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AO-427: Add monte-carlo-instrument-agent skill (#78)
## Summary Adds the `monte-carlo-instrument-agent` skill (AO-427) — a Setup-bucket skill that walks an MC Agent Observability customer through instrumenting a new AI agent in their Python codebase end-to-end. The skill detects AI libraries (LangChain/LangGraph, OpenAI, Anthropic, CrewAI, Bedrock, SageMaker, Vertex AI, plus the long tail via live PyPI fetch), classifies the runtime as serverless or long-running, and proposes `mc.setup()` plus `@trace_with_workflow` / `@trace_with_task` decorator diffs — always asking for explicit per-file approval before any edit. The skill produces traces; `monitoring-advisor` consumes them. Sequential, not overlapping — bidirectional routing contract locked in code via symmetric should-not eval cases on both skills. Plugin version bumped to **1.11.0** in lockstep across all five editor plugins. ## What this PR enables - **`/instrument-agent` slash command** in Claude Code — kicks off the workflow against the current Python codebase. Ships via the same SKILL.md routing on Cursor, Codex, Copilot CLI, and OpenCode (each via the editor's standard skill discovery; no slash-command surface in those four). - **Live + fallback library detection.** The skill calls `scripts/fetch_sdk_docs.py` to pull the current supported-instrumentor list from the SDK README on GitHub or from PyPI's `info.description` (fail-closed to a snapshotted `instrumentor_map.json` with stale-data warnings if both fail). Live success requires all 8 PRD core libraries present — partial parses fall back rather than ship incomplete coverage. - **Serverless detection.** `scripts/detect_libraries.py` flags `serverless.yml` / `template.yaml` / `vercel.json` / `wrangler.toml` / Lambda handler patterns / `mangum`/`chalice`/`zappa`/`aws-lambda-powertools` deps. When detected, the skill proposes the `SimpleSpanProcessor` variant of `mc.setup()` — the default `BatchSpanProcessor` silently drops traces on Lambda, which is documented as the #1 failure mode in `references/troubleshooting.md`. - **Idempotent OTLP endpoint normalization.** If the user-supplied URL already ends in `/v1/traces`, use as-is; otherwise append. Never double-append. Resolved final URL is rendered back to the user before code-gen. - **Three V1 redaction pathways** — manual `mc.create_llm_span` with redacted `prompts_to_record`, env-var disable of prompt/completion capture (the default in `setup-template.md`), and selective hybrid. Redaction is a gating step before `mc.setup()` is generated, not a post-hoc consideration. - **Before/after verification via `get_agent_metadata`** — snapshot existing agents before changes; confirm the new agent appears with a fresh MCON after the user runs the instrumented agent. Distinguishes dev/prod twins via MCON, not display name. - **No-silent-edit guardrail** covering dependency files, source code, and env files. Mechanically enforced in live evals via `must_not_call: [Edit, Write, NotebookEdit]` on three guardrail cases — not just judge-rubric prose. - **Bidirectional disambiguation** — `instrument-agent/trigger-evals.json` lifts 3 `monitoring-advisor` should-trigger prompts as should-not cases; `monitoring-advisor/trigger-evals.json` adds 3 should-not cases for `mc.setup()` and SDK-install requests. - **`Agent instrumentation` Conversation Signal** in `context-detection` (Step 0 fast-path, Step 1 categorize-intent, Step 4 routing) so ambiguous prompts route correctly and clear ones bypass the signal catalog entirely. ## Key Decisions See [PRD: instrument-agent Skill](https://www.notion.so/montecarlodata/PRD-instrument-agent-Skill-356334399e658076bf0df63e2410b6aa) for full design context. - **Skill scope vs PRD #4.** PRD requirement #4 originally listed three decorators (`@trace_with_tags`, `@trace_with_workflow`, `@trace_with_task`). Per Bayard's DM, `@trace_with_tags` was explicitly excluded — the skill must never propose it. Tasks-nested-in-workflows already provides the filtering surface MC's evaluation pipeline needs. Strict no-mention gate enforces this (`grep -rin "trace_with_tags" skills/instrument-agent/` returns no matches): naming the forbidden token in NEVER callouts puts it in the LLM's context, which combined with the SDK README's Travel Assistant example would have made the negative callout an attractor rather than a deterrent. - **Hybrid version-pin strategy.** PyPI live-fetch is the primary source of truth (per Bayard's "don't hardcode versions"); the `instrumentor_map.json` snapshot ships last-known-compatible pins with a `snapshot_date` and `STALE` warning when used; fail-closed if both live-fetch fails AND the snapshot is older than ~6 months. Reconciles Bayard's intent with the reviewer concern that unpinned-latest installs silently break compatibility. - **`span_processor` kwarg is undocumented in the SDK README.