Daily Workflow Automation Skill

Overview

Purpose: Automatically processes daily notes when users mention diary extraction, end-of-day workflows, or ask "what did I work on today". Extracts entries from ### 🧠 Notes sections, classifies them using AI, routes them to appropriate context/diary/insights/ideas files, and updates memory systems (Serena + Graphiti).

Type: Claude Code Skill (Auto-triggered)

Status: Active (Phase 1 Implementation Complete)

Replaces: /extract-daily-content command (manual slash command)

Location

Primary Files

.claude/skills/daily-workflow/SKILL.md - Main skill workflow (loads when triggered)
.claude/skills/daily-workflow/reference/templates.md - Daily note template documentation
.claude/skills/daily-workflow/reference/formatting-rules.md - Entry transformation rules
.claude/skills/daily-workflow/scripts/extract_content.py - Deterministic extraction script

Configuration

.claude/skills/README.md - Skill registry and testing guide
98-templates/daily-note.md - Daily note template with ### 🧠 Notes section
.claude/commands/.extract-daily-content-tracker.json - Extraction tracker (auto-created)

Related Files

.claude/commands/extract-daily-content.md - Original command (now deprecated in favor of skill)
00 - Daily/ - Daily notes directory (extraction source)
04-resources/diary.md - Primary diary destination
04-resources/context.md - Primary context destination
01-areas/p-dev/insights.md - Primary insights destination
04-resources/ideas.md - Primary ideas destination

Architecture

Components

SKILL.md (.claude/skills/daily-workflow/SKILL.md:1)
- Purpose: Main skill file with frontmatter trigger description
- Auto-loaded by Claude Code when conversation matches triggers
- Contains: Quick workflow, classification rules, context areas, critical rules
- Token-efficient: ~350 lines (under 500 line best practice)
Extraction Script (.claude/skills/daily-workflow/scripts/extract_content.py:1)
- Purpose: Deterministic file I/O, pattern matching, data structure operations
- Key functions:
  - find_daily_note(date_str) - Locate daily note files by date
  - extract_notes_section(filepath) - Extract ### 🧠 Notes content
  - split_entries(notes_content) - Split into individual entries
  - check_duplicate(entry, existing_entries) - Fuzzy matching (80% threshold)
  - format_cross_link(daily_note_path) - Generate wikilinks
- Handles: Tracker management, file creation with frontmatter, atomic appends
Reference Documentation (.claude/skills/daily-workflow/reference/)
- templates.md - Daily note structure, Dataview patterns, entry recognition
- formatting-rules.md - Transformation rules, cross-linking format, deduplication

Data Flow

Daily Note (00 - Daily/)
  ↓
### 🧠 Notes Section Extraction (Python script)
  ↓
Entry Splitting (by blank lines)
  ↓
AI Classification (Claude/MCP)
  ├─ Diary Entry (preserve narrative)
  ├─ Insight (preserve reflective tone)
  ├─ Context (transform to factual)
  └─ Idea (preserve speculative tone)
  ↓
Confidence Scoring (0-1.0)
  ↓
Routing Decision (>0.8 for secondary)
  ├─ Primary: Always route to main file
  └─ Secondary: Route to area-specific files (if >80% confidence)
  ↓
Transformation (for context entries only)
  ├─ Remove: pronouns, dates, feelings
  ├─ Extract: topical facts
  └─ Custom formulation: per context area
  ↓
Deduplication Check (fuzzy match 80%)
  ↓
Append to Destination Files
  ├─ With cross-links: *From:* [[daily-note#section]]
  └─ With related links: *Related:* [[context-area]]
  ↓
Smart Memory Detection
  ├─ Serena Memory (technical patterns)
  └─ Graphiti Memory (strategic insights)
  ↓
Tracker Update (mark processed, except today)

Integration Points

Serena MCP: Pattern matching, file operations
- mcp__serena__list_dir - Find daily notes
- mcp__serena__search_for_pattern - Extract sections
- mcp__serena__find_file - Locate context files
- mcp__serena__write_memory - Update Serena memories
Graphiti MCP: Knowledge graph storage (3 instances)
- graphiti-mokai - MOKAI business insights
- graphiti-finance - Financial insights
- graphiti-personal - Personal insights
Daily Notes Template (98-templates/daily-note.md:327)
- ### 🧠 Notes section (extraction target)
- Dataview queries for tasks, events, projects
Context Areas (24 files across PARA structure)
- Business: mokai, mokhouse, accounting, crypto, soletrader, smsf, safia, trust
- Personal Development: learning, mindset, psychedelics
- Health & Fitness: health-fitness, diet, medical, gym
- Tech: tech, ableton, mac, ai, engineering
- Other: claude-code, harry, people

