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- 不需要
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- 未声明(默认跨平台)
- 底层运行要求
- Python
- 文件与系统权限
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- 只读
- 允许写入 / 修改
- Shell 执行
- 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。
需要注意: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: skill-compiler
description: Automatic solved-to-skill compiler — detects novel task completions and autonomously drafts new…
category: AI 智能
runtime: Python
---
# skill-compiler 输出预览
## PART A: 任务判断
- 适用问题:提示词、Agent 工作流、模型评估或自动化推理。
- 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“The Problem This Solves / When to Use / Automatic Trigger (Post-Task Detection)”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提示词、Agent 工作流、模型评估或自动化推理,并标出输入、限制和预期结果。
- **02** 执行计划:优先按“The Problem This Solves / When to Use / Automatic Trigger (Post-Task Detection)”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、执行终端命令、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、执行终端命令;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 `/end`、`/audit`、`/steal` 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、执行终端命令。
先用一个小任务确认它会围绕“The Problem This Solves / When to Use / Automatic Trigger (Post-Task Detection)”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-compiler
description: Automatic solved-to-skill compiler — detects novel task completions and autonomously drafts new…
category: AI 智能
source: winstonkoh87/Athena-Public
---
# skill-compiler
## 什么时候使用
- 把 AI / Agent方向的常用动作沉淀成 Agent 可调用的技能 适合处理AI Agent、提示词、模型评估与自动化推理,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。 把任务拆成可执行、可检查…
- 面向提示词、Agent 工作流、模型评估或自动化推理,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「The Problem This Solves / When to Use / Automatic Trigger (Post-Task Detection)」组织步骤,不把推断写成作者事实。
- 读取文件、写入/修改文件、执行终端命令;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-compiler" {
输入层 -> 用户目标 + 目标文件 + 禁止范围 + 验收标准
上下文层 -> The Problem This Solves / When to Use / Automatic Trigger (Post-Task Detection)
规则层 -> SKILL.md 触发条件 / 执行顺序 / 输出格式
运行层 -> Python | 读取文件、写入/修改文件、执行终端命令 | 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 Compiler — Solved-to-Skill Automation
Source: Hermes Agent by Nous Research (May 2026) Core Claim: "It's the only agent with a built-in learning loop — it creates skills from experience." Athena Adaptation: Hermes does this via Python (
agent/curator.py+tools/skill_usage.py). Athena does it via workflow-level pattern detection + markdown SKILL.md generation.
The Problem This Solves
Athena currently relies on manual insight filing during /end sessions. The [S] and [V] markers in session logs capture learnings, but they remain trapped in session logs — they don't become reusable skills automatically.
Hermes solved this: when the agent completes a novel task, it automatically creates a new skill from the solution, so the same problem class never requires re-derivation.
When to Use
Automatic Trigger (Post-Task Detection)
After any task completion where ALL of the following are true:
- Novelty: The task required a solution path not covered by any existing skill
- Complexity: Task took ≥5 agent turns OR involved ≥3 tool calls
- Success: User confirmed the solution worked (explicit or implicit — no corrections in final 2 turns)
- Reusability: The solution generalizes beyond this specific instance
Manual Trigger
User says: "compile this into a skill", "save this as a skill", "I want to remember how we did this"
Execution Flow
Phase 1: Pattern Extraction (Analysis)
Perform private analysis in <analysis> tags (not written to files):
<analysis>
1. What was the PROBLEM CLASS? (Not the specific instance)
- e.g., "Pairs trading dashboard with cointegration analysis"
- NOT "Dashboard 4-decimal rounding fix"
2. What was the SOLUTION ARCHITECTURE?
- Key steps in order
- Tools/APIs used
- Decision points and their resolution criteria
3. What were the FAILURE MODES encountered?
- What went wrong initially?
- What heuristics resolved it?
4. What is the REUSE SURFACE?
- When would someone encounter this problem class again?
- What context_trigger keywords would match?
5. OVERLAP CHECK
- Which existing skills partially cover this?
- Is this better as a new skill or a subsection of an existing one?