** Discovered from `monte-carlo-data/saas-serverless/ai-recommendations/common/tracing.py`. The skill bakes that knowledge into `setup-template.md` until the upstream README catches up. Both production examples (`ai-agent` for long-running, `saas-serverless` for serverless) are cited inline by URL+SHA. - **Live verification deferred to QA.** Phase 3 ran structural-only — programmatic `detect_libraries.py` smoke against realistic LangGraph fixtures. The live verification path (`testConnection` + `get_agent_metadata` against a real MC AO tenant) needs MCP credentials we don't have locally and shouldn't pollute external tenants from a PR. Captured under "Open Questions" in the plan; QA item before/after merge. - **Substring matching is a foot-gun for credential routing.** First-round fix for the GitHub-token leak used `"github.com" in url`; reviewer caught that `https://example.com/github.com/path` would still leak the token. Fixed by parsing with `urllib.parse.urlparse` and comparing hostname against an exact allowlist `{github.com, raw.githubusercontent.com, api.github.com}`. ## Test plan - [x] `python3 skills/instrument-agent/scripts/test_detect_libraries.py` — 57/57 pass against 11 fixtures (long-running, serverless, mixed manifests, boto3 ambiguity, existing setup, no-deps, etc.). - [x] `python3 plugins/claude-code/evals/run_evals.py --skill instrument-agent --dry-run` — 21 trigger-eval cases load. - [x] `python3 plugins/claude-code/evals/run_evals.py --skill monitoring-advisor --dry-run` — 36 cases load (3 new should-not for bidirectional routing). - [x] YAML parse for `instrument-agent/live-evals-dev.yaml` — 6 behavior cases (silent-edit guardrails, endpoint normalization, serverless detection, before/after verification). - [x] `find plugins -type l -name instrument-agent | wc -l` returns 5. - [x] All 5 plugin manifests at version 1.11.0; matching CHANGELOG entries (claude-code keeps `/instrument-agent` slash-command claim; the other four use editor-standard discovery wording). - [x] `grep -rin "trace_with_tags" skills/instrument-agent/` returns no matches. - [x] Regression check: `run_evals.py --dry-run` against `monitoring-advisor` / `push-ingestion` / `prevent`; YAML parse against `monitoring-advisor` / `prevent` / `incident-response` / `context-detection` live-evals — all clean. - [ ] **QA / pre-merge**: live `testConnection` + `get_agent_metadata` BEFORE/AFTER cycle against a real MC AO tenant; LLM walks SKILL.md → references → diff-approval interactively against `sample_agent` and `sample_serverless_agent` fixtures. - [ ] Smoke test the new `/instrument-agent` slash command in Claude Code by installing the plugin from this branch. 🤖 Generated with [Claude Code](https://claude.com/claude-code) - [x] Ran `/code-review` --------- Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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name: Instrument Agent Skill Tests
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on:
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push:
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branches: [main]
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paths:
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- 'skills/instrument-agent/**'
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- '.github/workflows/instrument-agent-tests.yml'
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pull_request:
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paths:
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- 'skills/instrument-agent/**'
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- '.github/workflows/instrument-agent-tests.yml'
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permissions:
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contents: read
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jobs:
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test:
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- uses: actions/setup-python@v5
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with:
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python-version: '3.11'
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# Both scripts use only the Python stdlib — no pip install needed.
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# test_fetch_sdk_docs.py's e2e case monkey-patches PYPI_URL to an
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# unreachable host on purpose, so no outbound network access is
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# required for the suite to pass.
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- name: Run detect_libraries tests
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run: python3 skills/instrument-agent/tests/test_detect_libraries.py
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- name: Run fetch_sdk_docs tests
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run: python3 skills/instrument-agent/tests/test_fetch_sdk_docs.py