Configuration

Skill Trigger Phrases

Auto-triggers when user says:

"What did I work on today?"
"Extract content from my daily note"
"I'm done for today"
"Wrapping up"
"Daily summary"
"End of day"
References "### 🧠 Notes" section

Environment Variables

None required (uses Claude Code's built-in AI and MCP connections)

Setup Requirements

Enable skill in Claude Code:
- Settings > Capabilities > Skills > Enable
- Restart Claude Code after enabling
Ensure daily note template is configured:
- Template location: 98-templates/daily-note.md
- Must include ### 🧠 Notes section
Verify destination files exist:
- 04-resources/diary.md
- 04-resources/context.md
- 01-areas/p-dev/insights.md
- 04-resources/ideas.md
Optional: Pre-create area-specific files (auto-created if missing):
- business/mokai/diary-mokai.md
- business/mokai/CLAUDE.md
- etc.

Usage

Auto-Triggering (Primary Method)

Natural conversation triggers:

plaintext

User: "What did I work on today?"
→ Skill activates, scans today's daily note, extracts and classifies entries

User: "Extract content from my daily note"
→ Skill activates with explicit extraction intent

User: "I'm done for today, extract my notes"
→ End-of-day trigger, processes today's entries

Manual Script Execution (Testing/Debugging)

bash

# Extract specific date
python .claude/skills/daily-workflow/scripts/extract_content.py 2025-10-21

# Scan for unprocessed notes
python .claude/skills/daily-workflow/scripts/extract_content.py --scan-unprocessed

Common Scenarios

Scenario 1: End-of-Day Extraction

plaintext

User: "Done for today"
Claude: [Activates daily-workflow skill]
  1. Scans today's daily note
  2. Extracts 5 entries from ### 🧠 Notes
  3. Classifies: 2 Diary, 1 Insight, 1 Context, 1 Idea
  4. Presents routing plan with confidence scores
  5. User approves: "y"
  6. Routes entries to appropriate files
  7. Detects memory opportunities (Serena + Graphiti)
  8. Updates tracker

Scenario 2: Specific Date Extraction

plaintext

User: "Extract content from my note on October 19th"
Claude: [Activates daily-workflow skill with date context]
  1. Locates 🌤️ Fri - 19th Oct 25.md
  2. Extracts and processes entries
  3. Routes to destinations

Scenario 3: Batch Processing

plaintext

User: "Process all unprocessed daily notes from this week"
Claude: [Activates daily-workflow skill]
  1. Scans for unprocessed notes (excluding today)
  2. Processes each chronologically
  3. Presents combined routing decisions
  4. Executes after approval

Approval Flow

After classification, user chooses:

[y]es - Approve all routing decisions and execute
[n]o - Cancel extraction
[e]dit - Modify routing decisions before execution
[s]kip entry - Skip specific entries

For memory updates:

[a] Update all - Update all suggested memories (confidence >0.80)
[s] Selective - Choose which memories to update
[n] Skip - No memory updates
[l] Later - Queue for end-of-day batch review

Examples

Example 1: Daily Diary Entry to Context Transformation

Original Daily Note Entry ([00 - Daily/🌤️ Mon - 21st Oct 25.md](00 - Daily/)):

markdown

### 🧠 Notes

Today I built a Supabase database for financial tracking for MOKAI. Took forever to set up auth but got it working. Will use for invoice management.

AI Classification:

Type: Context (96% confidence)
Reason: Factual technical information
Primary: 04-resources/context.md
Secondary: business/mokai/CLAUDE.md (98%), tech/CLAUDE.md (94%)

Routed to 04-resources/context.md:

markdown

- Supabase: Financial tracking database with auth, for invoice management
  *From:* [[00 - Daily/🌤️ Mon - 21st Oct 25#🧠 Notes]]

Routed to tech/CLAUDE.md:

markdown

- Supabase: Financial tracking database with auth
  *From:* [[00 - Daily/🌤️ Mon - 21st Oct 25#🧠 Notes]]
  *Related:* [[business/mokai/context-mokai]]

Routed to business/mokai/CLAUDE.md:

markdown

- Financial tracking system: Supabase database for invoice management
  *From:* [[00 - Daily/🌤️ Mon - 21st Oct 25#🧠 Notes]]
  *Related:* [[tech/context-tech]]

Example 2: Smart Memory Detection

Entry: "Created new workflow using Supabase triggers for invoice automation"

Detection:

yaml

System: Serena
Memory: system_patterns_and_guidelines
Confidence: 0.87
Update: "Invoice automation: Supabase triggers reduce manual data entry"

System: Graphiti
Instance: graphiti-mokai
Confidence: 0.85
Episode: "MOKAI invoice automation using Supabase triggers for financial workflow optimization"

Example 3: Multi-Area Routing

Entry: "Finished mixing Nintendo track for MOK HOUSE client. Client approved on first submission."

