{"id":"ee603965-36f5-422f-8514-f9917a66ac5d","shortId":"7XXQHV","kind":"skill","title":"configuring-tasks","tagline":"Configures scheduled AI tasks that analyze Insights and Dashboards. Use for anomaly detection, forecasting, alerts, or recurring automated monitoring.","description":"# Configuring Tasks\n\n## Quick Start\n\nA task is a scheduled AI agent that runs on a cron, analyzes an Insight or Dashboard, and creates a discovery when the analysis produces a finding. Your `instructions` string is the prompt the AI follows on each run.\n\nTo create a task:\n\n1. Identify what the user wants the AI to watch for (anomalies, a forecast, or open-ended analysis)\n2. Choose the task type and target slug\n3. Write clear natural-language instructions -- these are the AI's prompt every run\n4. Pick a cron schedule that fits the task instructions\n5. Call `create_task` on the Altertable MCP server\n\n## When to Use This Skill\n\n- User wants an Insight monitored for anomalies on a schedule\n- User wants a metric forecast recurring on a cadence\n- User wants ongoing AI analysis of an Insight or Dashboard\n- User asks for automated alerts when something changes\n\n## Task Types\n\nAll three types run AI analysis driven by your `instructions`. They differ in what the AI is asked to focus on.\n\n| Type                 | Target               | AI focus                                                    |\n| -------------------- | -------------------- | ----------------------------------------------------------- |\n| `anomaly_detection`  | Insight slug           | Find outliers and unusual values in the Insight's data        |\n| `forecast`           | Insight slug           | Project future values and flag divergence from expectations |\n| `monitor`            | Insight/Dashboard slug | Open-ended analysis -- whatever the instructions describe   |\n\n## Core Workflow\n\n### Step 1: Identify the Target\n\nThe user needs an existing resource to target. If they don't have one yet:\n\n1. Help them create the Insight, Dashboard, or connection first (see creating-insights or exploring-data skills)\n2. Use the resulting slug as the `target_slug`\n\n### Step 2: Choose Task Type\n\nMatch the user's goal to a task type:\n\n- \"Alert me if signups drop unexpectedly\" -> `anomaly_detection` on the signup Insight\n- \"Forecast next month's revenue\" -> `forecast` on the revenue Insight\n- \"Analyze my dashboard for anything unusual\" -> `monitor` on the dashboard\n\n### Step 3: Write Instructions\n\nInstructions tell the task what to focus on. Be specific about:\n\n- What patterns to look for\n- What thresholds matter\n- When to create a discovery\n\nExample:\n\n```\nMonitor weekly revenue trends. Create a discovery if:\n- Revenue drops more than 10% week-over-week\n- Revenue exceeds forecast by 20%\n- Unusual patterns in regional breakdown\n```\n\n### Step 4: Create the Task\n\nUse the Altertable MCP task-creation tool. Supply:\n\n- the task type -- one of `anomaly_detection`, `forecast`, or `monitor`\n- the target Insight or Dashboard slug the AI will analyze\n- a cron schedule (standard 5-field, UTC)\n- the natural-language instructions -- the prompt the AI follows on each run\n- the author (the user creating the task)\n\nRefer to the MCP tool description for the exact parameter names and any additional required fields -- the MCP schema is the source of truth.\n\n## Common Pitfalls\n\n- **Wrong task type** -- `anomaly_detection` detects outliers; `forecast` projects future values; `monitor` does open-ended analysis. Don't mix them up\n- **Vague instructions** -- \"watch this Insight\" produces noisy discoveries; be specific about thresholds and patterns\n- **Creating duplicate tasks** -- check if a task already exists on the target before creating a new one\n- **Missing the target** -- the user needs an existing Insight or Dashboard slug; help them create one first if needed\n- **Using `monitor` when `anomaly_detection` suffices** -- `monitor` is more general but less focused; prefer `anomaly_detection` for pure outlier detection\n\n## Reference Files\n\n- [Task types](references/task-types.md) - Read when choosing between anomaly_detection, forecast, and 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