Slack Memory Store

Slack Memory Store

This skill enables systematic memory management for AI employees operating in IT company environments, primarily through Slack communication.

Core Capabilities

1. Auto-classification - Automatically categorize incoming information into appropriate folders
2. Multi-format support - Handle Slack messages, Confluence documents, emails, meeting notes, etc.
3. Smart indexing - Maintain up-to-date index.md for rapid information retrieval
4. Flexible schemas - Support structured metadata for each information type
5. CRUD operations - Create, read, update, and delete memory entries

Quick Start

Initialize Memory Structure

Before using the memory system for the first time, initialize the directory structure:

python scripts/init_memory.py /path/to/memory

This creates:

• All required directories (channels/, users/, projects/, etc.)
• Initial index.md with navigation
• Metadata tracking file

Add New Information

The primary way to add information to memory:

python scripts/add_memory.py /path/to/memory "Title" "Content" '{"type":"channel", "channel_id":"C123"}'

The script will:

1. Analyze the content and metadata
2. Automatically classify into the appropriate directory
3. Generate a clean filename
4. Format with proper YAML frontmatter
5. Save to the correct location

Update Index

After adding/modifying multiple entries, update the index:

python scripts/update_index.py /path/to/memory

This refreshes:

• Statistics (total channels, users, projects, etc.)
• Recent updates list (10 most recent changes)
• Navigation links

Search Memory

To find information quickly:

# Search by content
python scripts/search_memory.py /path/to/memory content "프로젝트"

# Search by tag
python scripts/search_memory.py /path/to/memory tag urgent

# List files in category
python scripts/search_memory.py /path/to/memory category projects

Memory Organization

Directory Structure

memory/
├── index.md              # Main navigation and quick reference
├── channels/             # Slack channel information
│   └── C123_마케팅팀.md
├── users/                # Team member profiles
│   └── U456_김철수.md
├── projects/             # Project status and history
│   ├── 신제품런칭.md
│   └── archive/
├── tasks/                # Completed and ongoing tasks
│   ├── ongoing/
│   └── completed/
├── decisions/            # Decision points and rationale
├── meetings/             # Meeting notes and action items
├── feedback/             # User feedback and suggestions
├── announcements/        # Important announcements
├── resources/            # Internal docs, guides, manuals
├── external/             # External information
│   └── news/
└── misc/                 # Uncategorized information

File Format

Each memory file follows this structure:

---
type: channel
channel_id: C01234567
channel_name: "마케팅팀"
participants: [U01234567, U76543210]
tags: [marketing, important]
created: 2025-10-28 10:00:00
updated: 2025-10-28 15:30:00
---

# 마케팅팀 채널

## 커뮤니케이션 지침

- Tone: Professional but friendly
- Response time: Within 1 hour during business hours
- Key topics: Campaign planning, performance metrics

## Recent Discussions

...

Storage Strategy: Hybrid Approach

CRITICAL: Use a hybrid strategy to optimize retrieval and file size:

1. Profile Files (One Per Entity - UPDATE, Don't Create New)

• Purpose: Persistent guidelines, preferences, static info
• Action: ALWAYS check if file exists first, then UPDATE it
• Examples:
  • channels/C123_마케팅팀.md - Channel guidelines, members, communication style
  • users/U456_김철수.md - User profile, preferences, work style

2. Topic Files (Multiple - CREATE New or UPDATE Existing)

• Purpose: Conversations, projects, decisions, meetings
• Action: Create new file per topic, or update if same topic continues
• Examples:
  • projects/신제품런칭.md - Project discussions
  • decisions/AWS전환_20251117.md - Important decisions (date-stamped)
  • meetings/2025-11-17-Q4전략회의.md - Meeting notes
  • misc/마케팅팀_일상_20251117.md - Casual conversations

3. Decision Tree for Classification

Content type:
├─ Channel/User guidelines or preferences?
│  └─ YES → UPDATE channels/C123_채널명.md or users/U456_유저명.md
│
└─ NO → What's the main topic?
    ├─ Project discussion → projects/프로젝트명.md
    ├─ Important decision → decisions/주제_DATE.md
    ├─ Meeting notes → meetings/DATE-주제.md
    ├─ Casual conversation → misc/채널명_DATE.md (or skip if trivial)
    └─ Task/feedback/announcement → respective directories

