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…
Manage GitHub issues, labels, project boards, sprint operations, and roadmap health for the Arize-ai/phoenix repository. Use when filing roadmap issues, triaging bugs, applying labels, running sprint close-out and rollover, auditing board hygiene, checking ticket-load balance across the team, keeping roadmap epics up…
Generate synthetic evaluation datasets for the PXI eval harness (evals/pxi/). Use whenever the user asks to create, author, draft, expand, or audit an eval dataset for a PXI tool, skill, or behavior — including phrases like "write evals for ", "test PXI behavior", "synthetic dataset for PXI", "cover this tool with…
Turns production evidence into tested improvements for your AI agent. Forms hypotheses from real traces and analytics, explains the reasoning behind each one, then executes with the user: scenario tests that reproduce production failures, prompt and code changes as reviewable PRs, new evaluators and monitors that…
Connect the codebase's AI agent to LangWatch agent simulations over HTTP, so test suites run against it from the platform. Finds or adds the agent's chat endpoint, wires authentication for scenario traffic, makes the server adopt the W3C traceparent header so the judge reads the agent's own traces, registers the agent…
Improve a prompt on the evaluations workbench through a measured loop. Score the baseline first, then duplicate the target column, form a hypothesis from failing rows, edit the copy's prompt draft, run, compare pass rate and cost, and repeat until the numbers hold. Use when the user asks to optimize or improve a…
Find official portals, APIs, and download paths for authoritative primary data sources (governments, international organizations, research institutions, etc.). Use when users need to know "where to find this data from an official source", "which source is more authoritative", or "how to cite primary data". Covers…
Find, inspect, and check AI benchmark records with the Benchmark Radar CLI. Use when a request needs benchmark discovery, details, recent Radar evidence, or local data health; do not assume why the user needs the results.
Edit Internacia country and intblock YAML safely — validation, enrichment, provenance, and OpenSpec gates. Use when modifying data/countries/, data/intblocks/, data/blocktypes/, running validate scripts, enrich.py, or proposing schema changes in internacia-db.
Query Internacia reference datasets — countries, borders, org membership, entity linking. Use when looking up country codes, UN members, NATO/EU/ASEAN rosters, border neighbors, Wikidata links, or joining on stable identifiers. Prefer DuckDB or Parquet over source YAML.
Interactive, input-tool powered, task refinement workflow: interrogates scope, deliverables, constraints before carrying out the task; Requires the Joyride extension.
Interface for MCP (Model Context Protocol) servers via CLI. Use when you need to interact with external tools, APIs, or data sources through MCP servers, list available MCP servers/tools, or call MCP tools from command line.
Audit MCP (Model Context Protocol) server configurations for security issues. Use this skill when: Reviewing .mcp.json files for security risks Checking MCP server args for hardcoded secrets or shell injection patterns Validating that MCP servers use pinned versions (not @latest) Detecting unpinned dependencies in MCP…
Run budget-controlled Webhound reports or datasets when a question deserves a real, inspectable investigation. Use for market maps, due diligence, source verification, evidence-backed comparisons, cited reports, or structured web datasets where missing information could change a decision.
A minimal, deliberately clean SKILL.md fixture used to CI-test scripts/lint-skills.mjs against a known-good file. It has valid frontmatter, no dollar-digit sequences, no secret-shaped strings, and no personal paths.