{{SKILL_NAME}} Skill

Overview

{{SKILL_DESCRIPTION}}

This is a System Skill - it provides handles to operate a personal data system. As commands run and data accumulates, context builds and compounds. The system learns patterns and provides increasingly valuable insights through an OODA loop of observation, orientation, decision, and action.

Mental Model: The OODA Loop

Operating this skill involves running a continuous cycle:

1. Observe - Check current state (./{{skill-name}} status) and review data (./{{skill-name}} list)
2. Orient - Analyze patterns in the data (./{{skill-name}} stats --period week)
3. Decide - Determine optimal actions based on accumulated insights
4. Act - Execute commands, provide recommendations, celebrate milestones

Each cycle builds on accumulated data, making insights more valuable over time.

Dependencies

• Binary location: ~/.claude/skills/{{skill-name}}/{{skill-name}}
• Database: Auto-created at ~/.claude/skills/{{skill-name}}/{{skill-name}}.db on first run
• No external dependencies required

Quick Decision Tree

User request -> What kind of action?
   |-- Add new data -> Use add command
   |-- View current state -> Use status or list command
   |-- Analyze patterns -> Use stats command (day/week/month/year)
   |-- Update existing data -> Use update command
   |-- Remove data -> Use delete command
   +-- Search/query -> Use search or list with filters

Core Commands

To see all available options: Run ./{{skill-name}} --help or ./{{skill-name}} <command> --help

Adding Data

Add a new item to the system:

./{{skill-name}} add --content "Description of item"
./{{skill-name}} add --content "Another item" --category "work"

Options:

• --content <text> - The main content/description (required)
• --category <name> - Optional category for organization
• --priority <level> - Optional priority (low/medium/high)

JSON output:

./{{skill-name}} add --content "New item" --json
# Returns: {"status": "added", "id": 1, "content": "New item", "created_at": "..."}

Checking Status

See current state of the system:

./{{skill-name}} status
./{{skill-name}} status --json  # For programmatic use

Listing Data

View stored items:

./{{skill-name}} list                    # Recent items
./{{skill-name}} list --days 7           # Last 7 days
./{{skill-name}} list --category "work"  # Filter by category
./{{skill-name}} list --json             # For programmatic use

Output includes:

• Item IDs for reference
• Content and metadata
• Timestamps
• Status/completion info

Analyzing Patterns

Get insights from accumulated data:

./{{skill-name}} stats --period day     # Today's stats
./{{skill-name}} stats --period week    # This week
./{{skill-name}} stats --period month   # This month
./{{skill-name}} stats --period year    # This year
./{{skill-name}} stats --json           # For programmatic use

Statistics include:

• Total items and trends
• Category distribution
• Most active times
• Pattern insights

Updating Data

Modify existing items:

./{{skill-name}} update <id> --status "completed"
./{{skill-name}} update <id> --note "Additional context"

Deleting Data

Remove items:

./{{skill-name}} delete <id>
./{{skill-name}} delete <id> --confirm  # Skip confirmation

Essential Workflows

Daily Review

To provide a daily summary:

1. Fetch today's data: ./{{skill-name}} stats --period day --json
2. Parse and present insights:
   • Summary of activity
   • Notable patterns
   • Recommendations

Weekly Analysis

To provide weekly insights:

1. Fetch week's data: ./{{skill-name}} stats --period week --json
2. Identify patterns:
   • Compare to previous periods
   • Note trends and changes
3. Make recommendations:
   • Actionable insights based on data

Pattern Recognition Over Time

As data accumulates, provide increasingly valuable insights:

After a few items (1-10):

• Basic counts and recent activity
• Initial patterns forming

After several days (10-50 items):

• Daily/weekly patterns visible
• Category trends emerging

After a week (50-100 items):

• Clear usage patterns
• Time-of-day preferences
• Category distributions

After a month (100+ items):

• Long-term trends
• Seasonal patterns
• Predictive insights

Commands for pattern recognition:

./{{skill-name}} stats --period month --json  # Monthly patterns
./{{skill-name}} list --days 90 --json        # Long-term history

Common Pitfalls

Missing Required Fields

• Don't run commands without required options
• Do use --help to see required options

Non-Descriptive Content

• Don't use vague content: "stuff", "thing", "misc"
• Do use specific, searchable descriptions

Ignoring Pattern Signals

• Don't ignore trends in the data
• Do surface insights when patterns emerge

Best Practices

Data Entry

• Use descriptive content for better analytics
• Apply categories consistently
• Add context via notes when relevant

User Interaction

• Celebrate milestones as data accumulates
• Surface insights proactively
• Suggest optimizations based on patterns

Data Analysis

• Review stats regularly
• Track trends over time
• Use JSON output for custom analytics

Command Composition

Combine commands for deeper insights:

# Morning check
./{{skill-name}} status
./{{skill-name}} stats --period day

# Weekly review
./{{skill-name}} list --days 7 --json
./{{skill-name}} stats --period week --json

# Long-term analysis
./{{skill-name}} stats --period month --json
./{{skill-name}} list --days 90 --json

Technical Notes

JSON Output

All commands support --json flag for programmatic access:

./{{skill-name}} status --json
./{{skill-name}} list --json
./{{skill-name}} stats --json

Use JSON output when:

• Parsing data programmatically
• Building custom analytics
• Feeding into other tools

Database

• Location: ~/.claude/skills/{{skill-name}}/{{skill-name}}.db
• Format: SQLite
• Persistence: All data saved permanently
• Analytics: Richer insights as data accumulates

Binary Location

• Path: ~/.claude/skills/{{skill-name}}/{{skill-name}}
• To run from skill directory: ./{{skill-name}}
• Full path from anywhere: ~/.claude/skills/{{skill-name}}/{{skill-name}}

The System Skill Pattern in Action

This skill demonstrates the System Skill Pattern (CLI + SKILL.md + Database):

1. CLI Binary: Handles to operate the system
2. SKILL.md: Operating procedure for the OODA loop
3. SQLite Database: Persistent memory where every interaction adds context

The key insight: With these three components, the skill animates a system rather than just responding to requests. Each interaction builds on the last. Analytics become richer. Insights get sharper. The tool compounds in value.

See also: README.md for technical details and implementation guide.