agent-evaluation
Use when evaluating agent performance, building test frameworks, measuring quality, or asking about "agent evaluation", "LLM-as-judge", "agent testing", "quality metrics", "evaluation rubrics", "agent benchmarks"
What this skill does
# Agent Evaluation Methods
Agent evaluation requires different approaches than traditional software. Agents are non-deterministic, may take different valid paths, and lack single correct answers.
## Key Finding: 95% Performance Drivers
Research on BrowseComp found three factors explain 95% of variance:
| Factor | Variance | Implication |
|--------|----------|-------------|
| Token usage | 80% | More tokens = better performance |
| Tool calls | ~10% | More exploration helps |
| Model choice | ~5% | Better models multiply efficiency |
**Implications**: Model upgrades beat token increases. Multi-agent architectures validate.
## Multi-Dimensional Rubric
| Dimension | Excellent | Good | Acceptable | Failed |
|-----------|-----------|------|------------|--------|
| Factual accuracy | All correct | Minor errors | Some errors | Wrong |
| Completeness | All aspects | Most aspects | Key aspects | Missing |
| Citation accuracy | All match | Most match | Some match | Wrong |
| Tool efficiency | Optimal | Good | Adequate | Wasteful |
## LLM-as-Judge
```python
evaluation_prompt = """
Task: {task_description}
Agent Output: {agent_output}
Ground Truth: {ground_truth}
Evaluate on:
1. Factual accuracy (0-1)
2. Completeness (0-1)
3. Citation accuracy (0-1)
4. Tool efficiency (0-1)
Provide scores and reasoning.
"""
```
## Test Set Design
```python
test_set = [
{"name": "simple", "complexity": "simple",
"input": "What is capital of France?"},
{"name": "medium", "complexity": "medium",
"input": "Compare Apple and Microsoft revenue"},
{"name": "complex", "complexity": "complex",
"input": "Analyze Q1-Q4 sales trends"},
{"name": "very_complex", "complexity": "very_complex",
"input": "Research AI tech, evaluate impact, recommend strategy"}
]
```
## Evaluation Pipeline
```python
def evaluate_agent(agent, test_set):
results = []
for test in test_set:
output = agent.run(test["input"])
scores = llm_judge(output, test)
results.append({
"test": test["name"],
"scores": scores,
"passed": scores["overall"] >= 0.7
})
return results
```
## Complexity Stratification
| Level | Characteristics |
|-------|-----------------|
| Simple | Single tool call |
| Medium | Multiple tool calls |
| Complex | Many calls, ambiguity |
| Very Complex | Extended interaction, deep reasoning |
## Context Engineering Evaluation
Test context strategies systematically:
1. Run agents with different strategies on same tests
2. Compare quality scores, token usage, efficiency
3. Identify degradation cliffs at different context sizes
## Continuous Evaluation
- Run evaluations on all agent changes
- Track metrics over time
- Set alerts for quality drops
- Sample production interactions
## Avoiding Pitfalls
| Pitfall | Solution |
|---------|----------|
| Path overfitting | Evaluate outcomes, not steps |
| Ignoring edge cases | Include diverse scenarios |
| Single metric | Multi-dimensional rubrics |
| Ignoring context | Test realistic context sizes |
| No human review | Supplement automated eval |
## Best Practices
1. Use multi-dimensional rubrics
2. Evaluate outcomes, not specific paths
3. Cover complexity levels
4. Test with realistic context sizes
5. Run evaluations continuously
6. Supplement LLM with human review
7. Track metrics for trends
8. Set clear pass/fail thresholds
Related in AI Agents
skill-development
IncludedComprehensive meta-skill for creating, managing, validating, auditing, and distributing Claude Code skills and slash commands (unified in v2.1.3+). Provides skill templates, creation workflows, validation patterns, audit checklists, naming conventions, YAML frontmatter guidance, progressive disclosure examples, and best practices lookup. Use when creating new skills, validating existing skills, auditing skill quality, understanding skill architecture, needing skill templates, learning about YAML frontmatter requirements, progressive disclosure patterns, tool restrictions (allowed-tools), skill composition, skill naming conventions, troubleshooting skill activation issues, creating custom slash commands, configuring command frontmatter, using command arguments ($ARGUMENTS, $1, $2), bash execution in commands, file references in commands, command namespacing, plugin commands, MCP slash commands, Skill tool configuration, or deciding between skills vs slash commands. Delegates to docs-management skill for official documentation.
reprompter
IncludedTransform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.
adaptive-compaction
IncludedAdaptive add-on policy and recovery layer that decides WHEN to compact, prune, snapshot, or fork -- replacing fixed-percent auto-compaction across Claude Code, Codex, and MCP-capable hosts. Trigger on auto-compact timing or damage: "when should I compact", "is it safe to compact now or start a fresh session", "auto-compact fires too early/mid-task", "switching to an unrelated task but the window still has space", "context rot", "answers get worse the longer the session runs", "the agent forgot the plan or my decisions after it summarized", "add a layer on top that manages context without changing the agent", raising autoCompactWindow to give the policy room, or installing/tuning a cross-tool compaction policy or PreCompact hook -- even when "compaction" is never said but the problem is context-window pressure or post-summarization memory loss. Do NOT use to summarize a conversation, build RAG, write a summarization prompt (decides WHEN not HOW), or answer max-context-length trivia.
agent-skill-creator
IncludedCreate cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, and spec validation.
llm-wiki
IncludedUse when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
skill-master
IncludedAgent Skills authoring, evaluation, and optimization. Create, edit, validate, benchmark, and improve skills following the agentskills.io specification. Use when designing SKILL.md files, structuring skill folders (references, scripts, assets), ingesting external documentation into skills, running trigger evals, benchmarking skill quality, optimizing descriptions, or performing blind A/B comparisons. Keywords: agentskills.io, SKILL.md, skill authoring, eval, benchmark, trigger optimization.