Agent Comparison

Agent Comparison Skill

Compare agent variants through controlled A/B benchmarks. Runs identical tasks on both agents, grades output quality with domain-specific checklists, and reports total session token cost to a working solution. This skill is exclusively for agent variant comparison — use agent-evaluation for single-agent assessment, and skill-eval for skill testing.

Reference Loading Table

Instructions

See references/examples-and-errors.md for error handling. See references/optimize-phase.md for Phase 5 OPTIMIZE full procedure. See references/methodology.md for December 2024 benchmark data.

Phase 1: PREPARE

Goal: Create benchmark environment and validate both agent variants exist.

Read and follow the repository CLAUDE.md before starting any execution.

Step 1: Analyze original agent

wc -l agents/{original-agent}.md
grep "^## " agents/{original-agent}.md
grep -c '```' agents/{original-agent}.md

Step 2: Create or validate compact variant

If creating a compact variant, preserve:

• YAML frontmatter (name, description, routing)
• Core patterns and principles
• Error handling philosophy

Remove or condense:

• Lengthy code examples (keep 1-2 representative per pattern)
• Verbose explanations (condense to bullet points)
• Redundant instructions and changelogs

Target 10-15% of original size while keeping essential knowledge. Remove redundancy, not capability — stripping error handling patterns or concurrency guidance creates an unfair comparison because the compact agent is missing essential knowledge rather than expressing it concisely.

Step 3: Validate compact variant structure

head -20 agents/{compact-agent}.md | grep -E "^(name|description):"
echo "Original: $(wc -l < agents/{original-agent}.md) lines"
echo "Compact:  $(wc -l < agents/{compact-agent}.md) lines"

Step 4: Create benchmark directory and prepare prompts

mkdir -p benchmark/{task-name}/{full,compact}

Write the task prompt ONCE, then copy it for both agents. Both agents must receive the exact same task description, character-for-character, because different requirements produce different solutions and invalidate all measurements.

Keep benchmark scripts simple — no speculative features or configurable frameworks that were not requested.

Gate: Both agent variants exist with valid YAML frontmatter. Benchmark directories created. Identical task prompts written. Proceed only when gate passes.

Phase 2: BENCHMARK

Goal: Run identical tasks on both agents, capturing all metrics.

Step 1: Run simple task benchmark (2-3 tasks)

Use algorithmic problems with clear specifications (e.g., Advent of Code Day 1-6). Simple tasks establish a baseline — if an agent fails here, it has fundamental issues. Running multiple simple tasks is necessary because a single data point is sensitive to task selection bias and cannot distinguish luck from systematic quality.

Spawn both agents in parallel using Task tool:

Task(
  prompt="[exact task prompt]\nSave to: benchmark/{task}/full/",
  subagent_type="{full-agent}"
)

Task(
  prompt="[exact task prompt]\nSave to: benchmark/{task}/compact/",
  subagent_type="{compact-agent}"
)

Run in parallel to avoid caching effects or system load variance skewing results.

Step 2: Run complex task benchmark (1-2 tasks)

Use production-style problems that require concurrency, error handling, edge case anticipation — these are where quality differences emerge because simple tasks mask differences in edge case handling. See references/benchmark-tasks.md for standard tasks.

Recommended complex tasks:

• Worker Pool: Rate limiting, graceful shutdown, panic recovery
• LRU Cache with TTL: Generics, background goroutines, zero-value semantics
• HTTP Service: Middleware chains, structured errors, health checks

Step 3: Capture metrics for each run

Record immediately after each agent completes — delayed recording loses precision. Track input/output token counts per turn where visible, since total session cost (not just prompt size) is what matters.

Step 4: Run tests with race detector

cd benchmark/{task-name}/full && go test -race -v -count=1
cd benchmark/{task-name}/compact && go test -race -v -count=1

Use -count=1 to disable test caching. All generated code must pass the same test suite with the -race flag because race conditions are automatic quality failures.

Gate: Both agents completed all tasks. Metrics captured for every run. Test output saved. Proceed only when gate passes.

Phase 3: GRADE

Goal: Score code quality beyond pass/fail using domain-specific checklists.

Step 1: Create quality checklist BEFORE reviewing code

Define criteria before seeing results to prevent bias — inventing criteria after seeing one agent's output skews the comparison. See references/grading-rubric.md for standard rubrics.

