phoenix-cli
Debug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, structure trace review with open coding and axial coding, inspect datasets, review experiments, query annotation configs, and use the GraphQL API. Use whenever the user is analyzing traces or spans, investigating LLM/agent failures, deciding what to do after instrumenting an app, building failure taxonomies, choosing what evals to write, or asking "what's going wrong", "what kinds of mistakes", or "where do I focus" — even without naming a technique.
Profile →npx skills add github/awesome-copilot --skill phoenix-cli --agent copilot
Same command for any agent — swap --agent for claude-code, codex, cursor.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Phoenix CLI ## Invocation ```bash px <resource> <action> # if installed globally npx @arizeai/phoenix-cli <resource> <action> # no install required ``` The CLI uses singular resource commands with subcommands like `list` and `get`: ```bash px trace list px trace get <trace-id> px trace annotate <trace-id> px trace add-note <trace-id> px trace-annotations delete px span list px span annotate <span-id> px span add-note <span-id> px span-annotations delete px session list px session get <session-id> px session annotate <session-id> px session add-note <session-id> px session-annotations delete px dataset list px dataset get <name> px project list px project get <name> px annotation-config list px auth status px profile list px profile show [name] px profile create <name> px profile use <name> px profile edit <name> px profile delete <name> ``` ## Setup ```bash export PHOENIX_HOST=http://localhost:6006 export PHOENIX_PROJECT=my-project export PHOENIX_API_KEY=your-api-key # if auth is enabled ``` Always use `--format raw --no-progress` when piping to `jq`. ## Quick Reference | Task | Files | | ---- | ----- | | Look at sampled traces, spans, or sessions and write specific notes about w
- Invocation
- Setup
- Quick Reference
- Workflows
- Reference Categories
- Auth
- Profiles
- Projects
- Traces
- Trace JSON shape
- Spans
- Span JSON shape
- Sessions
- Session JSON shape
px <resource> <action> # if installed globally npx @arizeai/phoenix-cli <resource> <action> # no install required px trace list px trace get <trace-id> px trace annotate <trace-id> px trace add-note <trace-id> px trace-annotations delete px span list px span annotate <span-id> px span add-note <span-id>
What does the phoenix-cli skill do?
Debug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, structure trace review with open coding and axial coding, inspect datasets, review experiments, query annotation configs, and use the GraphQL API. Use whenever the user is analyzing traces or spans, investigating LLM/agent failures, deciding what to do after instrumenting an app, building failure taxonomies, choosing what evals to write, or asking "what's going wrong", "what kinds of mistakes", or "where do I focus" — even without naming a technique.
How do I install it?
Run `npx skills add github/awesome-copilot --skill phoenix-cli --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From github/awesome-copilot, a repository with 37,432 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.