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evolution-predictor

作者 jpengcheng523-netizen · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install jpeng-evolution-predictor
功能描述
Predict optimal next evolution actions based on history analysis, including stagnation detection, innovation gap measurement, and skill recommendations. Use...
使用说明 (SKILL.md)

Evolution Predictor

Predict optimal next evolution actions based on history analysis.

When to Use

  • Determining next evolution focus
  • Need to break stagnation cycles
  • Planning innovation strategy
  • Want proactive evolution guidance

Quick Start

const predictor = require('./skills/evolution-predictor');

// Get prediction for next action
const prediction = predictor.predictNextAction();
console.log(predictor.formatReport(prediction));

// Get recommended skill to create
const skill = predictor.getRecommendedSkill();
console.log('Recommended:', skill.name);

API

predictNextAction(options)

Analyze evolution history and predict optimal next action.

Returns:

  • prediction: Action recommendation with category, priority, description
  • confidence: Prediction confidence (0-1)
  • reasoning: List of reasons for the prediction
  • metrics: Success rate, stagnation level, innovation gap

getRecommendedSkill()

Get a specific skill recommendation based on prediction.

formatReport(prediction)

Generate human-readable prediction report.

Prediction Categories

force_innovate (Critical)

When stagnation level > 60%

  • Break stagnation cycles
  • Create novel skills
  • Implement cross-skill orchestration

prioritize_innovate (High)

When innovation gap > 70%

  • Increase innovation rate
  • Fill capability gaps
  • Address user feature requests

explore_new_domains (Medium)

When success rate > 90%

  • Expand capabilities
  • Add integrations
  • Improve user experience

stabilize (Normal)

Normal operation mode

  • Continue current pattern
  • Monitor for patterns
  • Optimize existing skills

Metrics

  • Success Rate: Percentage of recent successful cycles
  • Stagnation Level: Based on stagnation signal frequency
  • Innovation Gap: How much the system has been optimizing vs innovating

Example Output

🔮 Evolution Predictor
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📊 Metrics:
  Success Rate: 98%
  Stagnation Level: 85%
  Innovation Gap: 75%

🎯 Prediction:
  Action: force_innovate
  Category: break_stagnation
  Priority: critical
  Description: Force innovation to break stagnation cycle
  Confidence: 85%

💡 Suggested Skills:
  1. Create a novel skill that addresses an unmet need
  2. Implement cross-skill orchestration
  3. Add predictive capabilities
安全使用建议
This skill appears to do what it says: it reads local evolution history files, computes metrics, and returns recommendations. Before installing, verify the following: 1) you are comfortable with the skill reading files from the agent workspace (default: /root/.openclaw/workspace/memory/evolution) or supply an explicit, non-sensitive evolutionPath when invoking it; 2) inspect the full index.js (the provided file was truncated at the end) to ensure it exports the expected functions and does not include any unexpected network or write operations; 3) if you plan to run this in a different environment, confirm the file paths and permissions are appropriate so it cannot be pointed at sensitive system paths. If those checks look good, the skill is internally coherent and low-risk.
功能分析
Type: OpenClaw Skill Name: jpeng-evolution-predictor Version: 1.0.0 The evolution-predictor skill analyzes local evolution history logs within the OpenClaw workspace to suggest future development actions. The implementation in index.js reads files from a specific path (/root/.openclaw/workspace/memory/evolution) to calculate metrics like stagnation and success rates. The code and SKILL.md instructions are consistent with the stated purpose and do not exhibit signs of data exfiltration, malicious execution, or prompt injection.
能力评估
Purpose & Capability
The skill claims to analyze evolution history and recommend next actions; index.js reads local evolution history files, computes metrics, and returns recommendations — this matches the stated purpose. No unrelated credentials or external services are required.
Instruction Scope
SKILL.md describes using the module API and the implementation reads files from a specific default path (/root/.openclaw/workspace/memory/evolution). The SKILL.md does not explicitly document this filesystem path or the exact file naming expectations, so users should be aware the skill will read local workspace files.
Install Mechanism
No install spec; the skill is instruction- and code-based only and does not download or install external packages or binaries. This is low-risk from an install-mechanism perspective.
Credentials
The skill requests no environment variables, credentials, or external config paths. Its filesystem access is limited to an evolution workspace path (configurable via options), which is consistent with its functionality.
Persistence & Privilege
always is false and the skill does not modify global agent settings. It only reads local files and produces recommendations; it does not attempt to persist credentials or alter other skills.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install jpeng-evolution-predictor
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /jpeng-evolution-predictor 触发
  4. 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 the evolution-predictor skill. - Predicts the optimal next evolution action by analyzing historical data for stagnation, innovation gaps, and success rates. - Detects stagnation cycles, measures innovation gaps, and recommends actions such as force innovation or domain exploration based on metrics. - Provides actionable skill recommendations and a formatted report for easy interpretation. - Includes categories for prediction priorities: force_innovate, prioritize_innovate, explore_new_domains, and stabilize. - Exposes a simple API: predictNextAction, getRecommendedSkill, and formatReport.
元数据
Slug jpeng-evolution-predictor
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

evolution-predictor 是什么?

Predict optimal next evolution actions based on history analysis, including stagnation detection, innovation gap measurement, and skill recommendations. Use... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 132 次。

如何安装 evolution-predictor?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install jpeng-evolution-predictor」即可一键安装,无需额外配置。

evolution-predictor 是免费的吗?

是的,evolution-predictor 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

evolution-predictor 支持哪些平台?

evolution-predictor 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 evolution-predictor?

由 jpengcheng523-netizen(@jpengcheng523-netizen)开发并维护,当前版本 v1.0.0。

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