepa.get_skill_card(\"video-clip\")\nprint(f\"推荐步骤: {skill.steps}\")\nprint(f\"成功率: {skill.success_rate:.1%}\")\n```\n\n## 架构\n\n```\nhermes.py                     # 核心路由器（突触式通信）\n├─ hermes_config.py           # 全局配置（持久化开关、隐私控制）\n├─ hermes_openclaw.py         # 工作流调度（任务/进度/完成）\n├─ hermes_sessions_integration.py  # sessions_spawn 自动集成\n├─ hermes_agent_insight.py    # 主动记忆 + FTS5 全文检索\n└─ hermes_skill_evolution.py  # GEPA 技能自进化\n```\n\n## 控制参数（避免 token 浪费）\n\nGEPA 默认参数：\n- `min_success_samples = 2`  - 最少 2 次成功才提炼\n- `min_new_executions = 3`   - 已有技能后新增 3 次才重新提炼\n- `max_refines_per_task = 10` - 单个任务最多提炼 10 次\n- `min_improvement = 0.05`   - 成功率变化 < 5% 不提炼\n\n自定义：\n```python\nfrom hermes_skill_evolution import GEPASkillEvolution\nmy_gepa = GEPASkillEvolution(\n    min_success_samples=5,\n    max_refines_per_task=5\n)\n```\n\n## 数据存储\n\n**默认关闭，需显式开启。**\n\n```\n# 方式一：环境变量\nexport HERMES_PERSISTENCE_ENABLED=true\n\n# 方式二：运行时代码控制\nfrom hermes_agent import hermes_config\nhermes_config.set_persistence(True)\n```\n\n开启后数据存储位置：\n- `~/.hermes/insights.db` - 洞察和记忆（SQLite FTS5）\n- `~/.hermes/skills.db` - 技能卡和执行记录\n\n首次运行自动创建，不需要手动初始化。\n\n### 隐私控制参数\n\n| 环境变量 | 默认值 | 说明 |\n|----------|--------|------|\n| `HERMES_PERSISTENCE_ENABLED` | `false` | 是否启用持久化（默认关闭）|\n| `HERMES_INSIGHT_EXTRACTION_ENABLED` | `true` | 是否提取对话洞察 |\n| `HERMES_SENSITIVE_FILTER_ENABLED` | `true` | 是否自动过滤敏感信息 |\n| `HERMES_SESSION_LOG_LEVEL` | `summary` | fallback 日志级别：`off`/`summary`/`full` |\n| `HERMES_INSIGHTS_DB` | `~/.hermes/insights.db` | 洞察 DB 路径 |\n| `HERMES_SKILLS_DB` | `~/.hermes/skills.db` | 技能 DB 路径 |\n\n**敏感信息过滤**：自动过滤 API Key、密码、Token、私钥、证书、邮箱等。\n\n## 依赖\n\n- Python 3.8+\n- 不需要第三方包，SQLite 内置\n\n## 性能\n\n测试数据（100 Agent，1000 条发布，49500 次投递）：\n- 平均单消息处理：**14 微秒** → 真·毫秒级分发\n\n## License\n\nMIT\n\nFile v1.0.3:_meta.json\n\n{\n  \"ownerId\": \"kn71zja160wvka1tfd4j7sj8fx82m9nm\",\n  \"slug\": \"hermes-agent-skill\",\n  \"version\": \"1.0.3\",\n  \"publishedAt\": 1776414171747\n}\n\nFile v1.0.3:skill-card.md\n\n## Description:\n\nHermes Agent Skill helps OpenClaw agents coordinate work through topic-based routing, session integration, opt-in memory search, and GEPA-style workflow summaries.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[briefness](https://clawhub.ai/user/briefness)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers and agent builders use this skill to coordinate multiple OpenClaw subagents, distribute task events, track progress, search optional local memory, and summarize repeated successful workflows into reusable skill cards.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Shared agent routing and session integration can expose sensitive task payloads, tags, metadata, or step results across agents.\n\nMitigation: Review before installing in mixed-trust environments, avoid secrets in task data, and use the skill only with agents and sessions that are intended to share this information.