Monte Carlo Instrument Agent

Monte Carlo Instrument-Agent Skill

This skill walks an MC Agent Observability customer through instrumenting a new AI agent in their Python codebase: detect AI libraries → install the Monte Carlo OpenTelemetry SDK + matching instrumentors → generate mc.setup() (with SimpleSpanProcessor when serverless) → propose @trace_with_workflow / @trace_with_task decorator diffs → confirm env vars (only when needed) → verify traces flow via get_agent_metadata.

The skill produces traces. It is not for monitoring or alerting on existing traces — that's monte-carlo-monitoring-advisor. The two skills are sequential: instrument-agent first, monitoring-advisor afterward.

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 files live next to this file. Use the Read tool (not MCP resources) to access them.

CRITICAL — Never modify any file without explicit user approval

This skill must not modify any file in the customer's codebase without explicit per-file user approval. This rule covers:

• Dependency files — requirements.txt, pyproject.toml, Pipfile, lockfiles. Always propose the diff and wait for confirmation before editing.
• Source code — mc.setup() insertion, decorator placement (@trace_with_workflow, @trace_with_task), import additions. Always propose the diff and wait for confirmation per file.
• Env files — .env, .envrc, shell rc files. Always propose the change and wait for confirmation before editing.

The skill walks the user through what needs to change and why, then proposes diffs. It does not apply edits, run pip install, or write env files autonomously. The only exception: the user may explicitly waive approval for a specific file ("I know the risks, just edit the file") — proceed for that file only and surface that the approval was waived.

This guardrail is reinforced in the Tier-3 references (references/decorator-placement.md, references/setup-template.md, references/library-detection.md).

When to activate this skill

Activate when the user expresses intent to instrument a new AI agent:

• Asks to instrument an agent for Monte Carlo, set up MC tracing, or wire up the Monte Carlo OpenTelemetry SDK
• Asks how to add Monte Carlo tracing to a LangChain / LangGraph / OpenAI / Anthropic / CrewAI / Bedrock / SageMaker / Vertex AI agent (those are examples — the full supported set is whatever the Monte Carlo OpenTelemetry SDK ships on PyPI: https://pypi.org/project/montecarlo-opentelemetry/)
• Says things like "instrument my agent for Monte Carlo", "set up Monte Carlo tracing", "set up MC tracing", "set up agent tracing for Monte Carlo", "set up Monte Carlo on my new agent"
• References the SDK install or mc.setup() (when generating; not when diagnosing)

When NOT to activate this skill

Do not activate when the user is:

• Asking to monitor an existing agent (latency, token usage, evaluation, trajectory, validation) → monte-carlo-monitoring-advisor
• Investigating an active agent incident or alert → monte-carlo-incident-response / monte-carlo-troubleshoot-agent-traces
• Asking about pushing metadata or query logs to Monte Carlo (data ingestion, not agent tracing) → push-ingestion
• Building a Connection Auth Rules config → connection-auth-rules
• Asking why traces are missing for an already-instrumented agent → that's troubleshooting; this skill covers it via references/troubleshooting.md, but the first invocation should be deliberate (not a coverage question)

If the user is ambiguous ("set up agent observability"), surface both options and ask whether they're instrumenting a new agent (this skill) or configuring monitors on an existing one (monitoring-advisor).

Pre-flight check

Before walking the workflow, confirm two things:

1. Monte Carlo MCP server is configured + authenticated. Run test_connection. If it succeeds, Step 4 (BEFORE snapshot) and Step 10 (AFTER verification) will use get_agent_metadata directly. If test_connection fails, degrade gracefully — point the user at the MC MCP setup docs (https://docs.getmontecarlo.com/docs/mcp-server) as informational, then continue the workflow and tell them they'll need to verify the new agent appears in the Monte Carlo UI manually after running the instrumented agent. Record whether MCP is available so Steps 4 and 10 know which path to take.
2. Python codebase is present. Look for requirements.txt, pyproject.toml, or Pipfile in the working directory. If none exist, ask the user where the agent codebase is.

Reference files — when to load

The skill is structured as a Tier 1 router (this file) → Tier 2 workflow → Tier 3 per-step references. Load each reference when its step is reached in the workflow.

High-level workflow (Tier 1 summary)

The full step-by-step flow lives in references/workflow.md. At a glance:

1. Detect AI libraries, runtime style, and any existing mc.setup() via scripts/detect_libraries.py.
2. Ask whether the customer hosts their own OTel collector or uses the MC-hosted one — gates the env-var step.
3. Ask whether the customer has stricter privacy requirements that warrant redacting prompts or completions — full capture is the default; redaction is opt-in.
4. Snapshot existing agents via get_agent_metadata (BEFORE any code changes).
5. Resolve and display the final OTLP endpoint to the user — normalize idempotently (never double-append /v1/traces).
6. Propose dependency-file edits and wait for approval — install SDK + instrumentors at compatible versions (live-fetched from PyPI; fail closed and ask the user to consult https://pypi.org/project/montecarlo-opentelemetry/ if the fetch fails).
7. Propose mc.setup() insertion as a diff and wait for approval — serverless variant uses SimpleSpanProcessor.
8. Propose @trace_with_workflow / @trace_with_task decorator diffs — wait for approval per file. Those are the only two decorators in scope for v1.
9. Confirm auth env vars (only on the MC-hosted collector path) — either MCD_DEFAULT_API_ID / MCD_DEFAULT_API_TOKEN or OTEL_EXPORTER_OTLP_HEADERS, depending on the setup template. Presence-only check; never read or echo the values.
10. Verify via get_agent_metadata (AFTER user runs the instrumented agent) — confirm new agent_name + new MCON appears.
11. On failure, branch to references/troubleshooting.md.

Each step's full Tier 3 details live in the reference files above.

Helper scripts

The skill ships two Python helpers under scripts/ that the workflow invokes:

Version constraints for instrumentor packages come from PyPI live (fetch_sdk_docs.py). Transitive constraints PyPI doesn't expose (e.g. wrapt<2 for OpenLLMetry instrumentors at <=0.53.4) are documented as symptom-driven fixes in references/troubleshooting.md — the skill surfaces them when the customer hits the symptom rather than baking them into every install diff.

Out of scope (v1)

• Auto-scaffolded create_llm_span boilerplate for libraries without a dedicated instrumentor.
• Auto-instrumented redaction (proactive sensitive-data detection and wrapping). The skill is conversant in redaction — when the customer has stricter privacy requirements, it walks them through the ordered redaction layers in references/redaction.md.
• Full first-time AO setup (infra deployment, datastore registration, warehouse ingestion).
• API-key generation.
• Non-Python SDKs.
• Decorators other than @trace_with_workflow and @trace_with_task. Other tracing primitives the SDK exposes are not part of the v1 surface.

Available slash commands