Slack Memory Store

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

Core Capabilities

• Auto-classification - Automatically categorize incoming information into appropriate folders

• Multi-format support - Handle Slack messages, Confluence documents, emails, meeting notes, etc.

• Smart indexing - Maintain up-to-date index.md for rapid information retrieval

• Flexible schemas - Support structured metadata for each information type

• 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:

• Analyze the content and metadata

• Automatically classify into the appropriate directory

• Generate a clean filename

• Format with proper YAML frontmatter

• 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:

• Identify context: Channel, participants, date range

• Extract key info: Decisions, action items, important discussions

• 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)

• Format: Chronological order, preserve thread structure

• 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:

• Convert format: HTML → Markdown

• Preserve structure: Headers, lists, tables

• Add metadata: source_url, space, last_updated

• Classify: Usually → resources/ or projects/

Email Threads

When storing email conversations:

• Thread structure: Maintain reply chain

• Extract metadata: From, To, Subject, Date

• Classify by content:

• Announcements → announcements/

• Project updates → projects/

• Feedback → feedback/

Meeting Notes

When recording meetings:

• Structure: Date, attendees, agenda, discussions, action items

• Always goes to: meetings/

• Cross-reference: Link to related projects/decisions

• Action items: Extract and consider adding to tasks/

External News

When saving external articles:

• Always goes to: external/news/

• Add metadata: source, source_url, date, relevance

• Summarize: Focus on key points relevant to company

• 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:

• Detects duplicate

• Adds version suffix: filename_v2.md , filename_v3.md , etc.

Search Tips

• Content search: Case-insensitive by default

• Tag search: Find all files with specific tag

• Category search: List all files in a directory

• 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.