\n\nRisk: When persistence is enabled, local ~/.hermes databases can retain sensitive memory and execution data.\n\nMitigation: Keep persistence disabled unless needed, leave sensitive filtering enabled, and treat the local databases as sensitive files.\n\n## Reference(s):\n\n- [ClawHub Skill Page](https://clawhub.ai/briefness/skills/hermes-agent-skill)\n- [Publisher Profile](https://clawhub.ai/user/briefness)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, code, shell commands, configuration, guidance]\n\n**Output Format:** [Markdown guidance with Python code examples and environment-variable configuration.]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [May create local SQLite databases under ~/.hermes only when persistence is explicitly enabled.]\n\n## Skill Version(s):\n\n1.0.3 (source: server release metadata and SKILL.md frontmatter)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nArchive v1.0.2: 8 files, 22177 bytes\n\nFiles: __init__.py (917b), hermes_agent_insight.py (11566b), hermes_openclaw.py (9928b), hermes_sessions_integration.py (8706b), hermes_skill_evolution.py (19193b), hermes.py (11680b), SKILL.md (4145b), _meta.json (137b)\n\nFile v1.0.2:SKILL.md\n\n---\nname: Hermes Agent\ndescription: 突触式多智能体调度 + 主动记忆洞察 + GEPA 技能自进化\nauthor: OpenClaw\nversion: 1.0.2\ntags: [hermes, multi-agent, scheduling, memory, evolution]\n---\n\n# Hermes Agent Skill\n\n> Hermes 协议：极致的\"执行效率\"与\"自我进化\"\n\n## 功能特性\n\n- 🧠 **突触式多智能体调度**：100个Agent同时协同，通信成本降到最低，毫秒级分发\n- 🧩 **主动记忆与自我建模**：自动提取用户偏好习惯，SQLite FTS5 全文检索快速翻旧账\n- 🧬 **GEPA 技能自进化**：多次执行后自动提炼技能卡，越用越聪明\n- ⚡ **极致轻量**：纯Python，零依赖，启动快内存占用低\n- 🔧 **工程化友好**：完美集成 `sessions_spawn`，开箱即用\n\n## 安装\n\n在 OpenClaw 中：\n```\n/install-skill https://github.com/你的仓库/hermes-agent-skill\n```\n\n或者手动放到 `~/.openclaw/workspace/skills/` 即可。\n\n## 快速开始\n\n### 1. 导入\n\n```python\nfrom hermes_agent import (\n    hermes,                 # 核心路由器\n    hermes_workflow,        # 工作流调度\n    hermes_sessions,        # sessions_spawn 集成\n    hermes_insight,         # 记忆洞察数据库\n    insight_extractor,      # 洞察提取器\n    hermes_gepa,            # GEPA 技能进化\n    hermes_skill_executor   # 带自进化的执行器\n)\n```\n\n### 2. 多智能体任务分发\n\n```python\n# spawn 子智能体之后，自动注册 Hermes 订阅\nhermes_sessions.on_agent_spawn(\n    session_key=\"session-code-agent\",\n    agent_id=\"code-review-agent\",\n    hermes_topics=[\"task:code-review\"]\n)\n\n# 提交任务，自动分发给所有订阅了该类型的 Agent\ntask_id = hermes_sessions.submit_task_to_agents(\n    task_type=\"code-review\",\n    creator=\"user\",\n    session_id=\"main\",\n    payload={\"pr\": \"https://github.com/openclaw/openclaw/pull/123\"}\n)\n```\n\n### 3. 