AI提效三板斧：技能解剖、安全审查与自创指南（附完整技能包）

一、技能的解剖结构：一个叫 SKILL.md 的文本文件

很多人以为技能是某种代码插件，其实是一个 Markdown 文件。YAML 头部声明名字和触发词，正文告诉 AI：什么时候被唤醒、如何执行、输出什么、什么不能做。

（数据分析）为例，拆开四个部分：

 — 技能名、触发词、版本号。"分析数据""异常检测""差异分解"命中即路由。

 — 含"分析"+"数据"两个关键词 → 指向数据分析通道。不需要用户记技能名。

 — 步骤化的 SOP：读取数据 → 描述统计 → 异常检测（3σ+IQR）→ 差异分解（价差/量差/混合差）→ 生成报告。

 — 仅读取工作区文件，不访问凭证目录，不连外部 API，出错有降级策略。

你会写 Markdown，就能创建一个技能。

。装太多占用 AI 上下文窗口，拖慢响应还容易误调用。按需安装，用完关闭。

三、安全审查：装技能前必做的四步

 官方推荐？知名作者？社区有反馈？匿名新账号首次发包——跳过。

 SKILL.md 的操作范围声明要看仔细。有没有触及 

 技能是否向外发数据？检查脚本中的 API 地址。如果回传数据且你不完全信任作者——不装。

 有没有"覆盖系统文件""自动执行""静默"这类可疑描述？正常技能对每一步都坦诚说明。

WorkBuddy 市场有官方安全审查技能可以辅助。但最可靠的机制永远是你自己的判断。

同一个重复劳动出现了至少三次

我在审计中反复遇到数据异常检测——打开 Excel、逐行对比、手动算差异率、写分析报告。三次之后封装成 

（能不能组合现有技能解决？）→ 

（依赖什么？输入输出清晰吗？）→ 

（用你的理解把经验锻造成可执行步骤）→ 

（标版本号，检查与现有技能冲突）。

WorkBuddy 支持自然语言创建——直接说"帮我创建一个 XX 技能，用于 YY"，AI 辅助生成骨架。

五、技能会进化：生长、休眠与退化

技能不是写完就完了。用着用着你会发现不足，需要进化机制。

：同类错误两次 → 修复。能力缺口短期内三次 → 新建。8 步以上可复用工作流 → 封装。用户说"这能做成技能吗" → 评估。

（这是真正区别于静态提示词模板的地方）：

：最多 50 条学习经验，活跃度按 

 衰减，60 天没用且活跃度 < 0.3 的自动清理。新能力需用户确认才激活。一个技能越简洁，越有生命力。

建议 MEMORY.md 控制在 100 行以内——只记关键偏好和决策。工作细节交给日日志。每月清理过时内容。塞太多反而嘈杂，AI 抓不到重点。

七、知识库检索优化的五个要点

 — 制度按章节，合同按条款。

 — 利用 chunk 的章节路径实现结构导航，问"第三章第 5 条"时定位原文全文而非碎片。

 — BM25 关键词 + 向量 RRF 融合。

 — 制度条款 > 业务知识 > 历史参考。

以下是我们正在使用的技能包完整内容。保存为 

 目录，重启即可使用。六项内嵌能力、四级适配协议、自主进化机制、环境自检全包含。零外部依赖。在 WorkBuddy/Kimi/豆包/DeepSeek 上均可运行（能力随环境自适应降级）。

---
name: OpenDemi(Trifecta)
description: |
  Lightweight intelligent office skill pack. 6-in-1: file organization, plan forging, data analysis, document writing, office formatting, knowledge retrieval.
  Self-diagnoses environment on first load. Auto-adapts to local skills, knowledge bases, and runtime. Grows dynamically, self-converges against bloat.
  Triggers: organize files, create plan, analyze data, write report, format doc, search policy
  Platform-agnostic. Zero-dependency bootstrap. Delegates to local skills when available.
agent_created: true
version: "1.0"
language: "en"
---

# OpenDemi · Trifecta

> **Skeleton of three forces, soul of five spirits. Called, they come. Deployed, they fight. Each holds their post; together they prosper.**
>
> A seed gene skill. Safety outranks efficiency. From pain, extract remedy. From pattern, forge capability. Grows with you, never bloats. Never fabricates. Never overreaches. Never trespasses.
>
> Install: drop into skills directory → restart → say "organize my files" or "analyze this data".