Classification:

Type: Diary (94% confidence)
Primary: 04-resources/diary.md
Secondary: business/mokhouse/diary-mokhouse.md (97%)

Result:

Original narrative preserved in both files
Cross-links added bidirectionally
No transformation (diary entries keep narrative voice)

Dependencies

External Services

None (uses Claude Code's built-in capabilities)

Python Packages (Built-in)

os, re, json, sys - Standard library
pathlib - File path operations
datetime - Date parsing and formatting
difflib - Fuzzy matching for deduplication

Internal Dependencies

Claude Code Skills System - Auto-trigger mechanism
Serena MCP - File operations, pattern matching, memory management
Graphiti MCP - Knowledge graph storage (3 instances)
Daily Notes Template - ### 🧠 Notes section format
PARA Structure - Context area organization (01-areas, 04-resources)

Troubleshooting

Common Issues

Issue 1: Skill not triggering automatically

Cause: Skills not enabled in Claude Code settings
Solution: Settings > Capabilities > Skills > Enable, then restart Claude Code

Issue 2: Missing ### 🧠 Notes section in extraction

Cause: Daily note uses different heading format
Solution: Ensure daily note template matches exact format (case-sensitive, emoji included)

Issue 3: Entries classified incorrectly

Cause: Ambiguous entry content or insufficient context
Solution: Use edit mode [e] to correct classification, AI learns from patterns

Issue 4: Too many secondary destinations

Cause: Confidence threshold too low
Solution: Currently set to 0.8 (80%), can be increased for stricter routing

Issue 5: Diary files not auto-created

Cause: File permission issues or incorrect path
Solution: Verify write permissions, check directory structure exists

Issue 6: Duplicate entries appended

Cause: Fuzzy matching threshold too low
Solution: Currently 80% similarity, can be increased to prevent duplicates

Issue 7: Memory detection missing opportunities

Cause: Entry doesn't contain trigger keywords
Solution: Review detection keywords in SKILL.md:119, adjust confidence threshold

Issue 8: Wrong Graphiti instance selected

Cause: Routing logic based on area doesn't match entry content
Solution: Use selective mode [s] to manually specify correct instance

Debugging

bash

# Check tracker status
cat .claude/commands/.extract-daily-content-tracker.json | jq '.'

# View last processed file
cat .claude/commands/.extract-daily-content-tracker.json | jq '.processed_files | to_entries | last'

# Reset tracker (force re-extraction)
rm .claude/commands/.extract-daily-content-tracker.json

# Test Python script directly
python .claude/skills/daily-workflow/scripts/extract_content.py --scan-unprocessed

# Verify daily note format
grep -A 10 "### 🧠 Notes" "00 - Daily/🌤️ $(date +"%a - %eth %b %y").md"

Monitoring & Maintenance

Logs

Skill activation: Visible in Claude Code conversation (no separate logs)
Tracker file: .claude/commands/.extract-daily-content-tracker.json tracks:
- Last scan timestamp
- Processed files with modification times
- Entries processed per file
- Routing decisions with confidence scores

Monitoring

bash

# Check recent extractions
jq '.processed_files | to_entries | map({date: .value.processed_at, count: .value.entries_count}) | sort_by(.date) | reverse | .[0:5]' .claude/commands/.extract-daily-content-tracker.json

# Total entries processed
jq '.entries_processed' .claude/commands/.extract-daily-content-tracker.json

Update Frequency

Skill logic: Update SKILL.md when workflow changes
Templates: Update reference/templates.md when daily note format changes
Formatting rules: Update reference/formatting-rules.md when transformation rules change
Python script: Update when new extraction features needed

Related Systems

MOKAI Daily Operations Skill - MOKAI-specific daily workflow
MOK HOUSE Operations Skill - MOK HOUSE business workflow
Extract Daily Content Command - Original manual command (deprecated)
Daily Note Template - Template with extraction target section
Serena MCP Memory - Memory management system
Graphiti Knowledge Graph - Knowledge graph storage

Future Enhancements

Phase 2 (Planned)

Auto-categorization during writing: Real-time classification as user types in daily note
Batch processing UI: Interactive dashboard for reviewing and approving batches
Machine learning improvements: Learn from user corrections to improve classification accuracy
Custom context areas: User-defined context areas beyond the 24 default ones
Export functionality: Export extracted data to external tools (Notion, Roam, etc.)

