| name | monte-carlo-context-detection |
| description | Route data-related requests to the right Monte Carlo skill or workflow. USE WHEN alerts, incidents, data broken, stale, coverage gaps, data quality, or any ambiguous data observability request. |
| when_to_use | Invoke for ambiguous or incomplete data-observability requests that don't clearly name a specific skill.
Example triggers: "something is wrong with my data", "I have alerts firing", "check my pipelines", "what should I monitor?", "my data looks off".
CRITICAL on vague first turns: ALWAYS ask 1–3 targeted clarifying questions FIRST (what symptom? which table/warehouse? when did it start?). Do NOT call Monte Carlo MCP tools (get_alerts, search, get_table, etc.) on turn 1 until the user has named a specific table, warehouse, or alert. Calling tools prematurely on a vague prompt wastes turns and frustrates the user.
Do NOT invoke when the user's intent clearly matches a single existing skill (e.g. "check health of orders table" → asset-health; "create a volume monitor on X" → monitoring-advisor).
|
| bucket | Agent-routing |
| version | 1.0.0 |
Monte Carlo Context Detection
This skill determines which Monte Carlo skill or workflow best fits the user's current context. It activates reactively for ambiguous or multi-step data-related messages, gathers signals, and routes to the right skill or workflow.
Monte Carlo tool routing (required): Always call Monte Carlo MCP tools through this plugin's
bundled server, whose fully-qualified tool names are
mcp__plugin_mc-agent-toolkit_monte-carlo-mcp__<tool> (e.g.
mcp__plugin_mc-agent-toolkit_monte-carlo-mcp__get_alerts). Bare tool names used in this skill
(get_alerts, search, get_table, …) refer to that bundled server. If the session also has a
separately-configured monte-carlo-mcp server, do not route to it — it may point at a
different endpoint or credentials.
Reference file for signal definitions: references/signal-definitions.md (relative to this file). Read it before routing.
When to activate this skill
This skill is activated by the CLAUDE.md routing table when:
- The user's message relates to data quality, alerts, incidents, coverage, or Monte Carlo — but doesn't clearly match a single skill in the routing table
- The user's intent is ambiguous or could span multiple skills
- The user asks a broad question like "help me with my data" or "what's going on?"
When NOT to activate this skill
- A skill or workflow is already active in the conversation — the active skill owns the conversation, do not intercept
- The user's message clearly matches a single skill in the CLAUDE.md routing table — route directly, no need for context detection
- The user is editing a dbt model — defer to the
prevent skill which auto-activates via hooks
- The user's message is not data-related at all
Workflow: Reactive Routing
This skill is purely reactive — it activates for ambiguous or multi-step data-related messages and routes them.
Follow these steps in order.
Step 0: Fast-path clear intent (stop early if matched)
Before doing anything else, check whether the user's message unambiguously matches a single existing skill. If so, skip the rest of this workflow and immediately load that skill — do NOT read references/signal-definitions.md, do NOT make API probes.
| Clear user intent | Skill to load immediately |
|---|
| "Check health of [named table]" / "status of [named table]" | ../asset-health/SKILL.md |
| "Create a [monitor type] on [named table]" | ../monitoring-advisor/SKILL.md |
| "Investigate alert on [named table]" / "why is [named table] stale/broken?" | ../incident-response/SKILL.md |
| "What should I monitor?" / "where are my coverage gaps?" | ../proactive-monitoring/SKILL.md |
| "Instrument my agent" / "set up Monte Carlo tracing on [named framework] agent" / "setting up an agent" | ../instrument-agent/SKILL.md |
Context-detection is for ambiguous requests only. If the request is clear, routing through this skill wastes turns and tokens.
If no clear match, proceed to Step 1.
