smithery.ai

evals

USE WHEN eval, evaluate, test agent, benchmark, verify behavior, regression test, capability test. Includes three grader types (code-based, model-based, human), transcript capture, pass@k/pass^k metrics, and ALGORITHM integration.

First seen Mar 29, 2026

Installation

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Agent compatibility

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Skill metadata

Parsed from SKILL.md frontmatter.

Declared agents claude-code

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 7,336 B
  • docs SUMMARY.md 243 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 2 installs

SKILL.md

Customization

Before executing, check for user customizations at: ~/.claude/skills/PAI/USER/SKILLCUSTOMIZATIONS/Evals/

If this directory exists, load and apply any PREFERENCES.md, configurations, or resources found there. These override default behavior. If the directory does not exist, proceed with skill defaults.

🚨 MANDATORY: Voice Notification (REQUIRED BEFORE ANY ACTION)

You MUST send this notification BEFORE doing anything else when this skill is invoked.

  1. Send voice notification:

``bash curl -s -X POST http://localhost:8888/notify \ -H "Content-Type: application/json" \ -d '{"message": "Running the WORKFLOWNAME workflow in the Evals skill to ACTION"}' \ > /dev/null 2>&1 & ``

  1. Output text notification:

`` Running the WorkflowName workflow in the Evals skill to ACTION... ``

This is not optional. Execute this curl command immediately upon skill invocation.

Evals - AI Agent Evaluation Framework

Comprehensive agent evaluation system based on Anthropic's "Demystifying Evals for AI Agents" (Jan 2026).

Key differentiator: Evaluates agent workflows (transcripts, tool calls, multi-turn conversations), not just single outputs.


When to Activate

  • "run evals", "test this agent", "evaluate", "check quality", "benchmark"
  • "regression test", "capability test"
  • Compare agent behaviors across changes
  • Validate agent workflows before deployment
  • Verify ALGORITHM ISC rows
  • Create new evaluation tasks from failures

Core Concepts

Three Grader Types

Type Strengths Weaknesses Use For
Code-based Fast, cheap, deterministic, reproducible Brittle, lacks nuance Tests, state checks, tool verification
Model-based Flexible, captures nuance, scalable Non-deterministic, expensive Quality rubrics, assertions, comparisons
Human Gold standard, handles subjectivity Expensive, slow Calibration, spot checks, A/B testing

Evaluation Types

Type Pass Target Purpose
Capability ~70% Stretch goals, measuring improvement potential
Regression ~99% Quality gates, detecting backsliding

Key Metrics

  • pass@k: Probability of at least 1 success in k trials (measures capability)
  • pass^k: Probability all k trials succeed (measures consistency/reliability)

Workflow Routing

Trigger Workflow
"run evals", "evaluate suite" Run suite via Tools/AlgorithmBridge.ts
"log failure" Log failure via Tools/FailureToTask.ts log
"convert failures" Convert to tasks via Tools/FailureToTask.ts convert-all
"create suite" Create suite via Tools/SuiteManager.ts create
"check saturation" Check via Tools/SuiteManager.ts check-saturation

Quick Reference

CLI Commands

# Run an eval suite
bun run ~/.claude/skills/Evals/Tools/AlgorithmBridge.ts -s <suite>

# Log a failure for later conversion
bun run ~/.claude/skills/Evals/Tools/FailureToTask.ts log "description" -c category -s severity

# Convert failures to test tasks
bun run ~/.claude/skills/Evals/Tools/FailureToTask.ts convert-all

# Manage suites
bun run ~/.claude/skills/Evals/Tools/SuiteManager.ts create <name> -t capability -d "description"
bun run ~/.claude/skills/Evals/Tools/SuiteManager.ts list
bun run ~/.claude/skills/Evals/Tools/SuiteManager.ts check-saturation <name>
bun run ~/.claude/skills/Evals/Tools/SuiteManager.ts graduate <name>

ALGORITHM Integration

Evals is a verification method for THE ALGORITHM ISC rows:

# Run eval and update ISC row
bun run ~/.claude/skills/Evals/Tools/AlgorithmBridge.ts -s regression-core -r 3 -u

ISC rows can specify eval verification:

| # | What Ideal Looks Like | Verify |
|---|----------------------|--------|
| 1 | Auth bypass fixed | eval:auth-security |
| 2 | Tests all pass | eval:regression |

Available Graders

Code-Based (Fast, Deterministic)

Grader Use Case
string_match Exact substring matching
regex_match Pattern matching
binary_tests Run test files
static_analysis Lint, type-check, security scan
state_check Verify system state after execution
tool_calls Verify specific tools were called

Model-Based (Nuanced)

Grader Use Case
llm_rubric Score against detailed rubric
naturallanguageassert Check assertions are true
pairwise_comparison Compare to reference with position swap

Domain Patterns

Pre-configured grader stacks for common agent types:

Domain Primary Graders
coding binarytests + staticanalysis + toolcalls + llmrubric
conversational llmrubric + naturallanguageassert + statecheck
research llmrubric + naturallanguageassert + toolcalls
computer_use statecheck + toolcalls + llm_rubric

See Data/DomainPatterns.yaml for full configurations.


Task Schema (YAML)

task:
  id: "fix-auth-bypass_1"
  description: "Fix authentication bypass when password is empty"
  type: regression  # or capability
  domain: coding

  graders:
    - type: binary_tests
      required: [test_empty_pw.py]
      weight: 0.30

    - type: tool_calls
      weight: 0.20
      params:
        sequence: [read_file, edit_file, run_tests]

    - type: llm_rubric
      weight: 0.50
      params:
        rubric: prompts/security_review.md

  trials: 3
  pass_threshold: 0.75

Resource Index

Resource Purpose
Types/index.ts Core type definitions
Graders/CodeBased/ Deterministic graders
Graders/ModelBased/ LLM-powered graders
Tools/TranscriptCapture.ts Capture agent trajectories
Tools/TrialRunner.ts Multi-trial execution with pass@k
Tools/SuiteManager.ts Suite management and saturation
Tools/FailureToTask.ts Convert failures to test tasks
Tools/AlgorithmBridge.ts ALGORITHM integration
Data/DomainPatterns.yaml Domain-specific grader configs

Key Principles (from Anthropic)

  1. Start with 20-50 real failures - Don't overthink, capture what actually broke
  2. Unambiguous tasks - Two experts should reach identical verdicts
  3. Balanced problem sets - Test both "should do" AND "should NOT do"
  4. Grade outputs, not paths - Don't penalize valid creative solutions
  5. Calibrate LLM judges - Against human expert judgment
  6. Check transcripts regularly - Verify graders work correctly
  7. Monitor saturation - Graduate to regression when hitting 95%+
  8. Build infrastructure early - Evals shape how quickly you can adopt new models

Related

  • ALGORITHM: Evals is a verification method
  • Science: Evals implements scientific method
  • Browser: For visual verification graders