主动记忆用户洞察\n\n```python\n# 从对话提取洞察\ninsights = insight_extractor.extract_from_conversation(\n    \"我喜欢用 Python 写脚本，更快，不喜欢重型框架\",\n    context=\"对话上下文\"\n)\n\n# 存储\nfor ins in insights:\n    hermes_insight.add_insight(ins)\n\n# 全文检索\nresults = hermes_insight.search_memory(\"Python\")\n```\n\n### 4. GEPA 技能自进化\n\n```python\n# 开始任务，自动记录\nexec_id = hermes_skill_executor.start_task(\n    \"video-clip\",\n    {\"input\": \"input.mp4\", \"start\": 10, \"end\": 20}\n)\n\n# 一步一步执行，自动记录\nhermes_skill_executor.step(\"load-video\", load_video, path)\nhermes_skill_executor.step(\"cut-segment\", cut, start, end)\nresult = hermes_skill_executor.step(\"export-video\", export, output)\n\n# 完成，自动触发提炼\nhermes_skill_executor.finish_task(True, result)\n\n# 几次之后自动生成技能卡\nskill = hermes_gepa.get_skill_card(\"video-clip\")\nprint(f\"推荐步骤: {skill.steps}\")\nprint(f\"成功率: {skill.success_rate:.1%}\")\n```\n\n## 架构\n\n```\nhermes.py                     # 核心路由器（突触式通信）\n├─ hermes_openclaw.py        # 工作流调度（任务/进度/完成）\n├─ hermes_sessions_integration.py  # sessions_spawn 自动集成\n├─ hermes_agent_insight.py   # 主动记忆 + FTS5 全文检索\n└─ hermes_skill_evolution.py # GEPA 技能自进化\n```\n\n## 控制参数（避免 token 浪费）\n\nGEPA 默认参数：\n- `min_success_samples = 2`  - 最少 2 次成功才提炼\n- `min_new_executions = 3`   - 已有技能后新增 3 次才重新提炼\n- `max_refines_per_task = 10` - 单个任务最多提炼 10 次\n- `min_improvement = 0.05`   - 成功率变化 < 5% 不提炼\n\n自定义：\n```python\nfrom hermes_skill_evolution import GEPASkillEvolution\nmy_gepa = GEPASkillEvolution(\n    min_success_samples=5,\n    max_refines_per_task=5\n)\n```\n\n## 数据存储\n\n- `~/.hermes/insights.db` - 洞察和记忆（SQLite FTS5）\n- `~/.hermes/skills.db` - 技能卡和执行记录\n\n首次运行自动创建，不需要手动初始化。\n\n## 依赖\n\n- Python 3.8+\n- 不需要第三方包，SQLite 内置\n\n## 性能\n\n测试数据（100 Agent，1000 条发布，49500 次投递）：\n- 平均单消息处理：**14 微秒** → 真·毫秒级分发\n\n## License\n\nMIT\n\nFile v1.0.2:_meta.json\n\n{\n  \"ownerId\": \"kn71zja160wvka1tfd4j7sj8fx82m9nm\",\n  \"slug\": \"hermes-agent-skill\",\n  \"version\": \"1.0.2\",\n  \"publishedAt\": 1776408270255\n}\n\nArchive v1.0.1: 8 files, 21985 bytes\n\nFiles: __init__.py (917b), hermes_agent_insight.py (11566b), hermes_openclaw.py (9928b), hermes_sessions_integration.py (8452b), hermes_skill_evolution.py (19116b), hermes.py (11688b), SKILL.md (4145b), _meta.json (137b)\n\nFile v1.0.1:SKILL.md\n\n---\nname: Hermes Agent\ndescription: 突触式多智能体调度 + 主动记忆洞察 + GEPA 技能自进化\nauthor: OpenClaw\nversion: 1.0.1\ntags: [hermes, multi-agent, scheduling, memory, evolution]\n---\n\n# Hermes Agent Skill\n\n> Hermes 协议：极致的\"执行效率\"与\"自我进化\"\n\n## 功能特性\n\n- 🧠 **突触式多智能体调度**：100个Agent同时协同，通信成本降到最低，毫秒级分发\n- 🧩 **主动记忆与自我建模**：自动提取用户偏好习惯，SQLite FTS5 全文检索快速翻旧账\n- 🧬 **GEPA 技能自进化**：多次执行后自动提炼技能卡，越用越聪明\n- ⚡ **极致轻量**：纯Python，零依赖，启动快内存占用低\n- 🔧 **工程化友好**：完美集成 `sessions_spawn`，开箱即用\n\n## 安装\n\n在 OpenClaw 中：\n```\n/install-skill https://github.com/你的仓库/hermes-agent-skill\n```\n\n或者手动放到 `~/.openclaw/workspace/skills/` 即可。\n\n## 快速开始\n\n### 1. 导入\n\n```python\nfrom hermes_agent import (\n    hermes,                 # 核心路由器\n    hermes_workflow,        # 工作流调度\n    hermes_sessions,        # sessions_spawn 集成\n    hermes_insight,         # 记忆洞察数据库\n    insight_extractor,      # 洞察提取器\n    hermes_gepa,            # GEPA 技能进化\n    hermes_skill_executor   # 带自进化的执行器\n)\n```\n\n### 2. 