# Bounded Evaluation

> Design small evaluation gates for agent outputs, skill changes, prompts, and tool contracts. Use when a change needs rubric checks, pairwise comparison, position-bias mitigation, regression fixtures, or LLM-as-judge discipline without building a heavy eval platform.

- Skill: `markoblogo/bounded-evaluation` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add markoblogo/bounded-evaluation`
- Raw SKILL.md: https://api.skillmd.com/api/skills/markoblogo/bounded-evaluation/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: MIT
- Author: markoblogo (https://skillmd.com/u/markoblogo)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/markoblogo/bounded-evaluation

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# 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.