Evaluation V2

Evaluation Methods for Agent Systems

Overview

This public intake copy packages plugins/antigravity-awesome-skills/skills/evaluation from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Core Concepts, Detailed Topics, Practical Guidance, Integration, Skill Metadata, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Testing agent performance systematically
• Validating context engineering choices
• Measuring improvements over time
• Catching regressions before deployment
• Building quality gates for agent pipelines
• Comparing different agent configurations

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: Core Concepts

Agent evaluation requires outcome-focused approaches that account for non-determinism and multiple valid paths. Multi-dimensional rubrics capture various quality aspects: factual accuracy, completeness, citation accuracy, source quality, and tool efficiency. LLM-as-judge provides scalable evaluation while human evaluation catches edge cases.

The key insight is that agents may find alternative paths to goals—the evaluation should judge whether they achieve right outcomes while following reasonable processes.

Performance Drivers: The 95% Finding Research on the BrowseComp evaluation (which tests browsing agents' ability to locate hard-to-find information) found that three factors explain 95% of performance variance:

This finding has significant implications for evaluation design:

• Token budgets matter: Evaluate agents with realistic token budgets, not unlimited resources
• Model upgrades beat token increases: Upgrading to Claude Sonnet 4.5 or GPT-5.2 provides larger gains than doubling token budgets on previous versions
• Multi-agent validation: The finding validates architectures that distribute work across agents with separate context windows

Examples

Example 1: Ask for the upstream workflow directly

Use @evaluation-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @evaluation-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @evaluation-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @evaluation-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Imported Usage Notes

Imported: Examples

Example 1: Simple Evaluation

def evaluate_agent_response(response, expected):
    rubric = load_rubric()
    scores = {}
    for dimension, config in rubric.items():
        scores[dimension] = assess_dimension(response, expected, dimension)
    overall = weighted_average(scores, config["weights"])
    return {"passed": overall >= 0.7, "scores": scores}

Example 2: Test Set Structure

Test sets should span multiple complexity levels to ensure comprehensive evaluation:

test_set = [
    {
        "name": "simple_lookup",
        "input": "What is the capital of France?",
        "expected": {"type": "fact", "answer": "Paris"},
        "complexity": "simple",
        "description": "Single tool call, factual lookup"
    },
    {
        "name": "medium_query",
        "input": "Compare the revenue of Apple and Microsoft last quarter",
        "complexity": "medium",
        "description": "Multiple tool calls, comparison logic"
    },
    {
        "name": "multi_step_reasoning",
        "input": "Analyze sales data from Q1-Q4 and create a summary report with trends",
        "complexity": "complex",
        "description": "Many tool calls, aggregation, analysis"
    },
    {
        "name": "research_synthesis",
        "input": "Research emerging AI technologies, evaluate their potential impact, and recommend adoption strategy",
        "complexity": "very_complex",
        "description": "Extended interaction, deep reasoning, synthesis"
    }
]

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Use multi-dimensional rubrics, not single metrics
• Evaluate outcomes, not specific execution paths
• Cover complexity levels from simple to complex
• Test with realistic context sizes and histories
• Run evaluations continuously, not just before release
• Supplement LLM evaluation with human review
• Track metrics over time for trend detection

Imported Operating Notes

Imported: Guidelines

1. Use multi-dimensional rubrics, not single metrics
2. Evaluate outcomes, not specific execution paths
3. Cover complexity levels from simple to complex
4. Test with realistic context sizes and histories
5. Run evaluations continuously, not just before release
6. Supplement LLM evaluation with human review
7. Track metrics over time for trend detection
8. Set clear pass/fail thresholds based on use case

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/evaluation, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @cred-omega-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @customs-trade-compliance-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @docker-expert-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @ejentum-reasoning-harness-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: References

Internal reference:

• Metrics Reference - Detailed evaluation metrics and implementation

References

Internal skills:

• All other skills connect to evaluation for quality measurement

External resources:

• LLM evaluation benchmarks
• Agent evaluation research papers
• Production monitoring practices

Imported: Detailed Topics

Evaluation Challenges

Non-Determinism and Multiple Valid Paths Agents may take completely different valid paths to reach goals. One agent might search three sources while another searches ten. They might use different tools to find the same answer. Traditional evaluations that check for specific steps fail in this context.

