Xpersona
agentCLAWHUBUnverified

Deep Work Orchestrator

Integrates personal productivity data with deep work principles to create tailored focus schedules, optimize energy-task matching, and reduce distractions. Skill: Deep Work Orchestrator Owner: kingofzhao Summary: Integrates personal productivity data with deep work principles to create tailored focus schedules, optimize energy-task matching, and reduce distractions. Tags: latest:1.0.0 Version history: v1.0.0 | 2026-04-01T21:08:41.061Z | auto Deep Work Orchestrator 1.0.0 – Initial Release - Integrates Cal Newport's Deep Work theory with personal productivity data for per

OpenClaw
View on ClawHubHomepage

Rank

62

Safety

84

Downloads

2.5k

Updated

Oct 9, 2026

Version

1.0.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 2.5K downloads reported by the source. Last updated 10/9/2026.

Avoid when

  • Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor
Clawhubvendor · observed Oct 9, 2026
Protocol compatibility
OpenClawcompatibility · observed Oct 9, 2026
Adoption signal
2.5K downloadsadoption · observed Oct 9, 2026
Latest release
1.0.0release · observed Apr 1, 2026
Handshake status
UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s170jhyhajh4wd5e20y7r5mw2n83yyy9:deep-work-orchestrator
  1. Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
  2. Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-kingofzhao-deep-work-orchestrator/snapshot"

Documentation

CLAWHUB

4,166 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

# Deep Work Orchestrator

> 深度工作编排器 — 将 Cal Newport 的深度工作理论与个人生产力数据结合，构建个性化的专注力管理系统。

## Description

整合时间管理、注意力科学、环境设计和认知负荷理论，为知识工作者生成个性化的深度工作计划。通过分析个人工作模式、精力曲线和干扰模式，自动编排最优深度工作时段。

## Usage

当用户需要：
- 规划深度工作时间块
- 分析自己的注意力模式
- 减少浅层工作占比
- 建立深度工作习惯
- 优化创作/编码时段

使用本 Skill。

## Key Concepts

### 1. 注意力经济学框架
- **注意力预算模型**：每天高质量注意力约4-5小时，需要像预算一样分配
- **注意力税**：每次上下文切换消耗23分钟恢复时间（Gloria Mark研究）
- **复利专注**：连续深度工作的认知收益呈非线性增长，前30分钟热身，30-90分钟为峰值区

### 2. 环境触发设计
- **感官锚定**：固定音乐/光线/位置 → 触发"深度模式"的巴甫洛夫响应
- **数字护城河**：三层防御（通知静默→应用屏蔽→网络隔离），每层对应不同深度级别
- **社交契约信号**：耳机戴法/状态标记/物理信号让他人自动回避

### 3. 精力-任务匹配矩阵
```
        高精力    低精力
重要    深度区    防御区
不重要  流速区    丢弃区
```
- **深度区**：创作、编码、写作、战略思考
- **流速区**：学习新技能、探索性工作
- **防御区**：关键决策（低精力时易出错，需额外检查）
- **丢弃区**：委托、延迟或删除

## Instructions

1. 询问用户典型工作日结构和精力曲线
2. 识别当前浅层工作占比（目标 <30%）
3. 设计3种深度工作模板（晨间型/午间型/晚间型）
4. 创建环境触发方案
5. 建立每周深度工作时数追踪

## Examples

**输入**: "我是程序员，每天被会议打断很多"
**输出**: 会议批处理策略 + 编码深度块 + 异步沟通协议 + 精力低谷会议安排

## Boundaries

- 不替代专业心理咨询（ADHD等注意力障碍需医学支持）
- 框架灵活适配，不教条执行
- 尊重用户现有工作约束

## References

- Cal Newport《Deep Work》
- Gloria Mark 注意力切换研究（UC Irvine）
- Mihaly Csikszentmihalyi 心流理论

_meta.json

{
  "ownerId": "kn70bvfh9dnvbsya0s25fe751983zgcj",
  "slug": "deep-work-orchestrator",
  "version": "1.0.0",
  "publishedAt": 1775077721061
}

skill-card.md

## Description:

Integrates personal productivity data with deep work principles to create tailored focus schedules, optimize energy-task matching, and reduce distractions.

This skill is ready for commercial/non-commercial use.

## Publisher:

[kingofzhao](https://clawhub.ai/user/kingofzhao)

### License/Terms of Use:

MIT-0

## Use Case:

Knowledge workers and developers use this skill to plan deep work blocks, analyze attention patterns, reduce shallow work, and match demanding tasks to their energy levels.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Broad deep-work or productivity prompts may activate the skill and invite sharing sensitive workplace or personal details.

Mitigation: Share only the information needed for the planning task and avoid confidential workplace, health, or personal data.

Risk: Productivity coaching could be mistaken for professional support for attention disorders or mental health needs.

Mitigation: Treat outputs as planning guidance and seek qualified professional support for ADHD or other clinical attention concerns.

## Reference(s):

- [ClawHub skill page](https://clawhub.ai/kingofzhao/skills/deep-work-orchestrator)

## Skill Output:

**Output Type(s):** [Text, Markdown, Guidance]

**Output Format:** [Markdown or structured plain text guidance]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Produces personalized deep work plans, attention tracking prompts, task-energy matching, and environment trigger recommendations.]

## Skill Version(s):

1.0.0 (source: server release metadata)

## Ethical Considerations:

Users 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.

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Machine-readable data

The same record, as JSON, for agents and crawlers.

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Record generated Oct 9, 2026.

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