Step 2: Score each solution independently

Grade each agent's code on all five criteria. Score one agent completely before starting the other. Report facts and show command output rather than describing it — every claim must be backed by measurable data (tokens, test counts, quality scores).

## {Agent} Solution - {Task}

| Criterion | Score | Notes |
|-----------|-------|-------|
| Correctness | X/5 | |
| Error Handling | X/5 | |
| Idioms | X/5 | |
| Documentation | X/5 | |
| Testing | X/5 | |
| **Total** | **X/25** | |

Step 3: Document specific bugs with production impact

For each bug found, record:

### Bug: {description}
- Agent: {which agent}
- What happened: {behavior}
- Correct behavior: {expected}
- Production impact: {consequence}
- Test coverage: {did tests catch it? why not?}

"Tests pass" is necessary but not sufficient — production bugs often pass tests. Apply the domain-specific quality checklist rather than relying only on test pass rates, because tests can miss goroutine leaks, wrong semantics, and other production issues.

Step 4: Calculate effective cost

effective_cost = total_tokens * (1 + bug_count * 0.25)

An agent using 194k tokens with 0 bugs has better economics than one using 119k tokens with 5 bugs requiring fixes. The metric that matters is total cost to working, production-quality solution — not prompt size, because prompt is a one-time cost while reasoning tokens dominate sessions. Check quality scores before claiming token savings, since savings that come from cutting corners are not real savings.

Gate: Both solutions graded with evidence. Specific bugs documented with production impact. Effective cost calculated. Proceed only when gate passes.

Phase 4: REPORT

Goal: Generate comparison report with evidence-backed verdict.

Step 1: Generate comparison report

Use the report template from references/report-template.md. Include:

• Executive summary with clear winner per metric
• Per-task results with metrics tables
• Token economics analysis (one-time prompt cost vs session cost)
• Specific bugs found and their production impact
• Verdict based on total evidence

Step 2: Run comparison analysis

python3 ${CLAUDE_SKILL_DIR}/scripts/compare.py benchmark/{task-name}/

Step 3: Analyze token economics

The key economic insight: agent prompts are a one-time cost per session. Everything after — reasoning, code generation, debugging, retries — costs tokens on every turn. When a micro agent produces correct code, it uses approximately the same total tokens. The savings appear only when it cuts corners.

Our data showed a 57-line agent used 69.5k tokens vs 69.6k for a 3,529-line agent on the same correct solution — prompt size alone does not determine cost.

Step 4: State verdict with evidence

The verdict must be backed by data. Include:

• Which agent won on simple tasks (expected: equivalent)
• Which agent won on complex tasks (expected: full agent)
• Total session cost comparison
• Effective cost comparison (with bug penalty)
• Clear recommendation for when to use each variant

See references/methodology.md for the complete testing methodology with December 2024 data.

Step 5: Clean up

Remove temporary benchmark files and debug outputs. Keep only the comparison report and generated code.

Gate: Report generated with all metrics. Verdict stated with evidence. Report saved to benchmark directory.

Phase 5: OPTIMIZE (optional — invoked explicitly)

Goal: Run an automated optimization loop that improves a markdown target's frontmatter description using trigger-rate eval tasks, then selects the best measured variants through beam search or single-path search.

Invoke when the user says "optimize this skill", "optimize the description", or "run autoresearch". The existing manual A/B comparison (Phases 1-4) remains the path for full agent benchmarking.

See references/optimize-phase.md for the full 9-step procedure, all CLI flags, recommended modes, live eval defaults, current reality check, and optional extensions.

Gate: Optimization complete. Results reviewed. Cherry-picked improvements applied and verified against full task set. Results recorded.

References

• ${CLAUDE_SKILL_DIR}/references/methodology.md: Complete testing methodology with December 2024 data
• ${CLAUDE_SKILL_DIR}/references/grading-rubric.md: Detailed grading criteria and quality checklists
• ${CLAUDE_SKILL_DIR}/references/benchmark-tasks.md: Standard benchmark task descriptions and prompts
• ${CLAUDE_SKILL_DIR}/references/report-template.md: Comparison report template with all required sections
• ${CLAUDE_SKILL_DIR}/references/optimize-phase.md: Full Phase 5 OPTIMIZE procedure (autoresearch loop, CLI flags, beam search, reality check)
• ${CLAUDE_SKILL_DIR}/references/examples-and-errors.md: Error handling for common benchmark failures