Inbox Processing & Large-Scale Organization

Overview

Process large Things3 inboxes (100+ items) efficiently through batch analysis, confidence-based automation, and intelligent user interaction.

CRITICAL: Before using this skill:

•Load things3-productivity skill for MCP tool patterns
•Read private-prefs/personal-taxonomy.json for organizational context
•Create temp/inbox-processing/ folder for session state

When to use: Inbox has 100+ items requiring organization.

Personal Organization Integration

Claude uses LLM-driven analysis with semantic understanding from personal-taxonomy.json:

•Work identification: Tags (e.g., "AMPL") and areas (e.g., "Amplitude")
•Priority system: 1-9 scale, 7+ for high priority
•Project patterns: Existing projects and typical content
•Semantic matching: Based on meaning, not just keywords

Core Workflow: 14-Step Process

Phase 1: Initialize & Analyze (Steps 1-3)

Step 1: Setup - Create temp/inbox-processing/ with:

code

session.md                    # Batch progress, statistics
match_results.json            # Decisions with confidence scores
pending_decisions.json        # Items awaiting approval
high_confidence_actions.json  # Auto-apply candidates (≥90%)
reference_items.json          # Detected reference notes
execution_log.md              # Complete action history

Step 2: Load Inbox Batch - First batch: 50 items, subsequent: 50-100 items

python

read_tasks(when="inbox", limit=50, include_notes=True)

Step 3: Load System Inventory - Cache once per session

python

list_areas()    # All areas with IDs and tags
list_projects() # All projects with metadata
list_tags()     # All tags including hierarchy

Phase 2: Categorize & Match (Steps 4-6)

Step 4: Categorize Items - Use semantic understanding:

•Actionable tasks: Clear next actions, specific outcomes
•Reference items: Notes, ideas, meeting summaries
•Project candidates: Multiple related tasks suggesting new project

Step 5: Match to Existing Structure - LLM-driven matching:

•Analyze title and notes semantically
•Consider existing areas, projects, tags
•Apply personal taxonomy patterns
•Generate confidence score (0-100%)

Step 6: Categorize by Confidence

•High (90-100%): Auto-apply with batch approval
•Medium (70-89%): Ask user in batches
•Low (<70%): Keep in inbox or ask individually

Phase 3: User Interaction (Steps 7-9)

Step 7: High-Confidence Batch Approval

markdown

## High-Confidence Matches (Batch 1: 35 items)

### Area: Amplitude (25 items, 90-100% confidence)
**Direct area references:**
- "Amplitude: Fix login bug" (100%) - Explicit area mention
- "Dashboard review for AMPL" (95%) - Work tag + clear context

**Action Plan:**
1. Set area="Amplitude" for 25 items
2. Add tags=["AMPL"] where missing (15 items)
3. Move to Today (10 high-priority items)

Approve? [Yes/No/Review individually]

Step 8: Ambiguous Matches - Batched by suggested area/project

markdown

## Ambiguous Matches (10 items, 70-85%)

1. "Design review notes" (85%) → area="Amplitude"?
   [Approve / Different area / Keep in Inbox]

Quick response: "1 Approve, 2 Different area: Personal, ..."

Step 9: Reference Items - Present individually

markdown

## Reference Item 1 of 5

**Item:** [Empty title]
**Notes:** "Customer success meeting - Q4 roadmap..."

**Options:**
1. **Migrate to Notion** (Recommended)
2. **Create project "Q4 Roadmap"**
3. **Convert to task** with title
4. **Delete**

Your choice: [1/2/3/4]

Phase 4: Execute (Steps 10-12)

Step 10: Batch Execute - Use MCP tools efficiently

python

move_tasks(task_uuids=[...], target_list="today")
add_tags(task_uuids=[...], tags=["AMPL"])
edit_task(task_uuid="...", area="Amplitude")
migrate_inbox_to_notion(block_id="...")
create_project(name="Q4 Roadmap", area="Amplitude")

Step 11: Track Progress - Update session.md incrementally

markdown

## Progress
- Total: 446 | Processed: 100 | Remaining: 346

## Statistics
- High confidence: 70 (70%)
- Auto-applied: 65 (93% of high-confidence)

## Patterns Learned
- "Dashboard" + AMPL → area="Amplitude" (98% accuracy)

Step 12: Summary Report

markdown

## Complete Summary

**Before:** Inbox: 446 | Amplitude: 120 tasks

**Processed:** 310 organized (70%), 45 migrated (10%), 91 kept (20%)

**After:** Inbox: 91 | Amplitude: 385 tasks (+265)

**New Projects:** Q4 Roadmap (12 tasks), Team Onboarding (8 tasks)

Phase 5: Learn & Cleanup (Steps 13-14)

Step 13: Propose Taxonomy Updates - Based on patterns discovered

markdown

## Proposed Taxonomy Updates

### New Project Keywords
"On-call": ["on-call", "oncall", "incident"] (25 occurrences, 100% accuracy)
"Dashboard": ["dashboard", "metrics"] (18 occurrences, 98% accuracy)

### Common Patterns
- Empty title + meeting notes → migrate_to_notion (95% approval, 12 samples)

Approve these updates? [Yes/No/Modify]

CRITICAL: Always get user approval before updating personal-taxonomy.json.