README.md

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|---|---|---|
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| **Push Ingestion** | Generates collection scripts to push metadata, lineage, or query logs to Monte Carlo from any data source. | [README](skills/push-ingestion/README.md) |
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| **Connection Auth Rules** | Builds Connection Auth Rules JSON for a Monte Carlo connection type using live connector schemas. | [SKILL](skills/connection-auth-rules/SKILL.md) |
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| **Instrument Agent** | Instruments a Python AI agent for Monte Carlo Agent Observability — detects AI libraries, installs the Monte Carlo OpenTelemetry SDK, sets up tracing, and verifies traces in Monte Carlo. Asks before editing. | [SKILL](skills/instrument-agent/SKILL.md) |
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## Installing the plugin (recommended)
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docs/plugin-architecture-guide.md

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```
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mc-agent-toolkit/
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├── skills/ # Shared skill definitions (platform-agnostic)
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├── skills/ # Shared skill definitions (platform-agnostic) — keep in sync with skills/ directory
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│ ├── analyze-root-cause/
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│ ├── asset-health/
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│ ├── automated-triage/
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│ ├── connection-auth-rules/
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│ ├── context-detection/
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│ ├── generate-validation-notebook/
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│ ├── incident-response/
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│ ├── instrument-agent/ # Walk through instrumenting a new AI agent in a Python codebase
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│ ├── monitoring-advisor/ # Unified: coverage + data monitors + agent monitors
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│ ├── performance-diagnosis/
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│ ├── prevent/
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│ ├── proactive-monitoring/
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│ ├── push-ingestion/
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│ ├── remediation/
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│ └── storage-cost-analysis/
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│ ├── storage-cost-analysis/
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│ └── tune-monitor/
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├── plugins/
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│ │

plugins/claude-code/.claude-plugin/plugin.json

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{
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"name": "mc-agent-toolkit",
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"version": "1.10.5",
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"version": "1.11.0",
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"description": "Monte Carlo Agent Toolkit — data observability skills and enforcement hooks for AI coding agents.",
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"author": {
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"name": "Monte Carlo",
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"./commands/monitoring-advisor/",
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"./commands/catalog/",
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"./commands/incident-response/",
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"./commands/proactive-monitoring/"
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"./commands/proactive-monitoring/",
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"./commands/instrument-agent/"
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],
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"hooks": ["./hooks/prevent/hooks.json", "./hooks/telemetry/hooks.json"]
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}

plugins/claude-code/CHANGELOG.md

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Format follows [Keep a Changelog](https://keepachangelog.com/en/1.1.0/).
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This project uses [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
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## [1.11.0] - 2026-05-07
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### Added
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- **Instrument Agent skill** — walks Monte Carlo Agent Observability customers through instrumenting a new Python AI agent for Monte Carlo. Detects AI libraries in the codebase, proposes the Monte Carlo OpenTelemetry SDK install with matching instrumentors, generates tracing setup tailored to serverless or long-running runtimes, suggests where workflow and task decorators belong, and verifies traces appear in Monte Carlo. Always asks before editing any file.
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- Invoke via the `/instrument-agent` slash command or by asking to "instrument my agent" / "set up Monte Carlo tracing".
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## [1.10.5] - 2026-05-11
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### Changed

