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在 OpenClaw 中安装
/install energy-peak-finder
功能描述
Turn several days of observation into a simple energy pattern review with likely peak windows, low windows, disruptors, task matching, and a one-week experim...
使用说明 (SKILL.md)
Energy Peak Finder
Overview
Use this skill to spot when the user's energy actually supports focus, creativity, stamina, and recovery. It helps the user observe repeated peaks and troughs, separate sustainable energy from urgency or caffeine spikes, and match task types to the most realistic time windows.
This skill is descriptive only. It does not use wearables, calendars, biometrics, or analytics backends.
Trigger
Use this skill when the user wants to:
- find their best window for deep work
- understand daily energy slumps
- distinguish true energy from urgency or caffeine buzz
- match task type to time of day
- run a one-week experiment before locking in a schedule
Example prompts
- "Help me figure out when my energy peaks"
- "I keep doing hard work at the wrong time of day"
- "Map my focus windows from these notes"
- "Turn my energy observations into a weekly experiment"
Workflow
- Review several days of energy, alertness, mood, hunger, and interruptions.
- Divide the day into simple time blocks.
- Identify repeated peaks, troughs, and false peaks.
- Note confounders like sleep, meals, workouts, caffeine, and interruptions.
- Match task types to each energy band.
- Suggest a one-week experiment to validate the pattern.
Inputs
The user can provide any mix of:
- notes about mornings, afternoons, and evenings
- focus, creativity, stamina, or crash periods
- sleep quality, meals, caffeine, or workout context
- interruptions from work or family
- task types that feel easier or harder at different times
- notes from several days or one rough week
Outputs
Return a markdown energy review with:
- observation summary
- task matching by energy band
- hypotheses about what boosts or drains energy
- one seven-day experiment
Safety
- Use repeated observations when possible, not one unusually good or bad day.
- Separate energy from mood, panic, or deadline urgency.
- Keep recommendations flexible when family, health, or work obligations limit ideal scheduling.
- Do not turn the pattern into a fixed identity label too quickly.
Acceptance Criteria
- Return markdown text.
- Identify a best window, lowest window, and common disruptors.
- Match peak, medium, and low-energy tasks to usable time bands.
- End with a one-week experiment.
安全使用建议
This skill appears low-risk and does what its description promises: it analyzes text notes to suggest energy windows and a one-week experiment. Before installing or using it, consider: (1) It uses simple keyword heuristics — results are heuristic, not clinical; validate suggestions yourself. (2) Verify where the skill runs in your environment (local vs remote agent) because any user text you submit could be logged or transmitted by the hosting agent/platform — the skill itself contains no network calls. (3) Don't submit sensitive health or identifying information in observations you don't want stored or transmitted. (4) If you have concerns, review handler.py and run the included tests locally to confirm behavior.
功能分析
Type: OpenClaw Skill
Name: energy-peak-finder
Version: 1.0.0
The energy-peak-finder skill is a purely analytical tool that uses keyword matching to identify energy patterns and suggest productivity improvements. The Python code (handler.py) and instructions (SKILL.md) contain no network activity, file system modifications, or data exfiltration logic, and the implementation is consistent with its stated purpose.
能力评估
Purpose & Capability
Name/description (finding energy peaks from observation notes) matches the code and SKILL.md. The code only parses text, scores time blocks, reports disruptors, and produces a one-week experiment. There are no unrelated env vars, binaries, or surprising capabilities.
Instruction Scope
SKILL.md instructs the agent to analyze user-provided observations; handler.py implements that workflow. The runtime only reads the bundled SKILL.md file and the provided input text; it does not access calendars, wearables, external APIs, or other system paths.
Install Mechanism
No install specification or external downloads. This is effectively an instruction-only skill with small local Python code and tests, so nothing is pulled from the network or written to the system outside the skill package.
Credentials
The skill declares no required environment variables, credentials, or config paths and the code does not read environment secrets. All operations are local string processing; requested privileges are minimal and proportionate.
Persistence & Privilege
always is false and the skill does not modify other skills or system-wide settings. It performs no persistent installs or daemonization.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install energy-peak-finder - 安装完成后，直接呼叫该 Skill 的名称或使用
/energy-peak-finder触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v1.0.0
Initial release of Energy Peak Finder skill.
- Guides users to review self-observed energy patterns across several days.
- Identifies likely peak and low energy windows, common disruptors, and matches task types to energy levels.
- Provides a markdown summary including observation overview, task recommendations by energy band, and hypothesized energy influencers.
- Suggests a customizable one-week experiment for users to validate their patterns.
- Does not require wearables or digital tracking—relies entirely on user-reported observations.
元数据
常见问题
Energy Peak Finder 是什么？
Turn several days of observation into a simple energy pattern review with likely peak windows, low windows, disruptors, task matching, and a one-week experim... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 86 次。
如何安装 Energy Peak Finder？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install energy-peak-finder」即可一键安装，无需额外配置。
Energy Peak Finder 是免费的吗？
是的，Energy Peak Finder 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
Energy Peak Finder 支持哪些平台？
Energy Peak Finder 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 Energy Peak Finder？
由 haidong（@harrylabsj）开发并维护，当前版本 v1.0.0。
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