</analysis>
Phase 2: Skill Draft Generation
Generate a complete SKILL.md with Athena-standard 5W1H frontmatter:
---
name: [kebab-case-name]
description: "[One-line description of what the skill does]"
vibe: "[One-line emotional hook]"
context_trigger: "[comma-separated trigger keywords]"
auto-invoke: false
model: default
source: "Compiled from session [SESSION_ID] on [DATE]"
compiled_from: "[session log path]"
---
# [Skill Name] — [Subtitle]
> **Compiled**: [DATE] from session [SESSION_ID]
> **Problem Class**: [Description of the general problem this solves]
## When to Use
[Trigger conditions — when should the agent invoke this skill?]
## Solution Architecture
### Step 1: [Phase Name]
[What to do, with specifics]
### Step 2: [Phase Name]
[What to do, with specifics]
## Failure Modes & Mitigations
| Failure | Mitigation |
|---------|------------|
| [What can go wrong] | [How to recover] |
## Validated Patterns
- [V] [Pattern]: [Why it works] | Reapply: [When]
## References
- [Session log](path/to/session/log)
- [Related skill](path/to/related/skill)
Phase 3: Integration
- Write the skill to
.agent/skills/[name]/SKILL.md(orexamples/skills/[category]/[name]/SKILL.mdfor public) - Update skill index with the new entry
- Update
AGENTS.mdskill table (if context_trigger present) - Notify user: "📦 Compiled new skill:
[name]from this session. Review at [path]."
Curator Integration (Stolen: Hermes agent/curator.py)
Lifecycle States
Compiled skills follow a lifecycle identical to Hermes' curator model:
| State | Criteria | Action |
|---|---|---|
| active | Created or used within 30 days | Normal operation |
| stale | No invocation for 30+ days | Flag for review at next /audit |
| archived | No invocation for 90+ days | Move to archive directory |
Invariants (from Hermes)
- Never auto-delete — maximum destructive action is archive
- Pinned skills are exempt — manual pin via
pinned: truein frontmatter - Only touch compiled skills — bundled/manual skills are off-limits
- Archive is recoverable — archive directory with successor mapping in README.md
Umbrella Consolidation Rule (Stolen: Hermes Curator Prompt)
"A collection of hundreds of narrow skills where each one captures one session's specific bug is a FAILURE of the library — not a feature."
When compiling a new skill, first check if it belongs as a subsection of an existing umbrella skill rather than a standalone entry:
- PREFIX CLUSTER CHECK: Does the new skill share a first word or domain keyword with 2+ existing skills?
- CLASS-LEVEL CHECK: Would a maintainer write this as one skill with labeled subsections, or N separate skills?
- If the answer is "one skill", absorb into the existing umbrella and add a
references/entry instead.
Anti-Patterns
- ❌ Compiling trivial tasks (< 5 turns, single tool call)
- ❌ Compiling tasks that are already covered by existing skills
- ❌ Creating overly specific skills tied to one instance (use umbrella pattern instead)
- ❌ Compiling without user confirmation of success
- ❌ One-session-one-skill micro-entries — consolidate into class-level umbrellas
Hermes Comparison
| Feature | Hermes | Athena |
|---|---|---|
| Skill creation | Automatic (Python skill_manage) |
Workflow-triggered (this SKILL.md) |
| Skill lifecycle | curator.py (7-day review cycle) |
/audit + archive directory |
| Skill evolution | DSPy + GEPA (self-evolution repo) | Manual via /steal + session learnings |
| Skill storage | ~/.hermes/skills/ (SQLite telemetry) |
.agent/skills/ (git-tracked) |
| Consolidation | LLM-driven umbrella-ification pass | Manual during /audit |
Athena advantage: Skills are version-controlled in git, not SQLite. Every skill change has a commit hash, blame, and diff history. Hermes can't git blame a skill edit.
References
- Hermes Agent Curator — Source of lifecycle states + umbrella consolidation pattern
- Hermes Self-Evolution — DSPy + GEPA evolutionary optimization (future steal candidate)
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