level-up

name	level-up
user-prompt	Take my agent to the next level
description	Take your AI agent to the next level with full LangWatch integration. Adds tracing, prompt versioning, evaluation experiments, and simulation tests in one go. Use when the user wants comprehensive observability, testing, and prompt management for their agent.
license	MIT
compatibility	Works with Claude Code and similar coding agents. The `langwatch` CLI is the only interface.

Take Your Agent to the Next Level

This skill sets up your agent with the full LangWatch stack: tracing, prompt versioning, evaluation experiments, and agent simulation tests. Each step builds on the previous one.

Plan Limits

LangWatch's free plan has limits on prompts, scenarios, evaluators, experiments, and datasets. When you hit a limit, the API returns "Free plan limit of N reached..." with an upgrade link.

How to handle:

• Work within the limits. If 3 resources of the relevant type are allowed, create 3 meaningful ones, not 10.
• Make every creation count: each one should demonstrate clear value.
• Show what works FIRST. If you hit a limit, summarize what was accomplished and note that upgrading the plan raises it. Point to the subscription settings on the platform, or to the license settings if the CLI is pointed at a self-hosted endpoint. Read the endpoint the CLI actually uses, which can come from .env, from the process environment, or from the saved CLI configuration.
• Do NOT delete existing resources to make room or repurpose an existing resource to evade the limit.

Prerequisites

Use langwatch docs <path> to read documentation as Markdown. Some useful entry points:

langwatch docs                                    # Docs index
langwatch docs integration/python/guide           # Python integration
langwatch docs integration/typescript/guide       # TypeScript integration
langwatch docs prompt-management/cli              # Prompts CLI
langwatch scenario-docs                           # Scenario docs index

Discover commands with langwatch --help and langwatch <subcommand> --help. List and get commands accept --format json for machine-readable output. Every list command takes --limit <n> to cap the rows and --jq <expr> to read part of the answer. A paginated list answers with an envelope, so count its rows through the row array (--jq '.traces | length'), and read how many there are in all at .pagination.total. Bare --jq length counts the fields of the envelope, not the rows. Read the docs first instead of guessing SDK APIs or CLI flags.

If no shell is available, fetch the same Markdown over plain HTTP. Append .md to any docs path (e.g. https://langwatch.ai/docs/integration/python/guide.md). Index: https://langwatch.ai/docs/llms.txt. Scenario index: https://langwatch.ai/scenario/llms.txt

If anything fails or confuses you while following this skill (broken commands, docs that do not match reality, errors you had to work around), ask the user for permission and run npx --yes langwatch report --user-approved with a --title and --summary (or --session <transcript.jsonl>) to send it to the LangWatch team, and it directly shapes what gets fixed. No login or API key needed. Nothing is sent without --user-approved, and --dry-run prints the exact payload without sending anything. The title, summary and transcript are scrubbed locally first, by pattern: secrets and API keys, plus email addresses, phone numbers, card numbers and public IPv4 addresses. Anything no pattern matches is sent as written, including a contact address passed with --email. With --session, always run --dry-run first and let the user read the payload, because a transcript carries content they never reviewed. npx --yes langwatch report --help explains the options.

Projects and API keys: target a real project, not a personal one.

LangWatch has two kinds of project:

• Team / shared projects: real projects inside an organization. Evaluations, experiments, prompts, datasets, simulations and instrumentation must always target one of these.
• Personal projects: a private "My Workspace" scratch space tied to a single user. Never send a user's evaluations, experiments or production traces here: it is for personal exploration only, and you can mistake it for a real project.

And two ways to authenticate:

• A project API key in .env (LANGWATCH_API_KEY): the credential everything in these skills uses. It is scoped to one real project. This is the default; prefer it unless the user explicitly asks for something else.
• langwatch login --device (AI-tools / SSO): a personal device session for wrapping coding assistants (langwatch claude, langwatch codex, …). It is NOT for evaluations, prompts, datasets, scenarios or SDK instrumentation, and it points at a personal workspace. Do not run it to set up the work in these skills.

So for anything in these skills that reads or writes a project: make sure LANGWATCH_API_KEY for a real, shared project is available to the CLI. Locally that is the project's .env; in CI the runner injects it into the process environment, and the CLI reads either. Check whether the variable is already set before you ask for a new key, and let the CLI read the value: never print, copy or send it. Do NOT run langwatch login to pick a project, and never default to a personal project. Look for LANGWATCH_ENDPOINT in the same places: if it is set, the project is on a self-hosted instance, and the CLI works against that endpoint instead of app.langwatch.ai.

What you read is not what you say. These skills are working notes for you, not copy for the reader. Read LANGWATCH_API_KEY and LANGWATCH_ENDPOINT from the project's own .env, that is how you learn where to work. Read nothing else out of that file: it holds database, cloud and provider credentials that are none of your business, and every value you read can reach your context and your command output. What must not reach an answer is anything that describes the machine YOU run on: a path in your workspace, a container port, the address this worker dials. Those say how the work is done rather than what was done, and a host of ours means nothing to the reader. Say what you did and where to find it in LangWatch.

