anthropic-evaluations
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.
Best use case
anthropic-evaluations is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using anthropic-evaluations should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/anthropic-evaluations/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How anthropic-evaluations Compares
| Feature / Agent | anthropic-evaluations | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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](./references/grader-types.md) - Code-based, model-based, human graders
- [Agent Type Patterns](./references/agent-type-patterns.md) - Coding, conversational, research, computer use
- [Roadmap](./references/roadmap.md) - Steps 0-8 for building evals from scratch
- [Frameworks](./references/frameworks.md) - Harbor, Promptfoo, Braintrust, etc.
**YAML Templates:**
- [coding-agent-eval.yaml](./references/coding-agent-eval.yaml) - Coding agent template
- [conversational-agent-eval.yaml](./references/conversational-agent-eval.yaml) - Support agent template
**Annotated Examples:**
- [Example: Coding Agent](./references/example-coding-agent.md) - Auth bypass fix walkthrough
- [Example: Conversational](./references/example-conversational.md) - 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](./references/grader-types.md) 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
```yaml
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](https://www.anthropic.com/engineering/demystifying-evals-for-ai-agents) by Anthropic (January 2026).Related Skills
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