JinFanZheng

memory

Persistent memory for cross-session personalization. Trigger when user shares identity, preferences, relationships, or facts worth remembering.

JinFanZheng 79 29 Updated 7mo ago
GitHub

Install

npx skillscat add jinfanzheng/kode-sdk-csharp/memory

Install via the SkillsCat registry.

About this skill

This skill provides persistent memory storage for AI agents to retain user information across conversation sessions. It solves the problem of losing context between interactions by saving identity details, preferences, relationships, and project facts to structured JSONL files. Developers should use it when users share information with recurring value, triggering immediate writes without acknowledgment.

SKILL.md

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:

  1. Immediately call fs_write - don't acknowledge first, don't batch
  2. Extract structured fields from casual speech
  3. 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/"