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awesome-copilot/skills/phoenix-tracing/references/metadata-python.md
Jim Bennett d79183139a Add Arize and Phoenix LLM observability skills (#1204)
* Add 9 Arize LLM observability skills

Add skills for Arize AI platform covering trace export, instrumentation,
datasets, experiments, evaluators, AI provider integrations, annotations,
prompt optimization, and deep linking to the Arize UI.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Add 3 Phoenix AI observability skills

Add skills for Phoenix (Arize open-source) covering CLI debugging,
LLM evaluation workflows, and OpenInference tracing/instrumentation.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Ignoring intentional bad spelling

* Fix CI: remove .DS_Store from generated skills README and add codespell ignore

Remove .DS_Store artifact from winmd-api-search asset listing in generated
README.skills.md so it matches the CI Linux build output. Add queston to
codespell ignore list (intentional misspelling example in arize-dataset skill).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Add arize-ax and phoenix plugins

Bundle the 9 Arize skills into an arize-ax plugin and the 3 Phoenix
skills into a phoenix plugin for easier installation as single packages.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Fix skill folder structures to match source repos

Move arize supporting files from references/ to root level and rename
phoenix references/ to rules/ to exactly match the original source
repository folder structures.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* Fixing file locations

* Fixing readme

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-02 09:58:55 +11:00

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Markdown

# Phoenix Tracing: Custom Metadata (Python)
Add custom attributes to spans for richer observability.
## Install
```bash
pip install openinference-instrumentation
```
## Session
```python
from openinference.instrumentation import using_session
with using_session(session_id="my-session-id"):
# Spans get: "session.id" = "my-session-id"
...
```
## User
```python
from openinference.instrumentation import using_user
with using_user("my-user-id"):
# Spans get: "user.id" = "my-user-id"
...
```
## Metadata
```python
from openinference.instrumentation import using_metadata
with using_metadata({"key": "value", "experiment_id": "exp_123"}):
# Spans get: "metadata" = '{"key": "value", "experiment_id": "exp_123"}'
...
```
## Tags
```python
from openinference.instrumentation import using_tags
with using_tags(["tag_1", "tag_2"]):
# Spans get: "tag.tags" = '["tag_1", "tag_2"]'
...
```
## Combined (using_attributes)
```python
from openinference.instrumentation import using_attributes
with using_attributes(
session_id="my-session-id",
user_id="my-user-id",
metadata={"environment": "production"},
tags=["prod", "v2"],
prompt_template="Answer: {question}",
prompt_template_version="v1.0",
prompt_template_variables={"question": "What is Phoenix?"},
):
# All attributes applied to spans in this context
...
```
## On a Single Span
```python
span.set_attribute("metadata", json.dumps({"key": "value"}))
span.set_attribute("user.id", "user_123")
span.set_attribute("session.id", "session_456")
```
## As Decorators
All context managers can be used as decorators:
```python
@using_session(session_id="my-session-id")
@using_user("my-user-id")
@using_metadata({"env": "prod"})
def my_function():
...
```