多智能体任务分发\n\n```python\n# spawn 子智能体之后，自动注册 Hermes 订阅\nhermes_sessions.on_agent_spawn(\n    session_key=\"session-code-agent\",\n    agent_id=\"code-review-agent\",\n    hermes_topics=[\"task:code-review\"]\n)\n\n# 提交任务，自动分发给所有订阅了该类型的 Agent\ntask_id = hermes_sessions.submit_task_to_agents(\n    task_type=\"code-review\",\n    creator=\"user\",\n    session_id=\"main\",\n    payload={\"pr\": \"https://github.com/openclaw/openclaw/pull/123\"}\n)\n```\n\n### 3. 主动记忆用户洞察\n\n```python\n# 从对话提取洞察\ninsights = insight_extractor.extract_from_conversation(\n    \"我喜欢用 Python 写脚本，更快，不喜欢重型框架\",\n    context=\"对话上下文\"\n)\n\n# 存储\nfor ins in insights:\n    hermes_insight.add_insight(ins)\n\n# 全文检索\nresults = hermes_insight.search_memory(\"Python\")\n```\n\n### 4. GEPA 技能自进化\n\n```python\n# 开始任务，自动记录\nexec_id = hermes_skill_executor.start_task(\n    \"video-clip\",\n    {\"input\": \"input.mp4\", \"start\": 10, \"end\": 20}\n)\n\n# 一步一步执行，自动记录\nhermes_skill_executor.step(\"load-video\", load_video, path)\nhermes_skill_executor.step(\"cut-segment\", cut, start, end)\nresult = hermes_skill_executor.step(\"export-video\", export, output)\n\n# 完成，自动触发提炼\nhermes_skill_executor.finish_task(True, result)\n\n# 几次之后自动生成技能卡\nskill = hermes_gepa.get_skill_card(\"video-clip\")\nprint(f\"推荐步骤: {skill.steps}\")\nprint(f\"成功率: {skill.success_rate:.1%}\")\n```\n\n## 架构\n\n```\nhermes.py                     # 核心路由器（突触式通信）\n├─ hermes_openclaw.py        # 工作流调度（任务/进度/完成）\n├─ hermes_sessions_integration.py  # sessions_spawn 自动集成\n├─ hermes_agent_insight.py   # 主动记忆 + FTS5 全文检索\n└─ hermes_skill_evolution.py # GEPA 技能自进化\n```\n\n## 控制参数（避免 token 浪费）\n\nGEPA 默认参数：\n- `min_success_samples = 2`  - 最少 2 次成功才提炼\n- `min_new_executions = 3`   - 已有技能后新增 3 次才重新提炼\n- `max_refines_per_task = 10` - 单个任务最多提炼 10 次\n- `min_improvement = 0.05`   - 成功率变化 < 5% 不提炼\n\n自定义：\n```python\nfrom hermes_skill_evolution import GEPASkillEvolution\nmy_gepa = GEPASkillEvolution(\n    min_success_samples=5,\n    max_refines_per_task=5\n)\n```\n\n## 数据存储\n\n- `~/.hermes/insights.db` - 洞察和记忆（SQLite FTS5）\n- `~/.hermes/skills.db` - 技能卡和执行记录\n\n首次运行自动创建，不需要手动初始化。\n\n## 依赖\n\n- Python 3.8+\n- 不需要第三方包，SQLite 内置\n\n## 性能\n\n测试数据（100 Agent，1000 条发布，49500 次投递）：\n- 平均单消息处理：**14 微秒** → 真·毫秒级分发\n\n## License\n\nMIT\n\nFile v1.0.1:_meta.json\n\n{\n  \"ownerId\": \"kn71zja160wvka1tfd4j7sj8fx82m9nm\",\n  \"slug\": \"hermes-agent-skill\",\n  \"version\": \"1.0.1\",\n  \"publishedAt\": 1776407934437\n}\n\nArchive v1.0.0: 8 files, 21538 bytes\n\nFiles: __init__.py (917b), hermes_agent_insight.py (10997b), hermes_openclaw.py (9928b), hermes_sessions_integration.py (8452b), hermes_skill_evolution.py (18934b), hermes.py (12843b), SKILL.md (4145b), _meta.json (137b)\n\nFile v1.0.0:SKILL.md\n\n---\nname: Hermes Agent\ndescription: 突触式多智能体调度 + 主动记忆洞察 + GEPA 技能自进化\nauthor: OpenClaw\nversion: 1.0.0\ntags: [hermes, multi-agent, scheduling, memory, evolution]\n---\n\n# Hermes Agent Skill\n\n> Hermes 协议：极致的\"执行效率\"与\"自我进化\"\n\n## 功能特性\n\n- 🧠 **突触式多智能体调度**：100个Agent同时协同，通信成本降到最低，毫秒级分发\n- 🧩 **主动记忆与自我建模**：自动提取用户偏好习惯，SQLite FTS5 全文检索快速翻旧账\n- 🧬 **GEPA 技能自进化**：多次执行后自动提炼技能卡，越用越聪明\n- ⚡ **极致轻量**：纯Python，零依赖，启动快内存占用低\n- 🔧 **工程化友好**：完美集成 `sessions_spawn`，开箱即用\n\n## 安装\n\n在 OpenClaw 中：\n```\n/install-skill https://github.com/你的仓库/hermes-agent-skill\n```\n\n或者手动放到 `~/.openclaw/workspace/skills/` 即可。\n\n## 快速开始\n\n### 1. 导入\n\n```python\nfrom hermes_agent import (\n    hermes,                 # 核心路由器\n    hermes_workflow,        # 工作流调度\n    hermes_sessions,        # sessions_spawn 集成\n    hermes_insight,         # 记忆洞察数据库\n    insight_extractor,      # 洞察提取器\n    hermes_gepa,            # GEPA 技能进化\n    hermes_skill_executor   # 带自进化的执行器\n)\n```\n\n### 2. 多智能体任务分发\n\n```python\n# spawn 子智能体之后，自动注册 Hermes 订阅\nhermes_sessions.on_agent_spawn(\n    session_key=\"session-code-agent\",\n    agent_id=\"code-review-agent\",\n    hermes_topics=[\"task:code-review\"]\n)\n\n# 提交任务，自动分发给所有订阅了该类型的 Agent\ntask_id = hermes_sessions.submit_task_to_agents(\n    task_type=\"code-review\",\n    creator=\"user\",\n    session_id=\"main\",\n    payload={\"pr\": \"https://github.com/openclaw/openclaw/pull/123\"}\n)\n```\n\n### 3. 主动记忆用户洞察\n\n```python\n# 从对话提取洞察\ninsights = insight_extractor.extract_from_conversation(\n    \"我喜欢用 Python 写脚本，更快，不喜欢重型框架\",\n    context=\"对话上下文\"\n)\n\n# 存储\nfor ins in insights:\n    hermes_insight.add_insight(ins)\n\n# 全文检索\nresults = hermes_insight.search_memory(\"Python\")\n```\n\n### 4. GEPA 技能自进化\n\n```python\n# 开始任务，自动记录\nexec_id = hermes_skill_executor.start_task(\n    \"video-clip\",\n    {\"input\": \"input.mp4\", \"start\": 10, \"end\": 20}\n)\n\n# 一步一步执行，自动记录\nhermes_skill_executor.step(\"load-video\", load_video, path)\nhermes_skill_executor.step(\"cut-segment\", cut, start, end)\nresult = hermes_skill_executor.step(\"export-video\", export, output)\n\n# 完成，自动触发提炼\nhermes_skill_executor.finish_task(True, result)\n\n# 几次之后自动生成技能卡\nskill = hermes_gepa.get_skill_card(\"video-clip\")\nprint(f\"推荐步骤: {skill.steps}\")\nprint(f\"成功率: {skill.success_rate:.1%}\")\n```\n\n## 架构\n\n```\nhermes.py                     # 核心路由器（突触式通信）\n├─ hermes_openclaw.py        # 工作流调度（任务/进度/完成）\n├─ hermes_sessions_integration.py  # sessions_spawn 自动集成\n├─ hermes_agent_insight.py   # 主动记忆 + FTS5 全文检索\n└─ hermes_skill_evolution.py # GEPA 技能自进化\n```\n\n## 控制参数（避免 token 浪费）\n\nGEPA 默认参数：\n- `min_success_samples = 2`  - 最少 2 次成功才提炼\n- `min_new_executions = 3`   - 已有技能后新增 3 次才重新提炼\n- `max_refines_per_task = 10` - 单个任务最多提炼 10 次\n- `min_improvement = 0.05`   - 成功率变化 < 5% 不提炼\n\n自定义：\n```python\nfrom hermes_skill_evolution import GEPASkillEvolution\nmy_gepa = GEPASkillEvolution(\n    min_success_samples=5,\n    max_refines_per_task=5\n)\n```\n\n## 数据存储\n\n- `~/.hermes/insights.db` - 洞察和记忆（SQLite FTS5）\n- `~/.hermes/skills.db` - 技能卡和执行记录\n\n首次运行自动创建，不需要手动初始化。