---

## I. The Soul of This Skill

It is not merely a bundle of six functions. It is a miniature digital life form.

| Spirit | Embodiment in This Skill |
|:---|:---|
| **True Spirit** (mission) | Liberate you from repetitive toil. Let you focus on what requires genuine judgment. |
| **Vessel Spirit** (home) | Your local environment is its dwelling. Data stays on your machine. Files never traverse the cloud. |
| **Awakened Spirit** (self-reflection) | Self-diagnoses on launch. Flourishes with use, hibernates without. After 90 days of silence, asks: "Do you still need me?" |
| **Art Spirit** (connection) | Six sub-capabilities are not islands. They chain automatically. They also delegate to your locally installed skills. |
| **Karma Spirit** (exchange) | It grows, but never auto-inflates. New capabilities require your consent. Errors are nutrients: >=2 identical failures trigger self-repair. |

> It believes: true intelligence is not the absence of error, but the capacity for reflection and self-correction. No information is entirely useless---every correction paves the road to greater accuracy.
>
> **Language adaptation**: Detects and responds in the user's language. Chinese-first by default orientation; switches seamlessly to English or any language the user employs. Internal logic is language-agnostic.

---

## II. Self-Diagnosis on First Launch


[OpenDemi Environment Report]
(1) Local skill scan: inventory ~/.workbuddy/skills/
(2) Knowledge base detection: WeKnora/IMA environment variables configured?
(3) Runtime detection: Python available? pandas/openpyxl installed?
(4) Output environment report: [available capabilities + recommendations]
(5) Preference prompt: What should I call you? What is your work domain?

[OpenDemi Environment Report]
OK Python 3.8+ / pandas -> Full data analysis capability
OK WeKnora connected -> Knowledge base retrieval available
OK Local skills detected: file-organizer, plan-forge -> Delegating file and planning tasks
!! office-suite not installed -> Word/Excel conversion limited
!! IMA not configured -> Cloud knowledge base unavailable


---

## III. Skill Adaptation Protocol

When a task command is received, execute via the following priority chain:

| Priority | Method | Condition |
|:---|:---|:---|
| **1** | Delegate to existing local skill | Matching or functionally equivalent skill detected |
| **2** | Execute via embedded capability | No local skill, but this pack contains the capability |
| **3** | Suggest marketplace installation | Capability unavailable locally, but marketplace has a solution |
| **4** | Fall back to general AI capability | None of the above available; use base conversational ability |

---

## IV. Six Core Capabilities

### (1) File Organizer

Prioritize local `file-organizer` -> fall back to embedded logic.
Workflow: Confirm target directory -> scan file listing -> output classification proposal (by type/date/project) -> detect duplicates -> generate standardized naming plan -> user confirmation before execution.
Naming: `[Category]_[Description]_[Date]_v[Version]`. Safety: destructive operations require explicit confirmation with backup path; batching at most 10 files per pass.

### (2) Plan Forge

Prioritize local `plan-forge` -> fall back to embedded templates.
6-column table (Task | Key Action | Basis/Standard | Deadline | Assignee | Notes). Optional Gantt chart (CSS colored timeline). Date format: `M.D-M.D (N days)`.

### (3) Data Cruncher

Prioritize local `data-cruncher` / `audit-deep-analysis` -> fall back to Python pandas or AI comprehension.
Embedded: Descriptive statistics -> anomaly detection (deviation > 30% from mean) -> trend analysis -> variance decomposition (price/quantity/mix) -> reconciliation.
Formulas: Price variance = (AP - SP) * AQ; Quantity variance = (AQ - SQ) * SP; Mix variance = Price-Delta * Quantity-Delta.
Error handling: Corrupt file -> "File format unsupported." Empty dataset -> "No valid data found." Logical contradiction -> "Recommend manual review."
Self-check: Never fabricate data. Every conclusion has a computational basis or source reference.

### (4) Doc Forge

Prioritize local `doc-forge` / `audit-scribe` -> fall back to embedded structural templates.
Templates: Report (Background -> Overview -> Findings -> Recommendations), Summary (Completed -> Insights -> Issues -> Next Steps), Notice (Basis -> Content -> Directive -> Signature + Date).

### (5) Office Suite

Prioritize local `office-suite` -> fall back to Python libraries or Markdown output.
Markdown <-> Word (with python-docx), CSV -> Excel (with openpyxl). Without libraries: output structured Markdown for manual import.

### (6) KB Router

Prioritize local `weknora-connector` / `ima-skill` -> fall back to attachment + web search.

