| name | Evals |
| version | 1.2.17 |
| description | AI agent evaluation framework with three grader types (code-based, model-based, human) and pass@k/pass^k scoring over agent transcripts, tool-call sequences, and multi-turn conversations; covers capability and regression evals. 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.
-
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 &
-
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
bun run ${LIFEOS_SKILL_DIR}/Tools/AlgorithmBridge.ts -s <suite>
bun run ${LIFEOS_SKILL_DIR}/Tools/FailureToTask.ts log "description" -c category -s severity
bun run ${LIFEOS_SKILL_DIR}/Tools/FailureToTask.ts convert-all
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:
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
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)
- Start with 20-50 real failures - Don't overthink, capture what actually broke
- Unambiguous tasks - Two experts should reach identical verdicts
- Balanced problem sets - Test both "should do" AND "should NOT do"
- Grade outputs, not paths - Don't penalize valid creative solutions
- Calibrate LLM judges - Against human expert judgment
- Check transcripts regularly - Verify graders work correctly
- Monitor saturation - Graduate to regression when hitting 95%+
- 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.
