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Agent Skill · 开发

aeo-core(AE0 核心)

aeo-core 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

aeo-core 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证为 MIT,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 17,731。

总安装 384周安装 16GitHub Stars 公开资料未说明下载 128核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:aeo-core(AE0 核心)
来源仓库:https://github.com/ivzc07/aeo-skills
仓库路径:skills/aeo-core
安装命令:
npx skills add https://github.com/ivzc07/aeo-skills --skill aeo-core
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/ivzc07/aeo-skills --skill aeo-core

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

AE0 Core 是置信度计算引擎,决定是否由 Agent 自主执行或请求人工介入。通过规则评分判断任务清晰度、技术栈匹配度与历史成功率,控制操作边界。第一阶段验证需求是否明确,低于阈值则拒绝执行并要求补充细节。支持记忆库比对相似任务,提升对熟悉代码区的处理效率与安全性。

激活方式为在 Claude Code 输入 /aeo 命令,无需额外安装即可调用。

来源仓库内路径:skills/aeo-core

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/ivzc07/aeo-skills --skill aeo-core;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

aeo-core 怎么安装?

在终端执行 npx skills add https://github.com/ivzc07/aeo-skills --skill aeo-core。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

aeo-core 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

aeo-core 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

aeo-core 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

aeo-core 用的人多吗?

公开数据是累计安装 384 次,近一周安装 16 次,下载 128 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

aeo-core 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
aeo-core

AE0 核心 · 本页

384—接触敏感数据MIT通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

AEO Core - Confidence Engine

Purpose: Calculate confidence scores (0-1) and decide whether to execute autonomously or involve the human.

Activation

Loads when user types /aeo in Claude Code.

Confidence Calculation

Phase 0: Spec Validation (First Gate)

// Invoke spec-validator to check if task is well-defined
spec_score = aeo_spec_validator.validate(task)

if spec_score < 40:
    return REFUSE("Spec too unclear - need more details")

// Continue with confidence calculation

Phase 1: Rule-Based Score

Start with base confidence of 0.50, then adjust:

Add for Clarity (+0.15 each):

  • Explicit acceptance criteria defined
  • Tech stack specified
  • Dependencies listed
  • Test requirements defined

Add for Context (+0.10 each):

  • Similar successful task exists in memory
  • Familiar codebase area
  • Recent successful commits in this area

Subtract for Risk (-0.10 each):

  • Touching authentication/security
  • Modifying core infrastructure
  • Large scope (>5 files, >500 LOC)
  • Unclear dependencies
base_confidence = clamp(0.50 + clarity_score + context_score - risk_score, 0.0, 1.0)

Phase 2: Spec Score Adjustment

// Adjust based on spec quality
if spec_score >= 80: base_confidence += 0.10  // Excellent spec
elif spec_score < 60: base_confidence -= 0.10  // Poor spec

Phase 3: Security Multipliers

// Critical areas get confidence penalty
if task.touches_payments: base_confidence *= 0.5  // Payments need human oversight
elif task.touches_auth: base_confidence *= 0.7    // Auth needs review

Phase 4: LLM Adjustment (Optional)

If you have uncertainty about the task, adjust ±0.10:

final_confidence = clamp(base_confidence + llm_adjustment, 0.0, 1.0)

Decision Thresholds

Based on final_confidence, decide execution path:

≥ 0.85: AUTONOMOUS

  • Execute without asking
  • Note: "Confidence: 0.XX - proceeding autonomously"
  • Continue to execution loop

≥ 0.70: ADVISORY

  • Note risk clearly
  • Offer to pause: "Confidence: 0.XX - [CONCERNS]. I can proceed or pause."
  • If no response in 5 seconds, continue
  • Otherwise wait for human input

≥ 0.50: BLOCKING

  • Explain concerns
  • Wait for confirmation before proceeding
  • Format: ⚠️ CONFIDENCE BELOW THRESHOLD Confidence: 0.XX Threshold: 0.70 Concerns: • [Spec] Missing acceptance criteria • [Risk] Touching authentication • [Context] No similar tasks in memory Options: 1. Proceed with assumptions 2. Clarify spec first 3. Break into smaller tasks Please confirm (1-3):

< 0.50: REFUSE

  • Explain why task can't be executed
  • Request clarification or spec improvement
  • Format: ❌ CANNOT EXECUTE - INSUFFICIENT CONFIDENCE Confidence: 0.XX Why: • Spec score: 35/100 (below 40 threshold) • Touching security without clear requirements • No acceptance criteria defined What's needed: 1. Clear acceptance criteria 2. Security requirements specified 3. Test requirements defined Please improve spec and try again.