Step 1: Categorize intent
Read references/signal-definitions.md for the full signal catalog. Determine which category the user's message falls into:
| Category | Signals | Example messages |
|---|
| Specific asset | User mentions a table name, or has a .sql model file open in their IDE | "what's wrong with stg_payments?", "check this table" |
| Active incident | Keywords: alert, broken, stale, failing, incident, triage, wrong data | "I have alerts firing", "data looks wrong", "something broke" |
| Coverage/monitoring | Keywords: monitor, coverage, gaps, unmonitored, what should I watch | "what should I monitor?", "where are my gaps?" |
| Agent instrumentation | Keywords: instrument, set up tracing, set up Monte Carlo tracing, setting up an agent. Often mentions an AI framework (LangChain, LangGraph, OpenAI, Anthropic, CrewAI, Bedrock, SageMaker, Vertex AI) | "instrument my agent", "set up MC tracing on my LangGraph agent", "setting up an agent" |
| General/exploratory | No clear category, broad question | "help me with data quality", "what can Monte Carlo do?" |
Step 2: Gather scope (only if needed)
- Specific asset known (from file context or user mention) → proceed to Step 3
- Active incident, no scope → ask: "Want me to check recent alerts? Any specific time range or severity?"
- Coverage/monitoring, no scope → ask: "Which warehouse should I look at, or should I check across all?"
- General/exploratory → present the categories: "I can help with: (1) investigating active alerts or data issues, (2) analyzing monitoring coverage and creating monitors, or (3) checking the health of specific tables. What are you looking for?"
Step 3: Scoped API probe (when scope is available)
Only make API calls when you have enough context to scope them:
- Specific asset → call
get_alerts with the table's MCON or name filter, and get_monitors for that table
- Active incident with scope → call
get_alerts with the user's time range / severity filters
- Coverage/monitoring → skip API probe, route directly to proactive monitoring workflow (it handles its own API calls)
- If MCP tool calls fail (auth not configured) → skip API, fall back to conversation intent alone
Always scope MCP calls tightly. Unscoped get_alerts, search, or get_monitors on large accounts can return hundreds of results, overflow the tool-result token limit, spill to disk, and force expensive chunk reads — burning user tokens and risking workflow failure. Minimum scoping:
get_alerts → time filter (created_after, default last 7 days) + at least one of warehouse, table_names, severitysearch → needed to resolve a table name to its MCON (get_table requires MCON). ALWAYS pass limit (e.g. 5), the table name as query, and filter by warehouse_uuid or database/schema. warehouse_types alone ("snowflake") matches thousands of tables. Disambiguation rules when multiple matches return:
- If the user named a warehouse (e.g. "analytics-snowflake") → auto-pick the match whose
warehouse_display_name matches and proceed. Do NOT stop to ask.
- If the user named a database/schema → auto-pick the match in that database/schema.
- If one match is flagged
is_key_asset: true and others aren't → auto-pick the key asset.
- Only ask the user to disambiguate when none of the above resolve it.
get_monitors → always filter by mcons (table MCON) or warehouse_uuid
If you don't have enough scope, ask the user before calling.
Step 4: Route
Based on the combined signals from Steps 1-3:
| Combined signals | Confidence | Action |
|---|
| Active alerts found + incident intent | High | Auto-activate incident response workflow: read and follow ../incident-response/SKILL.md |
| Coverage intent + data project detected | High | Auto-activate proactive monitoring workflow: read and follow ../proactive-monitoring/SKILL.md |
| User asks to create a specific monitor (type + table known) | High | Auto-activate monitoring-advisor: read and follow ../monitoring-advisor/SKILL.md |
| Table mentioned + "health" / "status" / "check" intent | High | Auto-activate asset-health: read and follow ../asset-health/SKILL.md |
| Agent instrumentation intent (instrument / set up tracing / setting up an agent) + Python codebase context | High | Auto-activate instrument-agent: read and follow ../instrument-agent/SKILL.md |
| Ambiguous or conflicting signals | Low | Suggest options and wait for user to choose |
High confidence = auto-activate. Load the target skill's SKILL.md and begin executing it immediately. Do not ask for confirmation.
Low confidence = suggest. Present 2-3 options with brief descriptions and let the user choose. Example:
"Based on what you've described, I can:
- Investigate alerts — triage and fix active data issues (incident response workflow)
- Improve monitoring — find coverage gaps and create monitors (proactive monitoring workflow)
Which would be most helpful?"
Prevent guardrail
If the user is actively editing a dbt model file (making code changes, not just viewing or asking about it) and the prevent skill's hooks are active, do NOT route to any other skill. Instead respond:
"The prevent skill will automatically handle impact assessment for dbt model changes via its pre-edit hooks. No additional routing needed."
