Evaluate whether an LLM agent selects the right tools, constructs correct arguments, sequences tool calls appropriately, and handles errors gracefully. Use for any Claude agent that has access to tools (function calling, MCP servers, API integrations).
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Install skill "eval-tool-use" with this command: npx skills add majidraza1228/skillsmp-majidraza1228-majidraza1228-eval-tool-use
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Related by shared tags or category signals.
Build a custom browser-based annotation interface for reviewing LLM traces and collecting human labels. Use when reviewers are working with raw JSON files, when you need to collect Pass/Fail labels at scale, or when trace data needs domain-specific formatting to be readable.
Systematically identify and categorize failure modes in an LLM pipeline by reading traces. Use when starting a new eval project, after significant pipeline changes (new features, model switches, prompt rewrites), when production metrics drop, or after incidents.
Audit an LLM eval pipeline and surface problems: missing error analysis, unvalidated judges, vanity metrics, etc. Use when inheriting an eval system, when unsure whether evals are trustworthy, or as a starting point when no eval infrastructure exists.
Evaluate a coding agent's output quality across the failure modes specific to code generation and editing: correctness, scope discipline, instruction following, safety, and diff quality. Use when building or improving a Claude-powered coding assistant, code review agent, or code generation pipeline.