Step 14: Cleanup - Archive or delete temp folder

bash

mv temp/inbox-processing temp/inbox-processing-2025-11-30-archive
# Or: rm -rf temp/inbox-processing

Matching Strategy: LLM-Driven

Core Principle: Use Claude's semantic understanding, not hard-coded algorithms.

Confidence Assessment

High Confidence (90-100%)

•Explicit area/project mentions
•Strong semantic relationship to existing structure
•Consistent with taxonomy patterns

Example: "Amplitude: Fix login bug" → 100%

•Explicit area mention + work tag "AMPL" + matches existing area

Medium Confidence (70-89%)

•Reasonable but ambiguous
•Could fit multiple areas/projects

Example: "Design review notes" → 85%

•Has work tag but generic term, needs confirmation

Low Confidence (<70%)

•No clear organizational fit
•Keep in inbox or ask individually

Always Provide Reasoning

code

**Task:** "Dashboard analytics update"
**Confidence:** 95%
**Suggested:** area="Amplitude", project="Dashboard"
**Reasoning:**
- Work tag "AMPL" present
- "Dashboard" matches existing project
- Pattern seen 18 times with 98% accuracy

Reference Item Detection

Use semantic understanding to identify reference items:

Strong indicators:

•Empty title + substantial notes (>50 words)
•Tagged "migrate to notion"
•URL-only content
•Titles: "Note:", "Idea:", "Reference:"

Contextual analysis:

•Informational rather than actionable
•Multi-paragraph notes without clear next actions
•Meeting summaries, research findings

Four-Option Decision Tree

1. Migrate to Notion (Most Common)

•Preserve detailed notes as documentation
•Extract actionable items as separate Things3 tasks
•Best for: Meeting notes, research, planning docs

2. Create Project + Extract Tasks

•Content suggests multiple related actions
•Best for: Multi-step initiatives, campaigns

3. Convert to Single Task

•Add descriptive title to empty-title item
•Best for: Simple notes, reminders

4. Delete

•Outdated or no longer relevant

User Interaction Patterns

Minimizing Fatigue

•Auto-apply ≥90% confidence (reduces decisions by 60-70%)
•Batch similar questions together
•Provide quick response formats
•Show progress and remaining items
•Target: <20 interaction points for 400 items

Four Interaction Types

•Batch Approval (High confidence) - Group by area/project, simple Yes/No
•Batched Questions (Medium confidence) - Numbered list, quick format
•Individual Questions (References) - Full preview with 4 options
•Change Validation (Before execution) - Before/After summary

Learning & Taxonomy Updates

After each session, propose updates to personal-taxonomy.json:

What to capture:

•New project keywords - Projects that appeared frequently with accuracy rates
•Common matching patterns - Successful matches and user corrections
•Reference patterns - What user consistently migrated/converted
•Workflow preferences - Batch sizes and interaction patterns that worked

Update Format:

json

{
  "things3": {
    "project_keywords": {
      "On-call": ["on-call", "incident", "alert"]
    },
    "learned_patterns": [{
      "pattern": "Dashboard + AMPL tag",
      "action": "area=Amplitude, project=Dashboard",
      "accuracy": 98,
      "sample_count": 18
    }]
  }
}

Continuous Improvement

•First session: ~70% auto-apply
•Second session: ~80% auto-apply (learned patterns)
•Ongoing: Approach 85-90% auto-apply

Best Practices

Batch Sizing

•First batch: 50 items (establish patterns)
•Subsequent: 50-100 items (apply learned patterns)
•Large inboxes (500+): Consider 2-3 sessions across days

Performance

•Cache system inventory once per session
•Use batch MCP operations where possible
•Leverage LLM context window
•Take breaks after 100-150 items

Error Recovery

•All actions logged in execution_log.md
•Can undo with Things3 MCP tools
•Session state saved (resume capability)
•Ask before destructive operations

Troubleshooting

Low auto-apply rate (<50%)

•Inbox too diverse - manual review outliers
•Update taxonomy after session
•Consider lowering threshold to 85%

Processing too slow

•Reduce batch size to 25-50
•Skip reference reviews (mark for later)
•Focus on high-confidence first pass

Incorrect matches

•Review confidence reasoning
•Check taxonomy alignment
•Add corrections to learned patterns

Session interruption

•Resume from session.md
•Check execution_log.md for last action

Integration

things3-productivity: MCP tool patterns, taxonomy, change validation notion-workflows: Migration destinations, documentation structure productivity-integration: Cross-system orchestration, review cycles

Remember: Trust LLM semantic understanding over hard-coded rules. Always get user approval before changes. Learn from each session to improve future processing.