Phoenix-Cli
Phoenix-Cli是一款code方向的AI技能,核心价值是Debug LLM applications using the Phoenix CLI,可用于解决开发者在code领域的实际问题,帮助用户提升效率、自动化重复任务或优化工作流。
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, an
mkdir -p ./skills/phoenix-cli && curl -sfL https://raw.githubusercontent.com/github/awesome-copilot/main/skills/phoenix-cli/SKILL.md -o ./skills/phoenix-cli/SKILL.md Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# Phoenix CLI
Invocation
px <resource> <action> # if installed globally
npx @arizeai/phoenix-cli <resource> <action> # no install requiredThe CLI uses singular resource commands with subcommands like `list` and `get`:
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
export PHOENIX_HOST=http://localhost:6006
export PHOENIX_PROJECT=my-project
export PHOENIX_API_KEY=your-api-key # if auth is enabledAlways 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 what went wrong (no taxonomy yet) | [references/open-coding](references/open-coding.md) |
| Group those notes into a structured failure taxonomy and quantify what matters | [references/axial-coding](references/axial-coding.md) |
Both stages tag every artifact with one shared **coding annotation identifier** (descriptive shape, e.g. `coding-run:chatbot-context-loss-2026-05-06`) so the run is queryable, reversible, and viewable as a unit. Pass `--identifier <value>` explicitly on every `px` call — shell inheritance is unreliable across agent harnesses. Open coding writes notes via `px ... add-note` and records a small local JSONL sidecar at `.px/coding/<sanitized-identifier>.jsonl`; axial coding reads that sidecar as the deterministic handoff and records labels in `.px/coding/<sanitized-identifier>-axial.jsonl`. Pick the identifier once per run (see [references/open-coding.md](references/open-coding.md#coding-annotation-identifier-pick-this-first)), then share the Phoenix UI link from the wrap-up section. Revert is opt-in and runs three identifier-bound DELETEs only after explicit user confirmation.
> **Workflow term vs. server annotation name.** The skill prose calls this value the **coding annotation identifier** (shell-variable hint: `CODING_ANNOTATION_IDENTIFIER`). The server-side annotation NAME used for the UI filter is unchanged — `coding_session_id` — for data compatibility with rows already written by previous runs. Don't try to rename the server-side annotation; treat the asymmetry as load-bearing.
Workflows
**"What do I do after instrumenting?" / "Where do I focus?" / "What's going wrong?"**
[open-coding](references/open-coding.md) → [axial-coding](references/axial-coding.md) → build evals for the top categories.
Reference Categories
| Prefix | Description |
| ------ | ----------- |
| `references/open-coding` | Free-form notes against sampled traces, spans, or sessions — reach for it whenever the user wants to make sense of LLM traffic but has no failure categories yet. Includes a unit-of-analysis diagnostic so the workflow runs at the level the failure modes actually live at (trace for stateless single-shot calls, session for multi-turn agents, span for mechanical/in-isolation failures). |
| `references/axial-coding` | Inductive grouping of notes into a MECE taxonomy with counts — reach for it whenever the user has observations and needs categories or eval targets |
Auth
px auth status # check connection and authentication
px auth status --endpoint http://other:6006 # check a specific endpoint
px auth status --profile staging # check a named profile's connectionProfiles
Named profiles let you switch between multiple Phoenix
🎯 Best For
- Engineering teams doing code reviews
- Open source maintainers
- Debugging engineers
- QA teams
- Data analysts
💡 Use Cases
- Reviewing pull requests for security vulnerabilities
- Checking code style consistency
- Tracing runtime errors in production logs
- Identifying memory leaks
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude or GitHub Copilot and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply Phoenix-Cli to Your Work
Open your project in the AI assistant and ask it to apply the skill. Start with a small module to verify the output quality.
- 4
Review and Refine
Review AI suggestions before committing. Run tests, check for regressions, and iterate on the skill output.
❓ Frequently Asked Questions
Does this skill check for OWASP Top 10?
Security-focused review skills often include OWASP checks. Check the skill content for specific vulnerability categories covered.
Can this debug production issues?
Yes, but always ensure you have proper logging and monitoring in place first.
Can this connect to my database directly?
Most data skills accept CSV or JSON input. Database connectors are listed in the Works With section.
Is Phoenix-Cli compatible with Cursor and VS Code?
Yes — this skill works with any AI coding assistant including Cursor, VS Code with Copilot, and JetBrains IDEs.
Do I need specific dependencies for Phoenix-Cli?
Check the install command and Works With section. Most code skills only require the AI assistant and your codebase.
⚠️ Common Mistakes to Avoid
Blindly accepting AI suggestions
Always verify AI-generated review comments. Some suggestions may not apply to your specific codebase conventions.
Debugging without context
Always provide the full error stack and surrounding code context for accurate debugging.
Not validating data quality
AI analysis is only as good as your input data. Profile and clean data before analysis.
Skipping validation
Always test AI-generated code changes, even for simple refactors.