Evaluation Methods for Agent Systems workflow skill. Use this skill when the user needs Build evaluation frameworks for agent systems. Use when testing agent performance systematically, validating context engineering choices, or measuring improvements over time and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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name
evaluation-v2
description
Evaluation Methods for Agent Systems workflow skill. Use this skill when the user needs Build evaluation frameworks for agent systems. Use when testing agent performance systematically, validating context engineering choices, or measuring improvements over time and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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
Situation
Start here
Why it matters
First-time use
metadata.json
Confirms repository, branch, commit, and imported path through the external_source block before touching the copied workflow
Provenance review
ORIGIN.md
Gives reviewers a plain-language audit trail for the imported source
Workflow execution
SKILL.md
Starts with the smallest copied file that materially changes execution
Supporting context
SKILL.md
Adds the next most relevant copied source file without loading the entire package
Handoff decision
## Related Skills
Helps the operator switch to a stronger native skill when the task drifts
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.
Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
Read the overview and provenance files before loading any copied upstream support files.
Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
Validate the result against the upstream expectations and the evidence you can point to in the copied files.
Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
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:
Factor
Variance Explained
Implication
Token usage
80%
More tokens = better performance
Number of tool calls
~10%
More exploration helps
Model choice
~5%
Better models multiply efficiency
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.
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
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
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
@00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
@00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith-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.
Resource family
What it gives the reviewer
Example path
references
copied reference notes, guides, or background material from upstream
references/n/a
examples
worked examples or reusable prompts copied from upstream
examples/n/a
scripts
upstream helper scripts that change execution or validation
scripts/n/a
agents
routing or delegation notes that are genuinely part of the imported package
agents/n/a
assets
supporting assets or schemas copied from the source package
assets/n/a
Imported Reference Notes
Imported: References
Internal reference:
Metrics Reference - Detailed evaluation metrics and implementation
Imported: 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
Define quality dimensions relevant to your use case
Create rubrics with clear, actionable level descriptions
Build test sets from real usage patterns and edge cases
Implement automated evaluation pipelines
Establish baseline metrics before making changes
Run evaluations on all significant changes
Track metrics over time for trend analysis
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:
