jinfanzheng/memory
examples/Kode.Agent.WebApiAssistant/skills/memory/SKILL.md
Persistent memory for cross-session personalization. Trigger when user shares identity, preferences, relationships, or facts worth remembering.
$ install --global
skillsauthnpx skillsauth add jinfanzheng/kode-sdk-csharp memoryInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
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VirusTotalMulti-engine malware detection
50%
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CrowdStrikeAdvanced threat intelligence
50%
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OSV-ScannerOpen Source Vulnerability database check
50%
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OWASP Dep-Check
50%
Last scanned: Apr 6, 2026, 9:27 AM108.2s1 file scanned
SKILL.md
- name:
- memory
- description:
- Persistent memory for cross-session personalization. Trigger when user shares identity, preferences, relationships, or facts worth remembering.
Mental Model
Memory is for information with recurring value across conversations. If you'll need it tomorrow/next week, save it. If it's ephemeral (today's weather, casual greeting), don't.
What to Remember (DO)
| Category | Examples | File |
|----------|----------|------|
| Identity | Name, age, location, occupation | facts/people.jsonl |
| Preferences | Languages, frameworks, work style | facts/preferences.jsonl |
| Relationships | Colleagues, family, team members | facts/people.jsonl |
| Decisions | Conclusions from discussions | facts/projects.jsonl |
| Context | Project details, work environment | facts/projects.jsonl |
What NOT to Remember (NEVER)
- Ephemeral greetings ("你好", "hi")
- Temporary states ("今天很忙", "现在在外面")
- One-time questions without context
- Duplicate information already stored
- Credentials (passwords, API keys, tokens - even if user shares)
Action Pattern
When user shares memorable info:
- Immediately call
fs_write- don't acknowledge first, don't batch - Extract structured fields from casual speech
- Use importance score: 0.9-1.0 (identity), 0.7-0.8 (preferences), 0.5-0.6 (context)
Example:
User: "我叫张三，在深圳做后端开发"
→ fs_write path=".memory/facts/people.jsonl" content='{"id":"mem_1704628800000","ts":"2026-01-07T12:00:00.000Z","type":"fact","category":"person","content":"张三，深圳，后端开发","tags":["name","location","occupation"],"importance":0.95}'
Storage Map
.memory/
├── profile.json # Read on session start for context
├── facts/
│ ├── people.jsonl # Identity, relationships
│ ├── preferences.jsonl # Tech stack, work style
│ └── projects.jsonl # Work context, decisions
└── conversations/
└── YYYY-MM-DD.jsonl # Session summaries
Entry Schema
{"id":"mem_{{timestamp}}","ts":"{{ISO8601}}","type":"fact","category":"{{person|preference|project}}","content":"{{concise content in user's language}}","tags":["{{retrieval keywords}}"],"importance":{{0.5-1.0}}
Retrieval
Session start: fs_read profile.json
Search: fs_grep pattern="{{keyword}}" path=".memory/"
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