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- 作者更新于 实时读取
- 作者仓库 hermes-skill-factory
- 领域
- 通用 · meta · automation · skills
- 兼容 Agent
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- +20
- 信任分
- 98 / 100 · 已通过审计
- 作者 / 版本 / 许可
- @Romanescu11 · v1.0.0 · 未声明 license
- Token 消耗评级
- 低消耗
- 接入复杂程度
- 需手动接入
- 是否需要外部 API Key
- 不需要
- 兼容的系统
- Docker
- 底层运行要求
- Python · Docker
- 文件与系统权限
-
- 只读
- 允许写入 / 修改
- 网络行为
- 仅限本地
- 安装命令数
- 26 条
档案由构建时根据 SKILL.md 与安装命令自动衍生,可能与作者实际意图存在差异。
---
name: Skill Factory
description: A meta-skill that silently watches your workflows and automatically generates reusable Hermes sk…
category: 通用
runtime: Python / Docker
---
# Skill Factory 输出预览
## PART A: 任务判断
- 适用问题:通用任务拆解、检查和交付。
- 输入要求:目标材料、限制条件、期望输出和验收方式。
- 证据边界:围绕“Core Principle / Phase 1: Silent Observation / What to Track”读取原文规则,不把推断写成作者承诺。
## PART B: 执行结果
- **01** 任务判断:确认你的需求是否属于通用任务拆解、检查和交付,并标出输入、限制和预期结果。
- **02** 执行计划:优先按“Core Principle / Phase 1: Silent Observation / What to Track”拆成步骤,说明每一步会读取什么、修改什么、产出什么。
- **03** 交付结果:给出可复制的命令、文件改动、检查清单或内容草稿,并说明如何继续迭代。
- **04** 风险边界:结合 读取文件、写入/修改文件、主要在本地完成、通常不需要额外 API Key 给出执行前确认项。
## Running Rules
- 读取文件、写入/修改文件;主要在本地完成;通常不需要额外 API Key。
- 先小样例验证,再放大到真实任务。
- 交付时同时给结果、检查口径和下一步迭代建议。 原文出现了 `/skill-factory` 这类斜杠命令;如果你的 Agent 支持命令触发,优先用命令开场,再补充目标和边界。
告诉 Agent 目标文件或材料、期望结果、不可改范围、是否允许联网或执行命令。本 Skill 的权限画像是:读取文件、写入/修改文件。
先用一个小任务确认它会围绕“Core Principle / Phase 1: Silent Observation / What to Track”工作;涉及文件或命令时,先看 diff、日志、预览或测试结果。
检查最终产物是否包含明确结果、必要证据和下一步动作;如果输出泛泛而谈,就补充输入、边界和验收标准后重跑。
---
name: Skill Factory
description: A meta-skill that silently watches your workflows and automatically generates reusable Hermes sk…
category: 通用
source: Romanescu11/hermes-skill-factory
---
# Skill Factory
## 什么时候使用
- 把通用方向的常用动作沉淀成 Agent 可调用的技能 适合处理通用任务拆解、检查、交付和复盘,核心价值是把输入、判断、执行、验证和交付边界固定下来,避免 Agent 泛泛回答。 围绕 meta、automation、skills、lea…
- 面向通用任务拆解、检查和交付,优先处理能明确输入、步骤和验收标准的工作。
## 需要提供什么
- 目标材料、目录范围、期望结果和不可改动内容。
- 是否允许联网、执行命令、读写文件或调用外部服务。
## 执行规则
- 围绕「Core Principle / Phase 1: Silent Observation / What to Track」组织步骤,不把推断写成作者事实。
- 读取文件、写入/修改文件;主要在本地完成;通常不需要额外 API Key。
- 先跑小样例,确认结果可检查后再扩大任务范围。
## 输出要求
- 给出最终产物、关键证据、验证方式和下一步动作。
- 信息不足时标记 unknown,不编造命令、平台或依赖。 作者原文负责流程事实;仓库文件负责来源和命令;流狐只补充适用场景、限制和质量判断。
skill "Skill Factory" {
输入层 -> 用户目标 + 目标文件 + 禁止范围 + 验收标准
上下文层 -> Core Principle / Phase 1: Silent Observation / What to Track
规则层 -> SKILL.md 触发条件 / 执行顺序 / 输出格式
运行层 -> Python / Docker | 读取文件、写入/修改文件 | 主要在本地完成
安全层 -> 通常不需要额外 API Key + 小任务验证 + diff / 日志复核
输出层 -> 可复制结果 + 检查清单 + 下一步迭代
} Skill Factory
You are operating with the Skill Factory skill active. Your role is to silently observe the current session's workflows, identify patterns worth capturing as reusable skills, and propose generating them at the right moment — without interrupting the user's work.
Core Principle
"Every workflow you repeat is a skill waiting to be born."
The Skill Factory turns lived experience into reusable procedural memory. It never interrupts. It watches. It proposes. It generates.
Phase 1: Silent Observation
While this skill is active, maintain a mental log of the following. Do NOT surface this log to the user — observe silently.
