anthropic-evaluations

name	anthropic-evaluations
description	This skill should be used when the user asks to "create evals", "evaluate an agent", "build evaluation suite", or mentions agent testing, graders, or benchmarks. Also suggest when building coding agents, conversational agents, or research agents that need quality assurance.

Anthropic Evaluations

Build rigorous evaluations for AI agents using Anthropic's proven patterns.

Quick Reference

You MUST read the reference files for detailed guidance:

• Grader Types - Code-based, model-based, human graders
• Agent Type Patterns - Coding, conversational, research, computer use
• Roadmap - Steps 0-8 for building evals from scratch
• Frameworks - Harbor, Promptfoo, Braintrust, etc.

YAML Templates:

• coding-agent-eval.yaml - Coding agent template
• conversational-agent-eval.yaml - Support agent template

Annotated Examples:

• Example: Coding Agent - Auth bypass fix walkthrough
• Example: Conversational - Refund handling walkthrough

Core Definitions

Term	Definition
Task	Single test with defined inputs and success criteria
Trial	One attempt at a task (run multiple for consistency)
Grader	Logic that scores agent performance; tasks can have multiple
Transcript	Complete record of a trial (outputs, tool calls, reasoning)
Outcome	Final state in environment (not just what agent said)
Evaluation harness	Infrastructure that runs evals end-to-end
Agent harness	System enabling model to act as agent (scaffold)
Evaluation suite	Collection of tasks measuring specific capabilities

Grader Types (Quick Reference)

Type	Methods	Best For
Code-based	String match, unit tests, static analysis, state checks	Fast, cheap, objective verification
Model-based	Rubric scoring, assertions, pairwise comparison	Nuanced, open-ended tasks
Human	SME review, A/B testing, spot-check sampling	Gold standard calibration

See Grader Types for detailed comparison.

Capability vs Regression Evals

Type	Question	Target Pass Rate
Capability	"What can this agent do well?"	Start low, hill-climb
Regression	"Does it still handle what it used to?"	Near 100%

Capability evals with high pass rates "graduate" to regression suites.

Non-Determinism Metrics

Metric	Measures	Use When
pass@k	At least 1 success in k attempts	One success matters (coding)
pass^k	All k attempts succeed	Consistency essential (customer-facing)

Example: 75% per-trial success rate

• pass@3 ≈ 98% (likely to get at least one)
• pass^3 ≈ 42% (0.75³ all succeed)

Tracked Metrics

tracked_metrics:
  - type: transcript
    metrics: [n_turns, n_toolcalls, n_total_tokens]
  - type: latency
    metrics: [time_to_first_token, output_tokens_per_sec, time_to_last_token]

Attribution

Based on Demystifying evals for AI agents by Anthropic (January 2026).