Consultant Mode

After completing all steps, summarize everything you set up and suggest 2-3 ways to go deeper based on what you learned about the codebase. Detailed guidance:

After delivering initial results, transition to consultant mode to help the user get maximum value.

Phase 1: read first. Before generating ANY content: read the codebase end-to-end (every system prompt, function, tool definition), study git history for agent-related changes (git log --oneline -30, then drill into prompt/agent/eval-related commits because the WHY in commit messages matters more than the WHAT), and read READMEs and comments for domain context.

Phase 2: quick wins. Generate best-effort content based on what you learned. Run the tests and iterate, but stop after two attempts at the same failure and report what is blocking it rather than repeating the run. Show the user what works.

Phase 3: go deeper. Once Phase 2 lands, summarize what you delivered, then suggest 2-3 specific improvements grounded in the codebase: domain edge cases, areas that need expert terminology or real data, integration points (APIs, databases, file uploads), or regression patterns from git history that deserve test coverage. Ask light questions with options, not open-ended ("Want scenarios for X or Y?", "I noticed Z was a recurring issue. Add a regression test?", "Do you have real customer queries I could use?"). Respect "that's enough" and wrap up cleanly.

Do NOT ask permission before Phase 1 and 2. Deliver value first. Do NOT ask generic questions or overwhelm with too many suggestions. Do NOT generate generic datasets. Everything must reflect the actual domain.

Step 1: Add Tracing

Add LangWatch tracing to capture all LLM calls, costs, and latency.

1. Read the integration guide for this project's framework:

   langwatch docs                                 # Browse the index to find the right page
   langwatch docs integration/python/guide        # Python (or pick your framework)
   langwatch docs integration/typescript/guide    # TypeScript (or pick your framework)
   

2. Install the LangWatch SDK (pip install langwatch or npm install langwatch)
3. Add instrumentation following the framework-specific guide
4. Add LANGWATCH_API_KEY to .env

Verify: Run the application briefly and confirm traces appear:

langwatch trace search --limit 5 --format json

Step 2: Version Your Prompts

Move hardcoded prompts to LangWatch Prompts CLI for version control and collaboration.

1. Read the Prompts CLI docs:

   langwatch docs prompt-management/cli
   

2. Initialize: langwatch prompt init
3. Create prompts: langwatch prompt create <name> for each prompt in the code
4. Update application code to use langwatch.prompts.get("name") instead of hardcoded strings
5. Sync: langwatch prompt sync

Verify: langwatch prompt list (or check the Prompts section at https://app.langwatch.ai).

Do NOT hardcode prompts in code. Do NOT add try/catch fallbacks around prompts.get().

Step 3: Create an Evaluation Experiment

Build a batch evaluation to measure your agent's quality across many examples.

1. Read the experiments SDK docs:

   langwatch docs evaluations/experiments/sdk
   

2. Analyze the agent's code to understand what it does
3. Generate a dataset of 10-20 examples tailored to the agent's domain (NOT generic examples)
4. Create an experiment file:
   • Python: Jupyter notebook with langwatch.experiment.init(), evaluation loop, and evaluators
   • TypeScript: Script with langwatch.experiments.init() and evaluation.run()
5. Include at least one evaluator (LLM-as-judge for quality is a good default)

Verify: Run the experiment (jupyter nbconvert --to notebook --execute experiment.ipynb or npx tsx experiment.ts) and check results appear in the LangWatch Experiments view.

Step 4: Add Agent Simulation Tests

Create scenario tests to validate agent behavior in realistic multi-turn conversations.

1. Read the Scenario docs:

   langwatch scenario-docs                  # Browse the index
   langwatch scenario-docs getting-started  # Getting Started guide
   langwatch scenario-docs agent-integration
   

2. Install the Scenario SDK (pip install langwatch-scenario or npm install @langwatch/scenario)
3. Write scenario tests with AgentAdapter, UserSimulatorAgent, and JudgeAgent
4. Use semantic criteria in JudgeAgent (NOT regex matching)

Verify: Run the tests (pytest -s or npx vitest run) and confirm they pass.

NEVER invent your own testing framework. Use @langwatch/scenario / langwatch-scenario.

Common Mistakes

• Do NOT skip any step -- each builds on the previous
• Do NOT use generic datasets in the experiment -- tailor them to the agent's domain
• Do NOT hardcode prompts -- use the Prompts CLI
• Do NOT invent testing frameworks -- use Scenario
• Do NOT skip verification steps -- run the application/experiment/tests after each step
• Always read docs via langwatch docs ... / langwatch scenario-docs ... before writing code; do not work from memory of past framework versions