Bounded Evaluation

Use this skill when a change needs a small, explicit evaluation gate.

Use When

• reviewing skill changes, AGENTS.md rules, prompt playbooks, or SET bundle changes;
• comparing two agent outputs or proposals;
• using an LLM judge where bias or weak rubrics could mislead;
• creating activation tests, regression cases, or ship gates for agent behavior;
• deciding whether a repeated workflow improved after a bounded edit.

Evaluation Contract

A bounded eval must state:

• target artifact or behavior;
• candidate outputs or versions;
• rubric dimensions and weights, if any;
• pass/fail threshold;
• fixtures or scenarios;
• judge type: deterministic check, human review, LLM judge, or hybrid;
• known bias risks and mitigation.

Pairwise And Judge Rules

• Compare A/B outputs against the same rubric.
• Swap candidate order when using an LLM judge to detect position bias.
• Require reasons before scores, but treat reasons as evidence to inspect, not truth.
• Mark the eval invalid when swapped order changes the winner without a defensible reason.
• Prefer deterministic checks for syntax, schema, file existence, and command success.
• Keep human approval for high-stakes or ambiguous decisions.

Workflow

1. Define the smallest behavior being evaluated.
2. Pick 1-5 representative fixtures or scenarios.
3. Choose deterministic checks before LLM judging.
4. If using pairwise judging, run both candidate orders.
5. Record results, invalidations, and decision.
6. Feed accepted/rejected outcomes back into the relevant ledger.

Output Shape

Use:

• eval target;
• fixtures;
• rubric;
• judge type;
• results;
• bias checks;
• decision: accept, reject, revise, or invalid;
• follow-up artifact or ledger update.

Guardrails

• Do not publish benchmark claims without committed fixtures, arms, model, host, run count, and captured artifacts.
• Do not let an LLM judge approve its own rubric changes.
• Do not use pairwise preference as proof of correctness.
• Do not hide invalid or inconsistent judge results.

Provenance

Adapted from advanced-evaluation patterns in muratcankoylan/Agent-Skills-for-Context-Engineering, aligned with ABVX SkillOpt validation gates.