LightState Registry / Portable Agent Skill (SKILL.md)s / Research & Intel / skill-anthropic-eval-harness
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Agentic Tool-Use Evaluation Harness & Prompt Grading Playbook
Design reproducible evaluation suites for tool-calling AI agents: golden test cases, deterministic rubric graders, latency/token cost tracking, and regress
⚡ 1-Click Agent & Developer Install
Zero Auth · Ready for Grok Bot, Claude Code & Cursornpx lightstate install skill-anthropic-eval-harness
Copy-Paste System Prompt / Routine Blueprint:
Load the Agentic Tool-Use Evaluation Harness & Prompt Grading Playbook (skill-anthropic-eval-harness) playbook. First inspect our current configuration and codebase for LLM Evals, Tool-Use Benchmarks, System Prompts, Rubric Grading. Then propose a concrete, step-by-step implementation plan following the skill-anthropic-eval-harness guardrails, run the non-destructive verification commands (npx promptfoo@latest eval), and summarize any findings, risks, or required environment variables before making production changes.
📘 Complete SKILL.md Playbook Specification
--- name: anthropic-eval-harness description: Design reproducible evaluation suites for tool-calling AI agents: golden test cases, deterministic rubric graders, latency/token cost tracking, and regression diffs across prompt edits. Use when working with LLM Evals, Tool-Use Benchmarks, System Prompts, Rubric Grading. triggers: - anthropic eval harness - llm evals - tool-use benchmarks --- # Agentic Tool-Use Evaluation Harness & Prompt Grading Playbook ## 1. When to Use This Skill Load `skill-anthropic-eval-harness` whenever an engineering or operations task involves **LLM Evals, Tool-Use Benchmarks, System Prompts, Rubric Grading**. Specifically: - Design reproducible evaluation suites for tool-calling AI agents: golden test cases, deterministic rubric graders, latency/token cost tracking, and regression diffs across prompt edits. - Designing, reviewing, or debugging production configurations without guesswork or deprecated flags. - Running deterministic pre-flight and post-execution verification checks before reporting completion. ## 2. Prerequisites & Required Environment - **Target Stack:** LLM Evals, Tool-Use Benchmarks, System Prompts, Rubric Grading - **Permissions:** Read-only inspection by default; ask for explicit confirmation before executing destructive mutations or production deployments. - **Reference Repository:** `https://github.com/anthropics/courses` ## 3. Step-by-Step Execution Playbook 1. **Context & Configuration Discovery:** Inspect existing project manifests (`package.json`, `wrangler.jsonc`, schema files, or environment declarations) before proposing changes. Never overwrite existing bindings or credentials blindly. 2. **Deterministic Implementation:** Apply the canonical patterns for **LLM Evals**: - Enforce strict schema/type validation at every external boundary. - Include explicit error handling, structured logging, and idempotency keys on all mutating calls. - Keep secrets in environment variables or secret stores—never hardcode tokens in source files. 3. **Verification & Regression Gate:** Run the verification commands below and inspect both exit codes and output payloads before marking the task complete. ## 4. Reference Commands & CLI Workflow ```bash npx promptfoo@latest eval npx promptfoo@latest view ``` ## 5. Edge Cases & Anti-Hallucination Guardrails - **Zero Unverified Claims:** Never claim a deployment, migration, or API call succeeded without inspecting the command output or HTTP status code. - **Rollback Readiness:** Always record the previous state or migration version so changes can be reverted cleanly if downstream health checks fail.
Synthesized Engineering Blueprint & Setup Guide
### Overview & Capability Scope
**Agentic Tool-Use Evaluation Harness & Prompt Grading Playbook** (`skill-anthropic-eval-harness`) is a verified, portable `SKILL.md` playbook curated with **@alexalbert__** for Grok Bot (`x.ai/bot/`), Claude Code (`~/.claude/skills/`), and Cursor (`.cursor/skills/`).
Design reproducible evaluation suites for tool-calling AI agents: golden test cases, deterministic rubric graders, latency/token cost tracking, and regression diffs across prompt edits.
### Required Stack & Tooling
- **Primary Stack:** LLM Evals, Tool-Use Benchmarks, System Prompts, Rubric Grading
- **Category:** `research`
- **Source & Reference:** [https://github.com/anthropics/courses](https://github.com/anthropics/courses)
### Quickstart Installation
Install this `SKILL.md` playbook directly into your agent workspace:
```bash
npx lightstate install skill-anthropic-eval-harness
```
Or copy the complete `SKILL.md` specification below into `.cursor/skills/skill-anthropic-eval-harness/SKILL.md` or `~/.claude/skills/skill-anthropic-eval-harness/SKILL.md`.
### Verification Commands
```bash
npx promptfoo@latest eval
npx promptfoo@latest view
```
Tags: #llm-evals#tool-use-benchmarks#system-prompts#rubric-grading#skill-md#research
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