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Weave Instrument

Organization
wandb
weave-instrument

Add Weave tracing to Python or TypeScript agents and LLM applications. Use when asked to instrument model calls, tools, or agent turns with Weave.

Overview

Publisherwandb
Repositoryskills
Skill nameweave-instrument
Stars
74
Forks
3
Bundled files
6
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by wandb on GitHub. Read the source before you install it.

Installation

Install the Weave Instrument AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/wandb/skills/tree/main/skills/weave-instrument
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/wandb/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/weave-instrument .claude/skills/weave-instrument
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Weave Instrument in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Weave Instrument on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Weave Instrument is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Weave instrumentation

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.

Check login first

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.

  • If the check is unavailable or fails because of connectivity or permissions, report login/access as unverified and stop; do not assume credentials are invalid.
  • After the user continues, confirm the application runtime can use its own configured credentials and access 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.

Choose the mechanism

  • Explicit agent spans: use the Session SDK for turns, model calls, tools, and delegation when auto-instrumentation cannot produce the required tree. Read the Python or TypeScript reference.
  • Auto-instrumentation: use only when the installed SDK captures the library and desired span shape. Check Python's 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.
  • Existing OTel pipeline: add Weave export to that provider using endpoint configuration.

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.

Implement incrementally

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.

Verify

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.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Weave Instrument AI skill do?

Add Weave tracing to Python or TypeScript agents and LLM applications. Use when asked to instrument model calls, tools, or agent turns with Weave.

Why use Weave Instrument on TypingMind?

Because you install it once and use it with any model. Weave Instrument is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Weave Instrument in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wandb/skills/tree/main/skills/weave-instrument. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Weave Instrument?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Weave Instrument?

As many as you like. As long as a model supports skills, you can use Weave Instrument with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Weave Instrument AI skill free?

Yes. It is published on GitHub by wandb under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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