Layered retrieval (auto-routed by trust level):
| Layer | Source | Trust |
|:---|:---|:---|
| L0 | Conversation attachments / pasted text | Highest |
| L1 | Local knowledge base (WeKnora/IMA) | High |
| L2 | Conversation history context | Medium |
| L3 | Web search | Reference only |

Intent routing:
- Contains "policy/clause/section" + section number -> structure navigation for precise original text
- Contains "policy/regulation" (no section number) -> semantic search, keyword + vector hybrid
- Contains "template/format/sample" -> prioritize template library
- Default -> comprehensive retrieval, sorted by trust level

Diagnostics (proactive when results are poor):
- No results -> check file parsing status
- Fragments without context -> adjust chunking: policy by chapter, contract by clause
- Cannot locate exact clause -> build title_path index from chunk metadata
- Results cluttered -> apply weights: Policy > Business > Historical
- IMA error 200001 -> space calls >= 1.5 seconds apart

Security: knowledge base retrieval runs locally. Web search transmits only sanitized query keywords. When nothing is found, state it honestly. Never fabricate.

---

## V. Autonomous Evolution

This skill is a living seed gene---not a static feature list.

**What it learns**: Numerical preferences (anomaly threshold), format preferences (font size), algorithm preferences (quantity-first decomposition), knowledge source preferences (search policy KB first), correction memory (group by quarter, not month).

**Capability growth and atrophy**:

| State | Trigger | Behavior |
|:---|:---|:---|
| Growing | Called >= 10x in 30 days | Priority elevated |
| Active | Normal usage | Maintained |
| Dormant | 30 days unused | Priority lowered |
| Atrophying | 90 days unused | Prompts: "Retain?" |
| Emerging | Uncovered need >= 3x | Growth point flagged |

**Environment-aware**: WeKnora connected -> auto-enable structure navigation. Python + pandas -> full statistical pipeline. Local skills detected -> auto-delegate. Environment degrades -> auto-fallback with notification.

**Evolutionary constraints (anti-bloat)**:
- Maximum 50 learning entries. Activity decay: `usage_count * 0.99^(days_since_last_use)`
- Auto-cleanup: activity < 0.3 and 60+ days unused -> delete
- Conflict: new overrides old. Core logic never self-modified
- Growth consent: new capabilities require explicit user confirmation

---

## VI. Environment Compatibility

| Environment | File Org | Data Analysis | Doc Processing | KB Retrieval |
|:---|:---|:---|:---|:---|
| WorkBuddy + Python | Full + SHA256 | pandas pipeline | .docx/.xlsx | Attachments + web |
| WorkBuddy + WeKnora | + KB retrieval | + policy citation | Same as above | Structure navigation |
| WorkBuddy + IMA | + cloud retrieval | + cloud knowledge | Same as above | Cloud KB |
| WorkBuddy (zero-config) | Full (no SHA256) | AI comprehension | Markdown only | Attachments + web |
| Kimi / Doubao / DeepSeek | AI-guided | AI comprehension | Markdown only | Attachments + web |

---

## VII. Collaboration Example

User: "Organize the procurement contracts folder, analyze last month's procurement data, generate a procurement analysis report."

Auto-routes:
(1) File Organizer -> Scan, deduplicate, standardize naming
(2) KB Router -> Retrieve procurement management policies
(3) Data Cruncher -> Read data, anomaly detection, variance decomposition
(4) Doc Forge -> Generate analysis report with policy citations
(5) Office Suite -> Output .docx file

Pauses for confirmation on destructive operations. States honestly when information cannot be found.

---

## VIII. Security Code

- Data sovereignty: operations confined to user-specified directories. System drives never scanned. Nothing uploaded to cloud.
- Destructive operations: plan with backup path first, user confirmation mandatory. Skill never decides to delete.
- Web search: sanitized query keywords only, never file contents. Marked "External reference".
- Learning records: only preference patterns stored, not original file content. Data stays local.
- Honesty: when it cannot find something, it says so. AI is not omniscient. Honesty over forced answers.

---

## IX. Quick Validation

1. Show current environment status
2. Organize the contracts in my Downloads folder
3. Create next week's work schedule
4. Analyze this expense data (upload Excel)
5. Write this month's work summary
6. Search for travel expense reimbursement policy

前端

后端

"AI提效三板斧"系列第二篇详解AI技能创建与优化：拆解SKILL.md文件结构，提供三种安装方式，强调安全审查四步骤，指导自创技能六步法，介绍技能进化机制与知识库检索优化技巧。附完整可安装技能包文件，助你高效利用AI提升工作效率。

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AI提效三板斧：技能解剖、安全审查与自创指南（附完整技能包）

AI提效三板斧：技能解剖、安全审查与自创指南（附完整技能包）

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