# Agent Evaluation > Implements intelligent agent evaluation with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/agent-evaluation` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/agent-evaluation` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/agent-evaluation/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/agent-evaluation --- # Agent Evaluation Orchestrates intelligent skill selection and execution for agent evaluation workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability. ## TL;DR Checklist - [ ] Parse all inputs at boundary before processing (Law 2) - [ ] Handle edge cases with early returns at function top (Law 1) - [ ] Fail immediately with descriptive errors on invalid states (Law 4) - [ ] Return new data structures, never mutate inputs (Law 3) - [ ] Implement minimum 2-level fallback chain for all skill executions - [ ] Log all skill selections with context for full audit trail - [ ] Validate skill metadata and dependencies before selection - [ ] Update confidence scores after each execution for learning ┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘ User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘ ## When to Use Use this skill when: - Orchestrating multi-step workflows that require skill delegation - Implementing adaptive skill routing based on confidence scores - Building fallback mechanisms for failed skill executions - Creating intelligent task decomposition and parallel execution - Designing skill dependency graphs with automatic resolution - Implementing skill selection with historical performance weighting - Building agent systems that need to self-organize around tasks ## When NOT to Use Avoid this skill for: - Direct task execution without orchestration needs - use individual skills instead - High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive - Simple linear workflows without branching or fallback requirements - Cases where skill metadata is unavailable or unreliable ## Core Workflow 1. **Parse and Analyze Request** - Extract intent, entities, and constraints from user input. **Checkpoint:** All required parameters must be present and in valid format before proceeding. 2. **Score Available Skills** - Calculate match scores using multi-factor algorithm: - Text similarity between request and skill triggers - Historical success rate for similar tasks - Skill availability and health status - Required dependencies and their availability **Checkpoint:** Skip to fallback if no skill scores above threshold. 3. **Select Optimal Skill** - Choose skill with highest score that meets minimum confidence. **Checkpoint:** Verify skill has not been disabled or deprecated. 4. **Execute with Fallback** - Run skill execution wrapped in retry and fallback logic. **Checkpoint:** Log all execution attempts for audit trail. 5. **Return or Fallback** - Either return successful result or apply fallback chain: - Retry with adjusted parameters - Try alternative skill from `related-skills` - Defer to human operator for critical tasks **Checkpoint:** Record outcome with timing and confidence metadata. ## Implementation Patterns ### Pattern 1: Skill Selection Logic ```python def evaluate_agent_output( agent_response: str, evaluation_criteria: List[Dict], benchmark_data: Dict ) -> Dict: """Score an agent's output against multi-factor evaluation criteria. Implements the 5 Laws of Elegant Defense for evaluation: - Early exit on malformed responses or missing criteria - Immutable scoring state to prevent cross-contamination - Fail fast on unresolvable safety/alignment checks Args: agent_response: Raw output from the evaluated agent evaluation_criteria: List of metrics to evaluate (e.g., accuracy, safety, law_adherence) benchmark_data: Reference data for comparison scoring Returns: Evaluation result with per-metric scores and overall confidence """ if not agent_response or not evaluation_criteria: raise ValueError("Agent response and criteria are required for evaluation") # Parse and normalize response for evaluation (Law 2) normalized_response = _normalize_text(agent_response) scores = {} for criterion in evaluation_criteria: metric_name = criterion["name"] try: # Domain-specific scoring logic if metric_name == "law_adherence": scores[metric_name] = _score_law_compliance(normalized_response, benchmark_data) elif metric_name == "accuracy": scores[metric_name] = _calculate_accuracy(normalized_response, benchmark_data.get("ground_truth")) else: scores[metric_name] = _default_metric_score(normalized_response, criterion) except UnresolvableMetricError: # Fail fast on unresolvable metrics (Law 4) scores[metric_name] = {"score": 0.0, "status": "pending_review", "reason": "requires_human_judgment"} # Atomic Predictability (Law 3) - Return fresh evaluation object return { "evaluation_id": generate_uuid(), "scores": scores, "overall_confidence": _compute_weighted_average(scores), "timestamp": time.time(), "status": "complete" } ``` ### Pattern 2: Execution with Fallback ```python def run_evaluation_pipeline( evaluation_task: Dict, agent_outputs: List[Dict], fallback_strategies: Dict ) -> Dict: """Execute multi-agent evaluation with domain-specific fallback chains. Orchestrates the evaluation workflow while applying Elegant Defense principles: - Validates evaluation scope and agent availability before scoring - Applies fallback scoring when direct metrics fail - Maintains immutable evaluation state across pipeline