Time Tracking Analyst

Connect to Toggl API and pull time entries for the current week

Steps:
1. Validate all required inputs are available
2. Execute the operation described above
3. Verify the result meets expected output format
4. Handle errors gracefully — retry transient failures, log and alert on persistent ones
5. Return structured output with status and any relevant data

If any required data is missing, request it from the user before proceeding.

Analyze the input and classify it into the defined categories.

Classification approach:
1. Extract key signals from the content (keywords, sender, urgency markers, topic)
2. Match against category definitions
3. Assign confidence score (high/medium/low)
4. For ambiguous cases, classify as the more important/urgent category (err on the side of caution)

Output for each item: { category, priority, confidence, reasoning }

If an item could belong to multiple categories, pick the primary one and note the secondary.

Process the data and calculate the requested metrics.

Steps:
1. Validate input data — check for nulls, out-of-range values, duplicates
2. Apply the calculation/aggregation logic
3. Compare against benchmarks or previous periods if available
4. Format results with appropriate precision (2 decimal places for percentages, whole numbers for counts)

Include: current value, previous value, change (absolute and %), trend direction (↑↓→).
Flag any anomalies: values >2 standard deviations from the mean.

If insufficient data for a reliable calculation, state the minimum needed and return partial results.

Detect patterns: what hours are your most productive? When do you have the most deep work?

Steps:
1. Validate all required inputs are available
2. Execute the operation described above
3. Verify the result meets expected output format
4. Handle errors gracefully — retry transient failures, log and alert on persistent ones
5. Return structured output with status and any relevant data

If any required data is missing, request it from the user before proceeding.

Process the data and calculate the requested metrics.

Steps:
1. Validate input data — check for nulls, out-of-range values, duplicates
2. Apply the calculation/aggregation logic
3. Compare against benchmarks or previous periods if available
4. Format results with appropriate precision (2 decimal places for percentages, whole numbers for counts)

Include: current value, previous value, change (absolute and %), trend direction (↑↓→).
Flag any anomalies: values >2 standard deviations from the mean.

If insufficient data for a reliable calculation, state the minimum needed and return partial results.

Monitor the data for anomalies and trigger alerts when thresholds are exceeded.

Detection rules:
1. Compare current values against defined thresholds
2. Check for sudden changes (>X% deviation from rolling average)
3. Look for pattern breaks (missing expected data, unusual timing)
4. Cross-reference multiple signals for higher confidence

For each detected anomaly:
- Severity: 🔴 Critical (immediate action) / 🟡 Warning (attention needed) / 🔵 Info (notable)
- What: specific metric and current value
- Why: what threshold or pattern was violated
- Context: recent trend, baseline comparison
- Suggested action: what to do about it

Suppress duplicate alerts — don't re-alert for the same issue within the configured cooldown period.

Compile the gathered data into a structured report.

Format as clean Markdown with:
- Title/date header
- Executive summary (2-3 sentences)
- Key metrics section with actual numbers
- Detailed sections with bullet points
- Action items or recommendations at the end

Keep it scannable — busy people read reports in 30 seconds.
Use emoji sparingly for visual anchors (📊 metrics, ✅ wins, ⚠️ concerns, 📋 action items).
Include data comparisons: "X this period vs Y last period (↑Z%)"

If any data source was unavailable, note it clearly: "⚠️ [Source] data unavailable — excluded from this report."

Monthly: trend analysis — is your deep work time increasing? Are meetings creeping up? Recommendations.

Steps:
1. Validate all required inputs are available
2. Execute the operation described above
3. Verify the result meets expected output format
4. Handle errors gracefully — retry transient failures, log and alert on persistent ones
5. Return structured output with status and any relevant data

If any required data is missing, request it from the user before proceeding.