Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add dwmkerr/claude-toolkit --skill anthropic-evaluationsgit clone --depth 1 https://github.com/dwmkerr/claude-toolkitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/dwmkerr/claude-toolkit/anthropic-evaluations)<a href="https://agentmods.dev/skills/dwmkerr/claude-toolkit/anthropic-evaluations"><img src="https://agentmods.dev/badge/skills/dwmkerr/claude-toolkit/anthropic-evaluations/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/dwmkerr/claude-toolkit/anthropic-evaluations"><img src="https://agentmods.dev/badge/skills/dwmkerr/claude-toolkit/anthropic-evaluations.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00059 | $0.00847 |
| Opus 5 | $0.00030 | $0.00424 |
| Sonnet 5 | $0.00012 | $0.00169 |
| Haiku 4.5 | $0.00006 | $0.00085 |
Grade A, and why
anthropic-evaluations scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 |
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/agent-type-patterns.md 3.2 KB
- references/coding-agent-eval.yaml 1.2 KB
- references/conversational-agent-eval.yaml 1.2 KB
- references/example-coding-agent.md 2.3 KB
- references/example-conversational.md 2.7 KB
- references/frameworks.md 2.8 KB
- references/grader-types.md 2.9 KB
- references/roadmap.md 3.8 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 84 lines · 59 tokens per session scan A 8dea0968bbfc
anthropic-evaluations is a skill published in the GitHub repository dwmkerr/claude-toolkit (23 stars, last pushed 3d ago), licensed MIT. It adds 59 tokens to every session and 847 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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