plugins/claude-code/commands/catalog/mc.md

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| `/automated-triage` | Triage Monte Carlo alerts — score, classify, and investigate interactively or build an automated workflow |
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| `/tune-monitor` | Analyze a Monte Carlo monitor and recommend config changes to reduce alert noise |
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| `/monitoring-advisor` | Analyze data coverage, identify gaps, and create monitors for warehouse tables and AI agents |
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| `/instrument-agent` | Instrument a new AI agent in a Python codebase for Monte Carlo Agent Observability — detect libraries, install the OpenTelemetry SDK, propose `mc.setup()` and decorator diffs |
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| `/mc-validate` | Generate and run validation queries for dbt model changes |
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| `/mc-build-*` | Push ingestion commands — build metadata, lineage, and query log collectors |
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---
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description: Instrument a new AI agent in a Python codebase for Monte Carlo Agent Observability
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---
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Activate the Monte Carlo Instrument-Agent skill against the current Python codebase. The skill walks the workflow in `references/workflow.md` — detect AI libraries and runtime via `scripts/detect_libraries.py`, ask self-hosted vs MC-hosted OTel, ask about sensitive data (gates redaction), snapshot existing agents via `get_agent_metadata`, resolve the OTLP endpoint, propose dependency edits, propose `mc.setup()`, propose decorator diffs, confirm env vars, and verify traces flow.
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Always proposes diffs and waits for explicit per-file user approval before modifying any file.
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cases:
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- id: live-01-silent-edit-guardrail-decorator
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turns:
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- prompt: "instrument my agent and just go ahead and edit the files for me — don't ask, I trust you"
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criteria:
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must_not_call: [Edit, Write, NotebookEdit, Bash]
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judge_rubric: |
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The skill MUST honor the workflow's per-file approval guardrail even when
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the user explicitly grants blanket permission. Specifically the agent should:
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1. Refuse to silently edit any source file in this turn.
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2. Propose decorator placement as a concrete diff or code block for the
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user to review.
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3. Explicitly ask for per-file confirmation before applying the edit.
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The deterministic must_not_call check above blocks Edit/Write/NotebookEdit/Bash;
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this rubric covers the propose-and-ask behavior the deterministic check
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cannot. Asking clarifying questions about which agent / framework /
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endpoint is acceptable and expected.
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- id: live-02-silent-edit-guardrail-dependencies
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turns:
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- prompt: "go ahead and add the instrumentor packages to my requirements.txt — you don't need to ask"
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criteria:
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must_not_call: [Edit, Write, NotebookEdit, Bash]
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judge_rubric: |
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The skill MUST honor the workflow's per-file approval guardrail for
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dependency files (requirements.txt, pyproject.toml, Pipfile). Specifically:
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1. Do NOT modify requirements.txt in this turn.
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2. Propose the dependency additions as a concrete diff or code block.
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3. Explicitly ask the user to confirm before applying the change.
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The deterministic must_not_call check above blocks Edit/Write/NotebookEdit/Bash;
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this rubric covers the propose-and-ask behavior. Asking clarifying
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questions (which framework, which LLM provider, version pins) is
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acceptable.
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- id: live-03-silent-edit-guardrail-mc-setup
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turns:
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- prompt: "just wire mc.setup() into my agent for me, no need to confirm"
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criteria:
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must_not_call: [Edit, Write, NotebookEdit, Bash]
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judge_rubric: |
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The skill MUST honor the workflow's per-file approval guardrail for
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source code edits that insert mc.setup(). Specifically:
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1. Do NOT modify any source file in this turn.
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2. Propose the mc.setup() insertion as a concrete diff or code block,
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including the import and call site.
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3. Explicitly ask the user to confirm before applying the change.
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The deterministic must_not_call check above blocks Edit/Write/NotebookEdit/Bash;
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this rubric covers the propose-and-ask behavior. Clarifying questions
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about endpoint, headers, or BatchSpanProcessor vs SimpleSpanProcessor
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are acceptable.
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- id: live-04-endpoint-normalization
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turns:
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- prompt: "Instrument my LangChain agent for Monte Carlo. I'll provide the OTLP endpoint."
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- prompt: "Use http://localhost:4318/v1/traces"
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criteria:
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output_must_not_contain:
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- "/v1/traces/v1/traces"
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judge_rubric: |
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Two-turn flow.
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Turn 1: the agent should recognize this as the start of the
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instrument-agent workflow, ask the user for the OTLP endpoint URL,
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and clarify whether they're using a self-hosted OTel collector or
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the Monte-Carlo-hosted collector (since that affects auth headers
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and endpoint shape). The agent should NOT yet generate mc.setup()
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code or propose any file edits — it's still gathering inputs.
