# Eval Gate Authoring

> Turn observed run outputs into eval-spec Artifacts, paired Gates, and policy bindings. Use when creating or calibrating automated, human, or LLM-as-judge eval gates for processkit workflows.

- Skill: `projectious-work/eval-gate-authoring` (Agent Skill, multi-file: 5 files)
- Install (CLI): `npx skillmds@latest add projectious-work/eval-gate-authoring`
- Raw SKILL.md: https://api.skillmd.com/api/skills/projectious-work/eval-gate-authoring/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: projectious-work (https://skillmd.com/u/projectious-work)
- Updated: 2026-08-19
- Page: https://skillmd.com/skills/projectious-work/eval-gate-authoring

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# Eval Gate Authoring

## Intro

Eval gate authoring promotes repeated run observations into enforceable
checks. Capture examples, codify an `eval-spec` Artifact, create the
paired Gate, calibrate any LLM judge against human labels, then bind
the eval to the target run surface with a `policy-application` Binding.

## Overview

The workflow starts from observed run output and ends with an auditable
Gate plus bindings that describe where the evaluation applies.
LLM-as-judge evals require calibration evidence before they should be
treated as enforceable.

## Gotchas

- Do not create an eval gate from one example unless the gate is clearly
  marked exploratory.
- Do not treat an LLM judge as calibrated until human labels and a
  calibration LogEntry exist.
- Bind evals to the narrowest useful run surface so a local experiment
  does not accidentally become a global policy.

## Full reference

### MCP tools

- `collect_run_outputs`
- `codify_eval`
- `calibrate_judge`
- `bind_eval_to_runs`

### Produced entities

- `Artifact(spec.kind=eval-spec)`
- `Gate`
- `Binding(type=policy-application)`
- calibration `LogEntry` records