Add Weave tracing to Python or TypeScript agents and LLM applications. Use when asked to instrument model calls, tools, or agent turns with Weave.
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Complete the login check before editing or initializing. If no valid login exists, send only that section's reply and stop.
After a valid login, inspect the application's dependencies, agent boundaries,
and existing OTel provider. Confirm entity/project and runtime credentials
before sending data; never read, print, or commit API keys. Read
trace fidelity before changing any instrumentation
path. Initialize once at startup with
weave.init("entity/project") in Python or await weave.init(...) in Node.
Before editing code or initializing tracing, verify the existing W&B login for
the target deployment with a noninteractive, read-only identity request through
the configured SDK or connector. Use credentials in place; never print, copy,
or inspect secret values. Do not call login or tracing init as a login probe.
If no valid login exists, stop. Do not edit, initialize, or continue this skill.
Do not run wandb login in this session. This session's shell is not a normal
terminal, so wandb login --verify exits with No API key configured instead
of prompting. Do not ask for a key in this chat or create credentials.
Send only this, and nothing else. No decisions, notes, plans, or documentation links:
There's no W&B login yet, so this stops here. Open a new terminal tab and run
wandb login --verify. Create an API key at
https://wandb.ai/authorize?ref=models. If the browser opens a different page,
open that link again. Paste the key in that terminal, not here, and type
continue.
On a self-hosted or dedicated deployment, use wandb login --verify --host <base-url>
and the authorize page that command prints. Still send only that.
entity/project. A browser login does not
establish SDK authentication. Node/Forge runtimes without a .netrc fallback
need credentials configured by the user through their runtime secret mechanism.INTEGRATION_MODULE_MAPPING
or Node's integrations/hooks.ts, then verify emitted spans. Read
auto-instrumentation caveats; registry membership
alone does not prove capture is active. Avoid duplicate manual wrapping.A turn spans one user exchange, including its tool loop. An LLM span covers one model call through stream completion; a Tool covers execution; a SubAgent covers delegation. Record actual output/usage and preserve provider tool-call IDs. Keep return values, exceptions, retries, and cancellation unchanged. Close spans on failure and isolate concurrent conversations.
Add one traced path at a time: a model call, its tool loop, then delegation or concurrency where applicable. After each change, run the path and follow read-back verification. Compare emitted and stored spans with the actual execution before extending the instrumentation.
Fix mismatches and rerun the same check before continuing. Empty results, failed queries, and unavailable checks are unverified, not passes. Stop at that boundary and report what the user must do to unblock verification.
Run relevant application tests and the
conformance check for the paths
you instrumented. If the full suite requires CI, browsers, or unavailable
services, run focused local checks covering success and failure, and report the
skipped coverage. Check parentage and
gen_ai.operation.name: invoke_agent for turns/subagents, chat for LLMs,
execute_tool for tools. Agent-shaped traces belong in Agents; flat calls in
Calls. Confirm backend arrival when credentials are available; an init banner
or successful flush is not delivery proof. Otherwise report the unverified
surface and give the application's exact smoke command.
Adapted from Weave. Use the installed SDK's exports when they differ from these references.
wandb/skills
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