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Bounded evaluation

Skill markoblogo/abvx-agent-skills/skills/bounded-evaluation

Reviewable capability layer for coding agents. Portable skills, delivery gates, workflow patterns, and verification-first engineering.

Install
npx -y skills add markoblogo/abvx-agent-skills --skill bounded-evaluation

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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.

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

2.6 KB, as published. Nobody here has run it

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.

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