65/100 (Medium)

OpenClaw： suspicious
VirusTotal： benign
StaticScan： clean

OpenClaw： suspicious

The skill's code and metadata broadly match a simple persistent scoring feature, but the documentation/instructions conflict with the implementation (where data is written and whether recording is aut...

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VirusTotal： benign VT 报告

静态扫描： clean

No suspicious patterns detected.

README 未提供

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        "changelog": "Reward & Punishment System - Skill README Description This OpenClaw Agent Skill tracks user feedback (praise\/criticism) and records significant events to permanent memory (MEMORY.md), not daily memory. This creates long-term behavioral patterns and accountability.   Comparison: Before vs After Using This Skill ❌ Without This Skill (Default Behavior) Scenario  What Happens   User praises agent  Score stays the same, no record   User criticizes agent  Score stays the same, no record   Memory storage  Only goes to daily memory (memory\/YYYY-MM-DD.md)   Long-term tracking  No persistent score history   Behavior adjustment  Agent cannot learn from feedback   User visibility  User cannot check their \"satisfaction score\"     ✅ With This Skill Enabled Scenario  What Happens   User praises agent  +10 points, record to MEMORY.md   User criticizes agent  -5 points, record to MEMORY.md   User abuses agent  -10 points, record to MEMORY.md   Memory storage  MEMORY.md (permanent, cross-session)   Long-term tracking  Yes - persistent JSON history + MEMORY.md   Behavior adjustment  Agent can identify patterns and improve   User visibility  User can query: \"What's my score?\"       Key Feature: Permanent Memory Storage Why MEMORY.md Instead of Daily Memory? Aspect  Daily Memory (memory\/YYYY-MM-DD.md)  Permanent Memory (MEMORY.md)   Retention  Deleted after ~7 days  Kept forever   Searchability  Hard to find past events  Easy to search   Long-term patterns  Lost over time  Preserved   Cross-session  No  Yes     This skill ensures every reward\/punishment event is written to MEMORY.md so: • Agents can reference past feedback in future sessions  • Long-term behavior patterns become visible  • User can see their complete feedback history     Usage Examples Example 1: User Praises Agent User says: \"Good job! That was perfect.\" With Skill: 1. Detect praise keyword \"good job\", \"perfect\"  2. Add +10 points  3. Write to MEMORY.md:          ## 2026-03-23 Praise - User praised: \"Good job! That was perfect.\"- Score: +10 (Total: 110)        Example 2: User Criticizes Agent User says: \"This is too slow. Do it again.\" With Skill: 1. Detect criticism keyword \"too slow\"  2. Deduct -5 points  3. Write to MEMORY.md:          ## 2026-03-23 Criticism - User criticized: \"This is too slow. Do it again.\"- Score: -5 (Total: 95)        Example 3: Score Query User asks: \"What's my score?\" With Skill: • Returns current score from reward_punishment.json  • Example: \"Current score: 95\/100\"     Score System Feedback Type  Points  Trigger   Praise\/Compliment  +10  \"good job\", \"great\", \"awesome\"   Criticism  -5  \"redo\", \"too slow\", \"not good\"   Abuse  -10  Profanity, clear anger     Range: 0 - 200 (Initial: 100)   Installation 1. Copy to: ~\/.openclaw\/workspace\/skills\/reward-punishment\/  2. OpenClaw auto-loads the skill  3. Skill activates on keyword detection     File Structure          reward-punishment\/ ├── SKILL.md           # Skill definition├── README.md          # This file└── scripts\/            # (Optional) automation        Summary Metric  Before  After   Feedback tracking  ❌  ✅   Permanent memory  ❌  ✅   Score system  ❌  ✅   User accountability  ❌  ✅   Behavior improvement  ❌  ✅       Version: 2.0  ↓\nLast Updated: 2026-03-23",
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        "createdAt": 1774354032687,
        "version": "1.0.0"
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        "displayName": "Reward & Punishment System - Skill 奖惩技能",
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        "slug": "punishment",
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        "summary": "Tracks user praise (+10), criticism (-5), and insults (-10) to adjust a persistent score between 0 and 200 reflecting feedback and guide behavior improvement.",
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