\n\n## 依赖\n\n- Python 3.8+\n- 不需要第三方包，SQLite 内置\n\n## 性能\n\n测试数据（100 Agent，1000 条发布，49500 次投递）：\n- 平均单消息处理：**14 微秒** → 真·毫秒级分发\n\n## License\n\nMIT\n\nFile v1.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn71zja160wvka1tfd4j7sj8fx82m9nm\",\n  \"slug\": \"hermes-agent-skill\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1776398220361\n}","readmeExcerpt":"Skill: hermes agent skill Owner: briefness Summary: 突触式多智能体调度 + 主动记忆洞察 + GEPA 技能自进化 Tags: latest:1.0.3 Version history: v1.0.3 | 2026-04-17T08:22:51.747Z | user - Introduced a new hermes_config.py module for global configuration and privacy controls. - Added support for fully disabling persistence by default; data storage is now opt-in and user-controllable. - Enhanced privacy: defaults, docs, and structure updated t","codeSnippets":[],"executableExamples":[{"language":"text","snippet":"/install-skill https://github.com/你的仓库/hermes-agent-skill"},{"language":"python","snippet":"from hermes_agent import (\n    hermes,                 # 核心路由器\n    hermes_workflow,        # 工作流调度\n    hermes_sessions,        # sessions_spawn 集成\n    hermes_insight,         # 记忆洞察数据库\n    insight_extractor,      # 洞察提取器\n    hermes_gepa,            # GEPA 技能进化\n    hermes_skill_executor,  # 带自进化的执行器\n    hermes_config           # 全局配置（控制持久化开关）\n)"},{"language":"python","snippet":"# spawn 子智能体之后，自动注册 Hermes 订阅\nhermes_sessions.on_agent_spawn(\n    session_key=\"session-code-agent\",\n    agent_id=\"code-review-agent\",\n    hermes_topics=[\"task:code-review\"]\n)\n\n# 提交任务，自动分发给所有订阅了该类型的 Agent\ntask_id = hermes_sessions.submit_task_to_agents(\n    task_type=\"code-review\",\n    creator=\"user\",\n    session_id=\"main\",\n    payload={\"pr\": \"https://github.com/openclaw/openclaw/pull/123\"}\n)"},{"language":"python","snippet":"# 从对话提取洞察\ninsights = insight_extractor.extract_from_conversation(\n    \"我喜欢用 Python 写脚本，更快，不喜欢重型框架\",\n    context=\"对话上下文\"\n)\n\n# 存储\nfor ins in insights:\n    hermes_insight.add_insight(ins)\n\n# 全文检索\nresults = hermes_insight.search_memory(\"Python\")"},{"language":"python","snippet":"# 开始任务，自动记录\nexec_id = hermes_skill_executor.start_task(\n    \"video-clip\",\n    {\"input\": \"input.mp4\", \"start\": 10, \"end\": 20}\n)\n\n# 一步一步执行，自动记录\nhermes_skill_executor.step(\"load-video\", load_video, path)\nhermes_skill_executor.step(\"cut-segment\", cut, start, end)\nresult = hermes_skill_executor.step(\"export-video\", export, output)\n\n# 完成，自动触发提炼\nhermes_skill_executor.finish_task(True, result)\n\n# 几次之后自动生成技能卡\nskill = hermes_gepa.get_skill_card(\"video-clip\")\nprint(f\"推荐步骤: {skill.steps}\")\nprint(f\"成功率: {skill.success_rate:.1%}\")"},{"language":"text","snippet":"hermes.py                     # 核心路由器（突触式通信）\n├─ hermes_config.py           # 全局配置（持久化开关、隐私控制）\n├─ hermes_openclaw.py         # 工作流调度（任务/进度/完成）\n├─ hermes_sessions_integration.py  # sessions_spawn 