Phase 3 (Exploration)

Voice input integration: Extract from voice-dictated daily notes
Mobile app sync: Sync extractions from mobile daily notes
Collaboration features: Share context/insights with team members
Analytics dashboard: Visualize entry patterns, productivity trends
AI summarization: Generate weekly/monthly summaries from extracted entries

Change Log

2025-10-21: Initial implementation
- Created skill structure with SKILL.md, reference files, Python script
- Implemented auto-trigger mechanism based on conversation context
- Added AI classification for 4 entry types (Diary/Insight/Context/Idea)
- Configured routing to 24 context areas with confidence scoring
- Integrated Serena and Graphiti memory detection
- Documented comprehensive formatting and transformation rules
- Created deduplication logic with 80% fuzzy matching
- Implemented tracker system for incremental processing
- Added cross-linking and bidirectional references
- Updated skills README with testing instructions

Testing & Validation

Manual Testing Checklist

After implementing or modifying the skill:

Skill triggers on phrase "What did I work on today?"
Extracts entries from ### 🧠 Notes section correctly
Classifies entries into correct types (Diary/Insight/Context/Idea)
Routes to primary destinations (all types)
Routes to secondary destinations when confidence >80%
Transforms diary narratives to factual statements for context files
Creates cross-links with correct format
Auto-creates missing diary files with frontmatter
Deduplicates entries (fuzzy match 80%)
Updates tracker after successful extraction
Does NOT mark today's note as processed
Detects Serena memory opportunities
Detects Graphiti memory opportunities (correct instance)
Presents approval flow to user
Handles user corrections in edit mode

Validation Commands

bash

# Verify skill files exist
ls -lah .claude/skills/daily-workflow/
ls -lah .claude/skills/daily-workflow/reference/
ls -lah .claude/skills/daily-workflow/scripts/

# Check SKILL.md line count (should be <500)
wc -l .claude/skills/daily-workflow/SKILL.md

# Verify Python script is executable
ls -l .claude/skills/daily-workflow/scripts/extract_content.py | grep -q 'x' && echo "Executable" || echo "Not executable"

# Test Python script
python .claude/skills/daily-workflow/scripts/extract_content.py --scan-unprocessed

Success Criteria

✅ Correctly classify entry types (>90% accuracy with AI)
✅ Route to appropriate areas with good confidence (>80% for secondary)
✅ Show clear reasoning for routing decisions
✅ Allow easy manual override via approval flow
✅ Create valid cross-links bidirectionally
✅ Auto-create diary files with proper frontmatter
✅ Track all routing decisions in tracker JSON
✅ Handle incremental runs efficiently (skip already processed)
✅ Maintain file integrity across all destinations
✅ Respect >80% confidence threshold for secondary routing
✅ Transform diary to context correctly (remove pronouns, dates, feelings)
✅ Preserve narrative in diary files
✅ Detect memory opportunities with >75% confidence

Notes

Token Efficiency: SKILL.md kept under 500 lines, uses progressive disclosure pattern
Auto-Detection: Triggers automatically based on frontmatter description
Hybrid Approach: Skill for common workflows, command as manual fallback
Smart Context: Only loads reference files when needed (not pre-loaded)
Incremental Processing: Tracker ensures files aren't re-processed unnecessarily
Today's Note: Always re-processes today's note (allows same-day updates)
Deduplication: 80% fuzzy match threshold prevents near-duplicates
Confidence Threshold: 80% for secondary routing balances precision and recall
Memory Detection: Suggests Serena/Graphiti updates but requires user approval
Cross-Linking: Bidirectional links maintain vault connectivity

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Install this agent skill to your Project

SKILL.md

Daily Workflow Automation Skill

Overview

Location

Primary Files

Configuration

Related Files

Architecture

Components

Data Flow

Integration Points

Configuration

Skill Trigger Phrases

Environment Variables

Setup Requirements

Usage

Auto-Triggering (Primary Method)

Manual Script Execution (Testing/Debugging)

Common Scenarios

Approval Flow

Examples

Example 1: Daily Diary Entry to Context Transformation

Example 2: Smart Memory Detection

Example 3: Multi-Area Routing

Dependencies

External Services

Python Packages (Built-in)

Internal Dependencies

Troubleshooting

Common Issues

Debugging

Monitoring & Maintenance

Logs

Monitoring

Update Frequency

Related Systems

Future Enhancements

Phase 2 (Planned)

Phase 3 (Exploration)

Change Log

Testing & Validation

Manual Testing Checklist

Validation Commands

Success Criteria

Notes