evals

name	Evals
description	Comprehensive AI agent evaluation framework with three grader types (code-based: deterministic; model-based: LLM rubric; human: gold standard) and pass@k / pass^k scoring. Evaluates agent transcripts, tool-call sequences, and multi-turn conversations — not just single outputs. Capability evals (~70% target) and regression evals (~99% target). Workflows: RunEval, CompareModels, ComparePrompts, CreateJudge, CreateUseCase, RunScenario, CreateScenario, ViewResults. Integrates with ALGORITHM ISC rows for automated verification. Domain patterns pre-configured for coding, conversational, research, computer-use agents. USE WHEN eval, evaluate, benchmark, regression test, compare models, create judge, test agent, pass@k, scenario simulation. NOT FOR scientific method framing (use Science).
effort	high
context	fork

Customization

Before executing, check for user customizations at: ~/.claude/LIFEOS/USER/CUSTOMIZATIONS/SKILLS/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:

   curl -s -X POST http://localhost:31337/notify \
     -H "Content-Type: application/json" \
     -d '{"message": "Running the WORKFLOWNAME workflow in the Evals skill to ACTION"}' \
     > /dev/null 2>&1 &
   

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

What It Does

Evaluates AI agents — their transcripts, tool-call sequences, and multi-turn conversations, not just single outputs. Three grader types cover it: code-based for deterministic checks, model-based for nuanced quality rubrics, and human for the gold standard. Scores with pass@k (capability) and pass^k (consistency). Splits evals into capability suites (~70% target) and regression suites (~99% target), and plugs into Algorithm ISC rows as a verification method.

The Problem

You can't tell if an agent got better or worse by eyeballing a few runs. A change that looks fine in one transcript may quietly regress on the next prompt, and a single run gives no statistical signal. Judging only the final output also misses how the agent got there — wrong tools, wrong order, lucky guess. This skill measures the whole workflow across repeated trials, so improvements and backsliding both show up as numbers you can gate on.

How It Works

Agent evaluation system based on Anthropic's "Demystifying Evals for AI Agents" (Jan 2026). It 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"
• "run scenario", "multi-turn eval", "simulated user test"
• "create scenario", "simulate conversation"
• 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

Workflow	Trigger	File
RunEval	Run eval, evaluate suite, run tests, benchmark	Workflows/RunEval.md
CompareModels	Compare models, model comparison, A/B test models	Workflows/CompareModels.md
ComparePrompts	Compare prompts, prompt comparison, test prompts	Workflows/ComparePrompts.md
CreateJudge	Create judge, model grader, evaluation judge	Workflows/CreateJudge.md
CreateUseCase	Create use case, new eval, test case, create suite	Workflows/CreateUseCase.md
RunScenario	Run scenario, multi-turn eval, simulated user test	Workflows/RunScenario.md
CreateScenario	Create scenario, new multi-turn eval, simulate conversation	Workflows/CreateScenario.md
ViewResults	View results, eval results, scores, pass rate	Workflows/ViewResults.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
Run scenario	Tools/ScenarioRunner.ts --scenario <path>

---

Quick Reference

CLI Commands

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

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

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

# Manage suites
bun run ${LIFEOS_SKILL_DIR}/Tools/SuiteManager.ts create <name> -t capability -d "description"
bun run ${LIFEOS_SKILL_DIR}/Tools/SuiteManager.ts list
bun run ${LIFEOS_SKILL_DIR}/Tools/SuiteManager.ts check-saturation <name>
bun run ${LIFEOS_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 ${LIFEOS_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
Tools/ScenarioRunner.ts	Multi-turn scenario runner (langwatch/scenario)
Tools/LifeosAgentAdapter.ts	Wraps LifeOS Inference.ts as scenario AgentAdapter
Tools/ScenarioToTranscript.ts	Scenario result → Evals Transcript/Trial/GraderResult
Scenarios/	Authored multi-turn scenarios (.scenario.ts)
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

Gotchas

• Choose the right grader type: Code-based for deterministic checks (fast, cheap). Model-based for nuanced quality (flexible, expensive). Human for calibration (gold standard, slow).
• pass@k scoring requires multiple runs. A single run doesn't give statistical significance. Default to pass@3 minimum.
• Transcript capture must be enabled BEFORE the test run. Can't retroactively capture transcripts.
• Eval results go to the current work directory — not a global location. Tie evals to the work item.
• Don't evaluate skills with trivial prompts. Simple one-liners may not trigger skill usage. Test prompts must be substantive.

Examples

Example 1: Compare two prompts

User: "evaluate which prompt produces better summaries"
→ Creates eval suite with 3+ test cases
→ Runs both prompts against test cases
→ Model-based grader scores quality
→ Reports pass@k and comparative analysis

Example 2: Regression test a skill change

User: "run evals on the Research skill after the update"
→ Uses existing test fixtures for Research
→ Before/after comparison
→ Reports any quality regressions

Execution Log

After completing any workflow, append a single JSONL entry:

echo '{"ts":"'$(date -u +%Y-%m-%dT%H:%M:%SZ)'","skill":"Evals","workflow":"WORKFLOW_USED","input":"8_WORD_SUMMARY","status":"ok|error","duration_s":SECONDS}' >> ~/.claude/LIFEOS/MEMORY/SKILLS/execution.jsonl

Replace WORKFLOW_USED with the workflow executed, 8_WORD_SUMMARY with a brief input description, and SECONDS with approximate wall-clock time. Log status: "error" if the workflow failed.