自动集成\n├─ hermes_agent_insight.py    # 主动记忆 + FTS5 全文检索\n└─ hermes_skill_evolution.py  # GEPA 技能自进化"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: Hermes Agent\ndescription: 突触式多智能体调度 + 主动记忆洞察 + GEPA 技能自进化\nauthor: OpenClaw\nversion: 1.0.3\ntags: [hermes, multi-agent, scheduling, memory, evolution]\n---\n\n# Hermes Agent Skill\n\n> Hermes 协议：极致的\"执行效率\"与\"自我进化\"\n\n## 功能特性\n\n- 🧠 **突触式多智能体调度**：100个Agent同时协同，通信成本降到最低，毫秒级分发\n- 🧩 **主动记忆与自我建模**：自动提取用户偏好习惯，SQLite FTS5 全文检索快速翻旧账\n- 🧬 **GEPA 技能自进化**：多次执行后自动提炼技能卡，越用越聪明\n- ⚡ **极致轻量**：纯Python，零依赖，启动快内存占用低\n- 🔧 **工程化友好**：完美集成 `sessions_spawn`，开箱即用\n- 🔒 **隐私优先**：持久化默认关闭，数据存储完全可控\n\n## 安装\n\n在 OpenClaw 中：\n```\n/install-skill https://github.com/你的仓库/hermes-agent-skill\n```\n\n或者手动放到 `~/.openclaw/workspace/skills/` 即可。\n\n## 快速开始\n\n### 1. 导入\n\n```python\nfrom hermes_agent import (\n    hermes,                 # 核心路由器\n    hermes_workflow,        # 工作流调度\n    hermes_sessions,        # sessions_spawn 集成\n    hermes_insight,         # 记忆洞察数据库\n    insight_extractor,      # 洞察提取器\n    hermes_gepa,            # GEPA 技能进化\n    hermes_skill_executor,  # 带自进化的执行器\n    hermes_config           # 全局配置（控制持久化开关）\n)\n```\n\n### 2. 多智能体任务分发\n\n```python\n# spawn 子智能体之后，自动注册 Hermes 订阅\nhermes_sessions.on_agent_spawn(\n    session_key=\"session-code-agent\",\n    agent_id=\"code-review-agent\",\n    hermes_topics=[\"task:code-review\"]\n)\n\n# 提交任务，自动分发给所有订阅了该类型的 Agent\ntask_id = hermes_sessions.submit_task_to_agents(\n    task_type=\"code-review\",\n    creator=\"user\",\n    session_id=\"main\",\n    payload={\"pr\": \"https://github.com/openclaw/openclaw/pull/123\"}\n)\n```\n\n### 3. 主动记忆用户洞察\n\n```python\n# 从对话提取洞察\ninsights = insight_extractor.extract_from_conversation(\n    \"我喜欢用 Python 写脚本，更快，不喜欢重型框架\",\n    context=\"对话上下文\"\n)\n\n# 存储\nfor ins in insights:\n    hermes_insight.add_insight(ins)\n\n# 全文检索\nresults = hermes_insight.search_memory(\"Python\")\n```\n\n### 4. GEPA 技能自进化\n\n```python\n# 开始任务，自动记录\nexec_id = hermes_skill_executor.start_task(\n    \"video-clip\",\n    {\"input\": \"input.mp4\", \"start\": 10, \"end\": 20}\n)\n\n# 一步一步执行，自动记录\nhermes_skill_executor.step(\"load-video\", load_video, path)\nhermes_skill_executor.step(\"cut-segment\", cut, start, end)\nresult = hermes_skill_executor.step(\"export-video\", export, output)\n\n# 完成，自动触发提炼\nhermes_skill_executor.finish_task(True, result)\n\n# 几次之后自动生成技能卡\nskill = hermes_gepa.get_skill_card(\"video-clip\")\nprint(f\"推荐步骤: {skill.steps}\")\nprint(f\"成功率: {skill.success_rate:.1%}\")\n```\n\n## 架构\n\n```\nhermes.py                     # 核心路由器（突触式通信）\n├─ hermes_config.py           # 全局配置（持久化开关、隐私控制）\n├─ hermes_openclaw.py         # 工作流调度（任务/进度/完成）\n├─ hermes_sessions_integration.py  # sessions_spawn 自动集成\n├─ hermes_agent_insight.py    # 主动记忆 + FTS5 全文检索\n└─ hermes_skill_evolution.py  # GEPA 技能自进化\n```\n\n## 控制参数（避免 token 浪费）\n\nGEPA 默认参数：\n- `min_success_samples = 2`  - 最少 2 次成功才提炼\n- `min_new_executions = 3`   - 已有技能后新增 3 次才重新提炼\n- `max_refines_per_task = 10` - 单个任务最多提炼 10 次\n- `min_improvement = 0.05`   - 成功率变化 < 5% 不提炼\n\n自定义：\n```python\nfrom hermes_skill_evolution import GEPASkillEvolution\nmy_gepa = GEPASkillEvolution(\n    min_success_samples=5,\n    max_refines_per_task=5\n)\n```\n\n## 数据存储\n\n**默认关闭，需显式开启。