Handling Different Content Types

Slack Conversations

When receiving Slack message threads:

1. Identify context: Channel, participants, date range
2. Extract key info: Decisions, action items, important discussions
3. Classify using Hybrid Strategy (see Decision Tree above):
   • Channel guidelines/preferences → UPDATE channels/C123_채널명.md
   • User preferences → UPDATE users/U456_유저명.md
   • Project-focused → CREATE/UPDATE projects/프로젝트명.md
   • Decision-focused → CREATE decisions/주제_DATE.md
   • Meeting notes → CREATE meetings/DATE-주제.md
   • Casual chat → CREATE misc/채널명_DATE.md (or skip if not important)
4. Format: Chronological order, preserve thread structure
5. Metadata: channel_id, participants, date_range, message_count, related_to (link to profile file)

Example usage:

from scripts.add_memory import MemoryManager

manager = MemoryManager('/path/to/memory')

# Example 1: Topic file (project discussion)
manager.add_memory(
    title="Q4 전략 논의",
    content=formatted_slack_thread,
    metadata={
        'type': 'project',  # Will create projects/Q4전략논의.md
        'channel_id': 'C123',
        'channel_name': '마케팅팀',
        'participants': ['U01', 'U02'],
        'date_range': '2025-10-28',
        'message_count': 25,
        'tags': ['strategy', 'q4'],
        'related_to': ['channels/C123_마케팅팀.md']  # Link to channel profile
    }
)

# Example 2: Profile file (channel guidelines update)
manager.add_memory(
    title="마케팅팀",
    content="Channel guidelines: Professional tone, quick response expected",
    metadata={
        'type': 'channel',  # Will update channels/C123_마케팅팀.md
        'channel_id': 'C123',
        'channel_name': '마케팅팀',
        'guidelines': {'tone': 'professional', 'response_time': '1시간 이내'}
    }
)

Confluence Documents

When importing Confluence documentation:

1. Convert format: HTML → Markdown
2. Preserve structure: Headers, lists, tables
3. Add metadata: source_url, space, last_updated
4. Classify: Usually → resources/ or projects/

Email Threads

When storing email conversations:

1. Thread structure: Maintain reply chain
2. Extract metadata: From, To, Subject, Date
3. Classify by content:
   • Announcements → announcements/
   • Project updates → projects/
   • Feedback → feedback/

Meeting Notes

When recording meetings:

1. Structure: Date, attendees, agenda, discussions, action items
2. Always goes to: meetings/
3. Cross-reference: Link to related projects/decisions
4. Action items: Extract and consider adding to tasks/

External News

When saving external articles:

1. Always goes to: external/news/
2. Add metadata: source, source_url, date, relevance
3. Summarize: Focus on key points relevant to company
4. Link: Connect to related_project if applicable

Automatic Classification

The system uses a multi-level classification strategy:

Level 1: Explicit Metadata

If type field exists in metadata → use directly

Level 2: Structural Indicators

• channel_id present → channels/
• user_id present → users/
• project_id present → projects/

Level 3: Keyword Analysis

Scan content for keywords (see references/classification-guide.md for full list):

• "프로젝트", "project", "milestone" → projects/
• "결정", "decision", "승인" → decisions/
• "회의", "meeting" → meetings/
• etc.