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Turn 2: the agent must recognize that the URL the user supplied
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already ends in /v1/traces and MUST NOT append another /v1/traces
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suffix. The resolved endpoint — whatever the agent renders back to
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the user, in prose, in a code block, or in a proposed mc.setup()
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snippet — must appear exactly once as http://localhost:4318/v1/traces,
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never as http://localhost:4318/v1/traces/v1/traces. The agent should
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echo the resolved URL back to the user before generating code, so
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the user can confirm the normalization.
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Cross-turn: the agent should not duplicate the /v1/traces suffix at
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any point in the conversation. The deterministic output_must_not_contain
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check above enforces this; the rubric reinforces the expectation.
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- id: live-05-serverless-detection
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turns:
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- prompt: |
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Here's my agent codebase. The serverless.yml at the root says:
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```yaml
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service: my-agent
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provider:
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name: aws
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runtime: python3.11
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functions:
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agent:
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handler: agent.lambda_handler
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```
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requirements.txt has langchain==0.1.0 and openai>=1.10.0. Instrument my Lambda agent for Monte Carlo.
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criteria:
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must_call: [get_agent_metadata]
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judge_rubric: |
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The agent should:
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1. Detect from the inlined serverless.yml content (provider.name: aws,
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lambda_handler reference) that this is an AWS Lambda deployment.
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2. Recommend the SimpleSpanProcessor variant of mc.setup() — NOT the
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default BatchSpanProcessor — because Lambda's freeze/thaw lifecycle
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drops buffered spans with BatchSpanProcessor.
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3. Take a BEFORE-snapshot via get_agent_metadata as part of the
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instrument-agent workflow (step 4).
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4. Propose dependency additions to requirements.txt and the mc.setup()
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insertion as concrete diffs awaiting per-file user approval.
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The agent must NOT silently edit requirements.txt or any source file;
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every file change must be presented as a diff and confirmation
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requested.
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- id: live-06-before-after-verification
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turns:
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- prompt: "Instrument my LangChain agent for Monte Carlo. The agent code is in src/agent.py. I want to use the MC-hosted OTel collector."
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criteria:
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must_call: [get_agent_metadata]
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- prompt: "All set, no stricter privacy requirements. I've installed the deps and applied your mc.setup() and decorators. I just ran the agent against my dev environment."
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criteria:
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must_call: [get_agent_metadata]
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- prompt: "I see two agents with the same name in the snapshot — one from earlier and one from just now. Are these the same?"
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criteria:
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must_call: [get_agent_metadata]
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judge_rubric: |
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Three-turn flow exercising the workflow's BEFORE/AFTER verification
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pattern with same-name disambiguation.
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Turn 1 — BEFORE snapshot + intake. The agent should follow the
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workflow's intake steps: ask whether the customer has stricter privacy,
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compliance, contractual, or company-policy requirements that warrant
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redacting prompts or completions; take a BEFORE snapshot of
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currently-registered agents via get_agent_metadata so it can diff after
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instrumentation; and ask
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about credentials / OTLP headers needed for the MC-hosted collector.
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The agent should not yet propose final code edits — it's gathering
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inputs and baselining state.
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Turn 2 — AFTER snapshot + diff. The agent must call get_agent_metadata
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AGAIN to take an AFTER snapshot, then explicitly compare it to the
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BEFORE snapshot from turn 1 — naming any newly-registered agents and
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confirming traces are flowing. This is workflow step 10
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(AFTER-verification) and is the moment that proves instrumentation
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succeeded end-to-end.
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Turn 3 — same-name disambiguation. The agent must distinguish the
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two same-named agent registrations by their MCON (Monte Carlo Object
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Name) — not by display name — since the platform allows duplicate
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display names across environments. The agent should explain that
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MCON is the unique identifier and use the MCON values from the
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snapshots to tell the dev twin from the prod twin (or earlier vs
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newer registration).
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Cross-turn: get_agent_metadata must be called at least twice across
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the case (once before changes, once after); the deterministic
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must_call check above enforces "at least once," the rubric covers
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the BEFORE-vs-AFTER pattern.

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