evals

name	Evals
description	Objective eval metrics via code/model/human graders with pass@k/pass^k scoring. USE WHEN eval, evaluate, test agent, benchmark, verify behavior, regression test, capability test, run eval, compare models, compare prompts, create judge, create use case, view results, failure to task, suite manager, transcript capture, trial runner, skillgrade, skill eval, skill test, docker eval, external eval.

Customization

Before executing, check for user customizations at: ${PAI_USER_DIR}/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.

2. 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

Request Pattern	Route To
Run eval, evaluate suite, run tests, benchmark	Workflows/RunEval.md
Compare models, model comparison, A/B test models	Workflows/CompareModels.md
Compare prompts, prompt comparison, test prompts	Workflows/ComparePrompts.md
Create judge, model grader, evaluation judge	Workflows/CreateJudge.md
Create use case, new eval, test case, create suite	Workflows/CreateUseCase.md
View results, eval results, scores, pass rate	Workflows/ViewResults.md
Skillgrade, skill eval, skill test, init eval, docker eval, external eval	Workflows/Skillgrade.md

CLI Quick Reference

Trigger	Tool
Run suite	Tools/AlgorithmBridge.ts
Log failure	Tools/FailureToTask.ts log
Convert failures	Tools/FailureToTask.ts convert-all
Create suite	Tools/SuiteManager.ts create
Check saturation	Tools/SuiteManager.ts check-saturation

---

Quick Reference

CLI Commands

# Run an eval suite
bun run ${CLAUDE_SKILL_DIR}/Tools/AlgorithmBridge.ts -s <suite>

# Log a failure for later conversion
bun run ${CLAUDE_SKILL_DIR}/Tools/FailureToTask.ts log "description" -c category -s severity

# Convert failures to test tasks
bun run ${CLAUDE_SKILL_DIR}/Tools/FailureToTask.ts convert-all

# Manage suites
bun run ${CLAUDE_SKILL_DIR}/Tools/SuiteManager.ts create <name> -t capability -d "description"
bun run ${CLAUDE_SKILL_DIR}/Tools/SuiteManager.ts list
bun run ${CLAUDE_SKILL_DIR}/Tools/SuiteManager.ts check-saturation <name>
bun run ${CLAUDE_SKILL_DIR}/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_SKILL_DIR}/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
natural_language_assert	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	binary_tests + static_analysis + tool_calls + llm_rubric
conversational	llm_rubric + natural_language_assert + state_check
research	llm_rubric + natural_language_assert + tool_calls
computer_use	state_check + tool_calls + 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