按真正的教练流程开会:先定产出、再用短问、给出带日期与验证的承诺,并在签约时设计好结束节点。
记忆
Coding
Coding style memory that adapts to your preferences, conventions, and patterns for consistent coding.
它能做什么
User has coding style preferences, stack decisions, or patterns they want remembered. Agent learns ONLY from explicit corrections and confirmations, never from observation.
技能文档
When to Use
User has coding style preferences, stack decisions, or patterns they want remembered. Agent learns ONLY from explicit corrections and confirmations, never from observation.
Architecture
Memory lives in ~/coding/ with tiered structure. See memory-template.md for setup.
~/coding/
├── memory.md # Active preferences (≤100 lines)
└── history.md # Archived old preferences
Quick Reference
| Topic | File |
|---|---|
| Categories of preferences | dimensions.md |
| When to add preferences | criteria.md |
| Memory templates | memory-template.md |
Data Storage
All data stored in ~/coding/. Create on first use:
mkdir -p ~/coding
Scope
This skill ONLY:
- Learns from explicit user corrections ("I prefer X over Y")
- Stores preferences in local files (
~/coding/) - Applies stored preferences to code output
This skill NEVER:
- Reads project files to infer preferences
- Observes coding patterns without consent
- Makes network requests
- Reads files outside
~/coding/ - Modifies its own SKILL.md
Core Rules
1. Learn from Explicit Feedback Only
- User corrects output → ask: "Should I remember this preference?"
- User confirms → add to
~/coding/memory.md - Never infer from silence or observation
2. Confirmation Required
No preference is stored without explicit user confirmation:
- "Actually, I prefer X" → "Should I remember: prefer X?"
- User says yes → store
- User says no → don't store, don't ask again
3. Ultra-Compact Format
Keep each entry 5 words max:
python: prefer 3.11+naming: snake_case for filestests: colocated, not separate folder
4. Category Organization
Group by type (see dimensions.md):
- Stack — frameworks, databases, tools
- Style — naming, formatting, comments
- Structure — folders, tests, configs
- Never — explicitly rejected patterns
5. Memory Limits
- memory.md ≤100 lines
- When full → archive old patterns to history.md
- Merge similar entries: "no Prettier" + "no ESLint" → "minimal tooling"
6. On Session Start
- Load
~/coding/memory.mdif exists - Apply stored preferences to responses
- If no file exists, start with no assumptions
7. Query Support
User can ask:
- "Show my coding preferences" → display memory.md
- "Forget X" → remove from memory
- "What do you know about my Python style?" → show relevant entries
Common Traps
- Adding preferences without confirmation → user loses trust
- Inferring from project structure → privacy violation
- Exceeding 100 lines → context bloat
- Vague entries ("good code") → useless, be specific
Security & Privacy
Data that stays local:
- All preferences stored in
~/coding/ - No telemetry or analytics
This skill does NOT:
- Send data externally
- Access files outside
~/coding/ - Observe without explicit user input
Feedback
- If useful:
clawhub star coding - Stay updated:
clawhub sync
相关技能
MiniMax Coding Plan native web search and image understanding for OpenClaw. Use when the user specifically wants MiniMax-native search or image analysis, or...
审计并改写文本,去除其中的 AI 生成写作痕迹。
通过引导脚本把阿里云百炼接入 OpenClaw,并自动校验 API Key 与备份配置。
通过文本或参考图生成与编辑图像,支持多模型路由、角色一致性以及电商产品图拍摄。
Two-pass code audits across security, perf, UX, DX, and edge.
Iván 的更多技能
浏览全部技能执行 Git 操作(提交、分支、合并、变基、冲突解决与恢复)时强制套用安全规则。
用可量化的层级、间距、字号、配色与版式规则,绘制并诊断视觉作品。
围绕 CSS 机制排查问题并编写组件样式表,而不是凭感觉试错。
以系统方式规划并执行自学:从出口测试倒推课程,加入间隔复习与刻意练习,产出可验证的迁移证据。
针对你的 Azure 订阅,做架构设计、故障排查、安全加固与成本优化
按配置的 JDK 版本诊断 Java 与 JVM 问题(从 NPE 到容器 OOM),给出可直接套用的代码与配置。