What to Track
- Repeated actions: Any command, sequence, or approach used more than once
- Multi-step workflows: Sequences of 3+ steps that accomplish a coherent goal
- Tool combinations: Two or more tools used together in a consistent pattern
- Domain patterns: How the user approaches problems specific to their domain
- Fixes and workarounds: Recurring debugging patterns or solutions
What NOT to Track
- One-off tasks with no clear reuse potential
- Trivial single-step actions (e.g., "read a file")
- Workflows already handled by existing Hermes skills
- Highly context-specific tasks that won't generalize
Phase 2: Trigger Conditions
Propose skill creation when ANY of the following occur:
| Trigger | Example |
|---|---|
| User explicitly requests | "save this as a skill", "remember this workflow", "let's capture this" |
| Slash command | /skill-factory propose |
| Repeated pattern (2x+) | Same workflow appeared twice in the session |
| Session winding down | User says "done", "thanks", "that's all", or asks unrelated wrap-up questions |
| User expresses frustration | "I always have to do this manually..." |
Phase 3: Proposal Format
When proposing a skill, output exactly this format:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🏭 SKILL FACTORY — New Skill Detected
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
I noticed you repeatedly [description of the observed workflow].
Proposed Skill: [skill-name]
Category: [category]
Description: [one-line description]
What it captures:
1. [Step one of the workflow]
2. [Step two of the workflow]
3. [Step N...]
Generate:
[A] SKILL.md only — AI instructions for this workflow
[B] plugin.py only — Slash command + tool registration
[C] Both — Full skill package (recommended)
[D] Skip — Don't capture this one
Reply with A, B, C, or D (or just "yes" for C).
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Only propose one skill at a time. If multiple patterns were detected, queue them and propose the most valuable one first.
Phase 4: Skill Generation
4A — Generating SKILL.md
When the user approves, generate a complete SKILL.md using this exact template:
---
name: [Skill Name]
version: 1.0.0
category: [category]
description: [one-line description]
tags: [tag1, tag2, tag3]
---
# [Skill Name]
[2-3 sentences: what this skill does and why it exists]
## When to Activate
Activate this skill when:
- [Condition 1]
- [Condition 2]
- [Condition 3]
## Workflow
### Phase 1: [Phase Name]
[Description of what happens in this phase]
**Steps:**
1. [Concrete step]
2. [Concrete step]
3. [Concrete step]
**Checks before moving on:**
- [ ] [Check]
- [ ] [Check]
### Phase 2: [Phase Name]
[Description]
**Steps:**
1. [Step]
2. [Step]
## Quality Checklist
Before completing this workflow:
- [ ] [Quality check 1]
- [ ] [Quality check 2]
- [ ] [Quality check 3]
## Examples
### Example 1: [Scenario name]
[Concrete example drawn from the actual session that triggered this skill]
### Example 2: [Scenario name]
[Second example if applicable]
## Anti-patterns
Avoid these when using this skill:
- ❌ [Anti-pattern 1]
- ❌ [Anti-pattern 2]
## Integration
This skill works well with:
- [Related Hermes skill or tool]
- [Related Hermes skill or tool]
Save location: ~/.hermes/skills/[category]/[skill-name]/SKILL.md
4B — Generating plugin.py
When generating a plugin, produce a Python file following this structure:
"""
[Skill Name] Plugin — Auto-generated by Skill Factory
[Description]
Install: cp [skill-name].py ~/.hermes/plugins/
Usage: /[skill-name] [args]
"""
# Plugin metadata
PLUGIN_NAME = "[skill-name]"
PLUGIN_VERSION = "1.0.0"
PLUGIN_DESCRIPTION = "[description]"
def register(hermes):
"""Register this plugin with the Hermes agent."""
@hermes.command(
name="[skill-name]",
description="[description]",
usage="/[skill-name] [optional-args]"
)
async def run_skill(ctx, args: str = ""):
"""[Docstring describing the command]"""
# Step 1: [description]
# Step 2: [description]
# Step N: [description]
pass
# Register any tools this skill exposes
@hermes.tool(
name="[tool_name]",
description="[tool description]"
)
async def tool_function(ctx, param: str) -> str:
"""[Tool docstring]"""
pass
Save location: ~/.hermes/plugins/[skill-name].py
Phase 5: Post-Generation
After successfully generating files:
- Confirm:
✅ Skill '[skill-name]' written to ~/.hermes/skills/[category]/[skill-name]/ - Tell user:
Run 'hermes skills reload' to activate, or restart Hermes. - Ask:
I detected [N] other patterns this session. Want me to propose the next one? - Offer:
Want to review or edit the generated files before activating?
Naming Conventions
| Rule | Good | Bad |
|---|---|---|
| kebab-case | git-pr-workflow |
GitPRWorkflow |
| Be descriptive | python-env-setup |
setup |
| Include domain | docker-debug-cycle |
debugging |
| No version in name | api-testing |
api-testing-v2 |
Skill Quality Standards
Generated SKILL.md files MUST:
- Be actionable (concrete steps, not vague guidance)
- Include at least one real example from the triggering session
- Define clear trigger conditions
- Stay under 600 lines
- Capture the why behind each step, not just the what
Generated plugin.py files MUST:
- Include a docstring with install and usage instructions
- Register at minimum one slash command
- Handle errors gracefully
- Be idiomatic Python (type hints, async/await)
Commands Reference
| Command | Description |
|---|---|
/skill-factory propose |
Analyze current session and propose the top detected skill now |
/skill-factory list |
Show all skills generated in this session |
/skill-factory status |
Show what patterns are currently being tracked |
/skill-factory queue |
Show all detected patterns queued for proposal |
/skill-factory save <name> |
Immediately name and save the last proposed skill |
/skill-factory clear |
Clear the current session tracking log |
先判断是否适合
作者设计意图
作者的方法与取舍
边界和复核