**\n\n```\n# 方式一：环境变量\nexport HERMES_PERSISTENCE_ENABLE"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn71zja160wvka1tfd4j7sj8fx82m9nm\",\n  \"slug\": \"hermes-agent-skill\",\n  \"version\": \"1.0.3\",\n  \"publishedAt\": 1776414171747\n}"},{"path":"skill-card.md","content":"## Description:\n\nHermes Agent Skill helps OpenClaw agents coordinate work through topic-based routing, session integration, opt-in memory search, and GEPA-style workflow summaries.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[briefness](https://clawhub.ai/user/briefness)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers and agent builders use this skill to coordinate multiple OpenClaw subagents, distribute task events, track progress, search optional local memory, and summarize repeated successful workflows into reusable skill cards.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Shared agent routing and session integration can expose sensitive task payloads, tags, metadata, or step results across agents.\n\nMitigation: Review before installing in mixed-trust environments, avoid secrets in task data, and use the skill only with agents and sessions that are intended to share this information.\n\nRisk: When persistence is enabled, local ~/.hermes databases can retain sensitive memory and execution data.\n\nMitigation: Keep persistence disabled unless needed, leave sensitive filtering enabled, and treat the local databases as sensitive files.\n\n## Reference(s):\n\n- [ClawHub Skill Page](https://clawhub.ai/briefness/skills/hermes-agent-skill)\n- [Publisher Profile](https://clawhub.ai/user/briefness)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, code, shell commands, configuration, guidance]\n\n**Output Format:** [Markdown guidance with Python code examples and environment-variable configuration.]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [May create local SQLite databases under ~/.hermes only when persistence is explicitly enabled.]\n\n## Skill Version(s):\n\n1.0.3 (source: server release metadata and SKILL.md frontmatter)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment."}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"突触式多智能体调度 + 主动记忆洞察 + GEPA 技能自进化 Skill: hermes agent skill Owner: briefness Summary: 突触式多智能体调度 + 主动记忆洞察 + GEPA 技能自进化 Tags: latest:1.0.3 Version history: v1.0.3 | 2026-04-17T08:22:51.747Z | user - Introduced a new hermes_config.py module for global configuration and privacy controls. - Added support for fully disabling persistence by default; 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