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Patterns and techniques for evaluating and improving AI agent outputs.
Passed 17 of 22 test cases with the skill, and 8 of 22 without it.
and 10 more in the full report
9 fixed8 passed either way5 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
What weight is assigned to confirming that a canonical version of a template exists in the catalog root?
| Checked | Without skill | With skill |
|---|---|---|
| assigns root authoritative template location weight of 0.15 | Missed | Passed |
The instructions your model reads when the skill loads.
Patterns for self-improvement through iterative evaluation and refinement.
Workspace-baseline context: This skill supports the template quality workflow. Use alongside
@Code Review Agent(quality checklist validation),@SpecKit Analyze(cross-artifact consistency), and the Quality Checklist incopilot-instructions.mdto evaluate generated templates against baseline standards.
Evaluation patterns enable agents to assess and improve their own outputs, moving beyond single-shot generation to iterative refinement loops.
Generate → Evaluate → Critique → Refine → Output
↑ │
└──────────────────────────────┘
Agent evaluates and improves its own output through self-critique.
def reflect_and_refine(task: str, criteria: list[str], max_iterations: int = 3) -> str:
"""Generate with reflection loop."""
output = llm(f"Complete this task:\n{task}")
for i in range(max_iterations):
# Self-critique
critique = llm(f"""
test cases passed, vs 8 / 22 without
Published skills whose descriptions closely match this one, with their headline benchmark lift.
More skills from the same source and nearby in the registry, with their headline benchmark lift.