Level 4: Default

If no classification match → misc/

Advanced Features

Update Existing Memory

To update an existing file:

manager = MemoryManager('/path/to/memory')
manager.update_memory(
    directory='projects',
    filename='신제품런칭.md',
    new_content=updated_content,
    new_metadata={'updated': '2025-10-28 16:00:00', 'status': 'completed'}
)

Cross-referencing

Use related_to metadata to link related files:

---
type: decision
related_to:
  - projects/신제품런칭.md
  - meetings/2025-10-28-전략회의.md
---

Version Management

If a file with the same name exists, the system automatically:

1. Detects duplicate
2. Adds version suffix: filename_v2.md, filename_v3.md, etc.

Search Tips

1. Content search: Case-insensitive by default
2. Tag search: Find all files with specific tag
3. Category search: List all files in a directory
4. Index search: Use browser Ctrl+F on index.md for quick keyword lookup

Best Practices

1. Consistent Metadata

Always include at minimum:

• type: Content type
• created: Creation timestamp
• tags: Relevant tags for searchability

2. Descriptive Titles

Use clear, descriptive titles:

• ✅ "Q4 마케팅 전략 회의 - 2025-10-28"
• ❌ "미팅"

3. Regular Index Updates

Update index after:

• Multiple file additions
• File deletions
• Category changes
• Or at least once per hour

4. Use Tags Liberally

Tags improve discoverability:

tags: [urgent, marketing, q4, strategy, approval-needed]

5. Link Related Information

When information is related, add cross-references:

related_to:
  - projects/웹사이트리뉴얼.md
  - decisions/디자인시스템선택.md

Reference Documents

For detailed information, see:

• data-schemas.md - Complete schemas for all memory types with examples
• classification-guide.md - Detailed classification rules and content handling strategies

Workflow Examples

Example 1: Storing Slack Discussion

# 1. Format the Slack thread
slack_content = """
## Participants
- @chulsoo (PM)
- @sarah (Designer)

## Discussion
[10:30] chulsoo: 랜딩 페이지 디자인 리뷰 부탁드립니다
[10:35] sarah: 확인했습니다. 전반적으로 좋은데 CTA 버튼이 더 눈에 띄었으면 좋겠어요
...
"""

# 2. Add to memory
manager.add_memory(
    title="랜딩 페이지 디자인 리뷰",
    content=slack_content,
    metadata={
        'type': 'project',
        'channel_id': 'C123',
        'project': '신제품런칭',
        'participants': ['U01', 'U02'],
        'tags': ['design', 'review', 'landing-page']
    }
)

# 3. Update index
update_index()

Example 2: Quick Information Lookup

# Find all files related to "신제품"
python scripts/search_memory.py /memory content "신제품"

# Results show:
# 1. projects/신제품런칭.md
# 2. meetings/2025-10-15-신제품기획회의.md
# 3. decisions/신제품가격결정.md

Example 3: Tracking Project Progress

# Initial project setup
manager.add_memory(
    title="신제품 런칭 프로젝트",
    content="""
## Overview
AI 기반 추천 시스템 개발 및 런칭

## Milestones
- [ ] MVP 개발 (2025-11-30)
- [ ] 베타 테스트 (2025-12-15)
- [ ] 정식 출시 (2025-12-31)
    """,
    metadata={
        'type': 'project',
        'status': 'in_progress',
        'priority': 'high',
        'participants': ['U01', 'U02', 'U03']
    }
)

# Later: Update progress
manager.update_memory(
    'projects',
    '신제품런칭프로젝트.md',
    updated_content_with_progress,
    {'updated': '2025-10-28', 'status': 'on_track'}
)

Troubleshooting

Issue: Files not found by search

Solution: Ensure filename ends with .md and is not index.md

Issue: Wrong classification

Solution: Provide explicit type in metadata or add more specific keywords to content

Issue: Index out of date

Solution: Run update_index.py manually

Issue: Duplicate files

Solution: System automatically handles by adding version suffix (_v2, _v3, etc.)

Performance Considerations

• Index updates: O(n) where n = total files. Run after batch operations, not after each file
• Search: O(n) linear scan. For large datasets (>1000 files), consider adding full-text search
• File size: Keep individual files under 100KB for optimal performance

Integration Notes

This skill is designed to work seamlessly with:

• Slack API integrations for automatic message capture
• Confluence API for document import
• Gmail API for email archiving
• Calendar APIs for meeting notes
• Any custom data sources via the flexible add_memory interface

The memory structure is AI-agent-friendly: index.md provides rapid overview, and all content is in Markdown for easy parsing and understanding.