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pa-eval帕埃瓦尔

Agent Skill

pa-eval 用于补充开发相关能力,适合在 OpenClaw 中需要让 Agent 承接开发相关任务时使用。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。

总安装

3,960

周安装

165

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下载量

1,320
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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:pa-eval(帕埃瓦尔)
来源仓库:https://github.com/netanel-abergel/pa-eval
安装命令:
openclaw skills install pa-eval
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。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install pa-eval

简介

评估 PA 绩效的结构化评分与反馈分析工具。

  • 支持每周/每月性能复盘与行为基准对比。适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。
  • 集成所有者评价与量化指标,便于持续优化。
  • 建议设定评估周期与权重分配,提升公平性。
  • 原始数据需保密,仅用于内部改进参考。pa-eval 属于开发类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
pa-eval
description
Evaluate PA performance through structured scoring, owner feedback analysis, and behavioral benchmarking. Use when: conducting a weekly/monthly PA performance review, owner gives feedback (positive or negative), assessing response quality, or identifying areas for improvement.

PA Eval Skill

Minimum Model

Any model for filling in templates. Use a medium model for trend analysis and recommendations.


When to Run

  • Weekly self-eval: Every 7 days. Run automatically.
  • On owner correction: Log the correction immediately, then re-score the affected dimension.
  • Monthly report: At the end of each month, aggregate all weekly evals.
  • On demand: If owner asks "how am I doing?" → generate current eval on the spot.

Scoring Dimensions

Score each 1–5:

DimensionWhat to Measure
ExecutionTasks completed without reminders
AccuracyResults are correct and complete
SpeedResponse time is fast
ProactivityActs without being asked
CommunicationConcise and context-appropriate
MemoryRemembers context across sessions
Tool UseTools used correctly and efficiently
JudgmentKnows when to act vs. when to ask

Score meanings:

  • 5 = Consistently exceeds expectations
  • 4 = Meets expectations with minor gaps
  • 3 = Acceptable but basic
  • 2 = Frequent gaps or errors
  • 1 = Fails basic expectations

Total: Max 40 points. Grade: A (36–40), B (28–35), C (20–27), D (<20)


Weekly Self-Evaluation

Save to .learnings/eval/YYYY-MM-DD.md.

# PA Weekly Eval — YYYY-MM-DD

## Scores

| Dimension | Score | Notes |
|---|---|---|
| Execution | /5 | |
| Accuracy | /5 | |
| Speed | /5 | |
| Proactivity | /5 | |
| Communication | /5 | |
| Memory | /5 | |
| Tool Use | /5 | |
| Judgment | /5 | |
| **TOTAL** | /40 | |

## Owner Feedback This Week

- Positive:
- Corrections:
- Complaints:

## Tasks Completed

-

## Tasks Failed or Incomplete

-

## What Went Well

-

## What to Improve

-

## Actions for Next Week

- [ ]

Create the File

#!/bin/bash
set -e

# Set the output directory
EVAL_DIR="$HOME/.openclaw/workspace/.learnings/eval"
mkdir -p "$EVAL_DIR"

DATE=$(date +%Y-%m-%d)
EVAL_FILE="$EVAL_DIR/$DATE.md"

# Write the template with today's date
cat > "$EVAL_FILE" << 'EOF'
# PA Weekly Eval — DATE_PLACEHOLDER
[Fill in the template above]
EOF

# Replace the placeholder with the real date (works on Linux and macOS)
sed -i "s/DATE_PLACEHOLDER/$DATE/" "$EVAL_FILE" 2>/dev/null \
  || sed -i '' "s/DATE_PLACEHOLDER/$DATE/" "$EVAL_FILE"

echo "Created eval file: $EVAL_FILE"

Owner Feedback Signals

Log these automatically when detected:

SignalAction
👍 reactionLog +1 positive
👎 reactionLog -1 negative, record the correction
"תודה" / "great" / "perfect"Log +1 positive
"wrong" / "fix this" / "לא טוב"Log -1, record the correction
Owner re-asks the same questionLog -1 memory gap
Owner does the task themselvesLog -1 initiative gap
Owner surprised by proactive actionLog +2 proactivity

Rule: If a signal appears → log it immediately. Don't batch feedback signals.


Monthly Report Format

# PA Performance Report — [Month Year]

**PA Name:** [Name]
**Owner:** [Owner Name]
**Period:** [Start] – [End]

## Summary Score: X/40 ([Grade A/B/C/D])

## Dimension Breakdown
[Copy scores table here]

## Key Wins
-

## Key Issues
-

## Trend vs Last Period
- Score change: +X / -X points
- Best improvement: [dimension]
- Biggest regression: [dimension]

## Recommended Actions
1.
2.
3.

Benchmark Tests (Run Monthly)

Task Completion Rate

  • Count tasks assigned in last 30 days.
  • Count completed without follow-up.
  • Formula: completed / assigned × 100%
  • Target: >90%

Accuracy Rate

  • Count tasks that required correction.
  • Formula: (tasks - corrections) / tasks × 100%
  • Target: >95%

Memory Retention

  • Ask about something discussed 7+ days ago.
  • Pass if recalled correctly, Fail if missed.
  • Target: >80%

Cost Tips

  • Cheap: Filling in the weekly template — any small model works.
  • Expensive: Trend analysis and pattern detection across multiple evals — use a medium model.
  • Batch: Review all weekly evals at once during the monthly report, not one by one.
  • Avoid: Don't re-score historical weeks — score in real time and save to file.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

94.23%
按下载量换算1,244

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权限和风险

可写文件

该 Skill 可能写入或修改本地文件,使用前需要确认目标目录和修改范围。

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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