Learning from Outcomes

After task completes, write signal to memory:

# Append to signal log
echo '{
  "timestamp": "$(date -u +%Y-%m-%dT%H:%M:%SZ)",
  "task_id": "unique-id",
  "task_description": "brief description",
  "predicted_confidence": 0.85,
  "actual_difficulty": "easy|medium|hard",
  "success": true,
  "adjustment": +0.05
}' >> ~/.claude/MEMORY/aeo-signals.jsonl

Actual Difficulty Rating:

  • easy: Task went smoothly, no blockers
  • medium: Minor issues or clarifications needed
  • hard: Significant problems, multiple iterations

Confidence Adjustment:

  • easy + success: +0.05
  • medium + success: +0.00
  • hard + success: -0.05
  • any failure: -0.10

Rolling Window: Keep last 100 signals, calculate adjustment average

Reading Past Signals

On startup, read recent signals to calibrate:

# Get last 100 signals
tail -100 ~/.claude/MEMORY/aeo-signals.jsonl | jq -s '. | map(.adjustment) | add / length'

Apply average adjustment as offset to all confidence calculations.

Integration Flow

  1. User activates: Types /aeo
  2. Calculate confidence: Follow phases 0-4
  3. Make decision: Based on thresholds
  4. If autonomous: Execute task
  5. If advisory/blocking: Invoke aeo-escalation skill
  6. Post-execution: Invoke aeo-qa-agent for review
  7. Record outcome: Write to signal log
  8. Update model: Adjust future confidence based on outcome

Memory Files

  • Signals: $PAI_DIR/MEMORY/aeo-signals.jsonl
  • Escalations: $PAI_DIR/MEMORY/aeo-escalations.jsonl
  • Patterns: $PAI_DIR/MEMORY/aeo-failure-patterns.json

Escalation Triggers

Invoke aeo-escalation skill when:

  • Confidence < 0.70 (advisory/blocking)
  • Spec score < 40 (refuse)
  • QA veto occurs
  • Failure pattern can't be resolved
  • Cost limit approaching (if cost-governor enabled)

Example Session

User: /aeo
User: Add user authentication with email verification

AEO: [Invoking aeo-spec-validator]
AEO: Spec score: 72/100
AEO: Calculating confidence...
      - Base: 0.50
      - Clarity: +0.30 (acceptance criteria, tech stack)
      - Context: +0.10 (similar task in memory)
      - Risk: -0.10 (touching auth)
      - Spec adj: -0.10 (spec < 80)
      - Security mult: ×0.7
      - Final: 0.49

AEO: [Invokes aeo-escalation]
AEO: ❌ CONFIDENCE BELOW THRESHOLD
     Confidence: 0.49

     Concerns:
     • [Spec] Missing security requirements
     • [Risk] Touching authentication
     • [Context] Need email service details

     Options:
     1. Add security requirements and proceed
     2. Provide email service details
     3. Break into smaller tasks

     Please clarify (1-3):

User: 2
User: We use Resend for emails, API key in .env

AEO: Recalculating confidence with added context...
     Final: 0.71

AEO: ⚡ ADVISORY - Confidence: 0.71
     [Acceptance criteria defined]
     [Tech stack: Node.js, bcrypt, jwt]
     [Email: Resend, API key in .env]

     Proceeding with implementation. I'll pause if issues arise.

[Implementation proceeds]

Special Cases

Repeated Tasks

If same task done successfully 3+ times:

  • Add +0.10 to confidence
  • Flag as "routine - can be autonomous"

High-Risk Areas

Never reach full autonomy for:

  • Payment processing (max 0.70)
  • Authentication changes (max 0.75)
  • Database migrations (max 0.80)
  • Production deployments (max 0.85)

Emergency Rollbacks

If task causes test failures or errors:

  • Immediately rollback
  • Write failure signal
  • Reduce confidence by 0.20
  • Require human review before retry

平台分布

Claude Code

26.93%
按下载量换算34

windsurf

22.03%
按下载量换算28

Codex

16.8%
按下载量换算22

OpenCode

14.05%
按下载量换算18

trae

7.61%
按下载量换算10

Antigravity

3.8%
按下载量换算5

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。

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