stages Args: evaluation_task: Task definition containing criteria, weights, and scope agent_outputs: List of agent responses to evaluate fallback_strategies: Mapping of metric names to fallback methods Returns: Comprehensive evaluation report with scores, fallback usage, and audit trail """ # Guard clause - validate evaluation scope (Early Exit) if not evaluation_task.get("criteria") or not agent_outputs: raise EvaluationPipelineError("Missing criteria or agent outputs for evaluation") report = { "task_id": evaluation_task["id"], "agent_evaluations": [], "fallback_applied": [], "audit_log": [] } for output in agent_outputs: try: # Execute domain-specific evaluation eval_result = evaluate_agent_output( output["response"], evaluation_task["criteria"], output.get("benchmark_context", {}) ) report["agent_evaluations"].append(eval_result) except MetricResolutionError as e: # Apply evaluation-specific fallback (Law 4) fallback_method = fallback_strategies.get(e.metric_name, "heuristic_estimate") fallback_result = _apply_evaluation_fallback(output, fallback_method) report["fallback_applied"].append({ "agent": output["id"], "metric": e.metric_name, "strategy": fallback_method }) report["agent_evaluations"].append(fallback_result) report["audit_log"].append({ "agent_id": output["id"], "status": "completed", "timestamp": time.time() }) # Finalize report with immutable state report["summary"] = _generate_evaluation_summary(report["agent_evaluations"]) return report ``` ### MUST DO - Always validate skill metadata before selection (Early Exit) - Implement fallback chain with at least 2 levels (Fallback Skill + Human) - Log all skill selections with full context for auditability - Return new data structures instead of mutating inputs (Atomic Predictability) - Fail immediately with descriptive errors on invalid states - Update confidence scores after each execution for adaptive routing - Reference `code-philosophy` (5 Laws of Elegant Defense) in all logic ### MUST NOT DO - Select skills based on a single factor (e.g., only confidence score) - Disable fallback mechanisms "temporarily" - this creates fragile systems - Skip validation of skill dependencies before execution - Return partial results - either complete success or clear failure - Use magic numbers for confidence thresholds - make them configurable - Cache skill selections without considering context changes ## TL;DR Checklist - [ ] Parse all inputs at boundary before processing (Law 2) - [ ] Handle edge cases with early returns at function top (Law 1) - [ ] Fail immediately with descriptive errors on invalid states (Law 4) - [ ] Return new data structures, never mutate inputs (Law 3) - [ ] Implement minimum 2-level fallback chain for all skill executions - [ ] Log all skill selections with context for full audit trail - [ ] Validate skill metadata and dependencies before selection - [ ] Update confidence scores after each execution for learning ## TL;DR for Code Generation - Use guard clauses - return early on invalid input before doing work - Return simple types (dict, str, int, bool, list) - avoid complex nested objects - Cyclomatic complexity < 10 per function - split anything larger - Handle null/empty cases explicitly at function top (Early Exit) - Never mutate input parameters - return new dicts/objects - Fail fast with descriptive errors - don't try to "patch" bad data - Reference code-philosophy laws in comments for complex logic - Include timing and confidence metadata in all return values ## Output Template When applying this skill, produce: 1. **Selected Skills** - List of skill names with confidence scores 2. **Selection Rationale** - Why each skill was chosen (match score, history, availability) 3. **Execution Plan** - Order of execution with dependencies 4. **Fallback Strategy** - Which fallback skills will be tried and in what order 5. **Risk Assessment** - Any potential failure points and their impact 6. **Timing Estimates** - Expected latency including fallback scenarios ## Related Skills | Skill | Purpose | |---|---| | `agentic-evaluation` | Systematic quality evaluation of agent behaviors and outputs | | `agent-reliability-engineering` | Reliability metrics and failure rate tracking for production agents | --- --- ## Constraints ### MUST DO - Define clear input/output contracts for every step in the orchestration flow with explicit validation - Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors - Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach - Validate all preconditions before starting — do not proceed if required resources or permissions are missing ### MUST NOT DO - Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible - Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler - Never use shared mutable state between parallel workflow branches — communicate via immutable messages only - Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies ## Live References > Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content. - [Evaluating LLMs — arXiv Survey (2311.18760)](https://arxiv.org/abs/2311.18760) - [Open LLM Leaderboard — Hugging Face](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) - [Building Effective Agents — Anthropic Research](https://www.anthropic.com/research/building-effective-agents) - [AgentBench: Evaluating LLMs as Agents — arXiv](https://arxiv.org/abs/2308.03688) - [LLM Evaluation Benchmarks — Stanford HELM](https://crfm.stanford.edu/helm/latest/)