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.
68
82%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Risky
Do not use without reviewing
Security
2 findings — 1 high severity, 1 medium severity. You should review these findings carefully before considering using this skill.
The skill handles credentials insecurely by requiring the agent to include secret values verbatim in its generated output. This exposes credentials in the agent’s context and conversation history, creating a risk of data exfiltration.
Insecure credential handling detected (high risk: 0.90). The prompt includes commands that pass API keys or headers directly on the command line (e.g., `--api-key <key>`, `--header <key=value>` and an explicit `export PHOENIX_API_KEY=your-api-key` example), which encourages embedding secret values verbatim in generated commands or outputs.
The skill fetches instructions or code from an external URL at runtime, and the fetched content directly controls the agent’s prompts or executes code. This dynamic dependency allows the external source to modify the agent’s behavior without any changes to the skill itself.
Potentially malicious external URL detected (high risk: 0.90). The skill invokes runtime CLI commands that fetch prompt text from the configured Phoenix server (via PHOENIX_HOST / --endpoint, e.g. https://app.phoenix.arize.com) using commands like `px prompt get` which can be piped into an AI and thus directly control agent prompts, and the workflow depends on that external server at runtime.
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