The solution is outcome-focused evaluation that judges whether agents achieve right outcomes while following reasonable processes.

Context-Dependent Failures Agent failures often depend on context in subtle ways. An agent might succeed on simple queries but fail on complex ones. It might work well with one tool set but fail with another. Failures may emerge only after extended interaction when context accumulates.

Evaluation must cover a range of complexity levels and test extended interactions, not just isolated queries.

Composite Quality Dimensions Agent quality is not a single dimension. It includes factual accuracy, completeness, coherence, tool efficiency, and process quality. An agent might score high on accuracy but low in efficiency, or vice versa.

Evaluation rubrics must capture multiple dimensions with appropriate weighting for the use case.

Evaluation Rubric Design

Multi-Dimensional Rubric Effective rubrics cover key dimensions with descriptive levels:

Factual accuracy: Claims match ground truth (excellent to failed)

Completeness: Output covers requested aspects (excellent to failed)

Citation accuracy: Citations match claimed sources (excellent to failed)

Source quality: Uses appropriate primary sources (excellent to failed)

Tool efficiency: Uses right tools reasonable number of times (excellent to failed)

Rubric Scoring Convert dimension assessments to numeric scores (0.0 to 1.0) with appropriate weighting. Calculate weighted overall scores. Determine passing threshold based on use case requirements.

Evaluation Methodologies

LLM-as-Judge LLM-based evaluation scales to large test sets and provides consistent judgments. The key is designing effective evaluation prompts that capture the dimensions of interest.

Provide clear task description, agent output, ground truth (if available), evaluation scale with level descriptions, and request structured judgment.

Human Evaluation Human evaluation catches what automation misses. Humans notice hallucinated answers on unusual queries, system failures, and subtle biases that automated evaluation misses.

Effective human evaluation covers edge cases, samples systematically, tracks patterns, and provides contextual understanding.

End-State Evaluation For agents that mutate persistent state, end-state evaluation focuses on whether the final state matches expectations rather than how the agent got there.

Test Set Design

Sample Selection Start with small samples during development. Early in agent development, changes have dramatic impacts because there is abundant low-hanging fruit. Small test sets reveal large effects.

Sample from real usage patterns. Add known edge cases. Ensure coverage across complexity levels.

Complexity Stratification Test sets should span complexity levels: simple (single tool call), medium (multiple tool calls), complex (many tool calls, significant ambiguity), and very complex (extended interaction, deep reasoning).

Context Engineering Evaluation

Testing Context Strategies Context engineering choices should be validated through systematic evaluation. Run agents with different context strategies on the same test set. Compare quality scores, token usage, and efficiency metrics.

Degradation Testing Test how context degradation affects performance by running agents at different context sizes. Identify performance cliffs where context becomes problematic. Establish safe operating limits.

Continuous Evaluation

Evaluation Pipeline Build evaluation pipelines that run automatically on agent changes. Track results over time. Compare versions to identify improvements or regressions.

Monitoring Production Track evaluation metrics in production by sampling interactions and evaluating randomly. Set alerts for quality drops. Maintain dashboards for trend analysis.

Imported: Practical Guidance

Building Evaluation Frameworks

1. Define quality dimensions relevant to your use case
2. Create rubrics with clear, actionable level descriptions
3. Build test sets from real usage patterns and edge cases
4. Implement automated evaluation pipelines
5. Establish baseline metrics before making changes
6. Run evaluations on all significant changes
7. Track metrics over time for trend analysis
8. Supplement automated evaluation with human review

Avoiding Evaluation Pitfalls

Overfitting to specific paths: Evaluate outcomes, not specific steps. Ignoring edge cases: Include diverse test scenarios. Single-metric obsession: Use multi-dimensional rubrics. Neglecting context effects: Test with realistic context sizes. Skipping human evaluation: Automated evaluation misses subtle issues.

Imported: Integration

This skill connects to all other skills as a cross-cutting concern:

• context-fundamentals - Evaluating context usage
• context-degradation - Detecting degradation
• context-optimization - Measuring optimization effectiveness
• multi-agent-patterns - Evaluating coordination
• tool-design - Evaluating tool effectiveness
• memory-systems - Evaluating memory quality

Imported: Skill Metadata

Created: 2025-12-20 Last Updated: 2025-12-20 Author: Agent Skills for Context Engineering Contributors Version: 1.0.0

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.