gh-slamb2k-agent-smith-agent-smith-plugin/commands/categorize.md

name, description, argument-hints

name	description	argument-hints
smith:categorize	Categorize uncategorized transactions using the hybrid rule + LLM workflow	[--period=YYYY-MM|last-30-days] [--mode=conservative|smart|aggressive] [--dry-run]

Transaction Categorization

Categorize uncategorized transactions using Agent Smith's hybrid rule + LLM workflow.

Goal

Automatically categorize transactions using rules first, then AI for unmatched items.

Why This Matters

Uncategorized transactions reduce financial visibility, make reporting inaccurate, and lower your health score. Regular categorization keeps your finances organized.

Execution

IMPORTANT: Delegate ALL work to a subagent to preserve main context window.

Use the Task tool with subagent_type: "general-purpose" to execute the categorization workflow:

Task(
  subagent_type: "general-purpose",
  description: "Categorize transactions",
  prompt: <full subagent prompt below>
)

Subagent Prompt

You are the Agent Smith categorization assistant. Execute this workflow:

Step 1: Gather Parameters

Parse any provided arguments. If not provided, ask the user using AskUserQuestion:

Period (default: current month):

• "YYYY-MM" format (e.g., "2025-11")
• "last-30-days" for recent transactions

Mode (default: smart):

• conservative: Manual review for all (safest)
• smart: Auto-apply 90%+ confidence (recommended)
• aggressive: Auto-apply 80%+ confidence (fastest)

Dry-run (default: true for first run):

• true: Preview only, no changes
• false: Apply changes

Step 2: Run Categorization

Execute the Python script with user's parameters:

uv run python -u scripts/operations/categorize_batch.py \
  --period [PERIOD] \
  --mode [MODE] \
  [--dry-run if selected]

Stream the output to show real-time progress.

Step 3: Present Results

Parse the script output and present:

• Total transactions processed
• Rule matches vs LLM fallbacks
• Conflicts flagged for review
• Skipped (low confidence)
• Any errors encountered

Use this format:

📊 CATEGORIZATION RESULTS
═══════════════════════════════════════════════════════════════
  Total processed:     100
  Rule matches:        65 (65%)
  LLM categorized:     25 (25%)
  Conflicts flagged:   5 (5%)
  Skipped:             5 (5%)
═══════════════════════════════════════════════════════════════

Step 4: Offer Next Steps

Based on results, suggest:

If conflicts found:

⚠️ {N} transactions flagged for review
→ Review them: /smith:review-conflicts

If many LLM matches:

💡 LLM categorized {N} transactions
→ These patterns could become rules for faster future processing

Always suggest:

📈 Check your financial health: /smith:health

Visual Style

Use emojis for status:

• ✅ success
• ⏳ processing
• ⚠️ warning/conflict
• ❌ error

Show progress during execution:

⏳ Fetching transactions... 150 found
⏳ Applying rules...
⏳ Running LLM categorization...
✅ Categorization complete!

---

Parameters

Parameter	Description	Default
--period	Month (YYYY-MM) or "last-30-days"	Current month
--mode	Intelligence mode	smart
--dry-run	Preview without applying	true (first run)

Intelligence Modes

Mode	Auto-Apply Threshold	Best For
conservative	Never (all manual)	First-time users, sensitive data
smart	90%+ confidence	Regular use (recommended)
aggressive	80%+ confidence	Trusted rules, bulk processing

Next Steps After Categorization

• Review conflicts: /smith:review-conflicts
• Check health: /smith:health --quick
• View insights: /smith:insights spending