# Engineering Config Grading > (Proposal, unverified) Grade reproducibility-relevant engineering configuration items (hyperparameter search range, compute budget, seed handling, dataset splits) on a complete/partial/none scale, requiring the grader to first define what "complete" means per item before judging against it. Use this after study-design-tool-gate has dispatched an ML/CS engineering paper here; this is a graded QUALITY judgment, distinct from dual-column-self-check's binary Yes/No/NA self-audit checklists. - Skill: `yogsoth-ai/engineering-config-grading` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add yogsoth-ai/engineering-config-grading` - Raw SKILL.md: https://api.skillmd.com/api/skills/yogsoth-ai/engineering-config-grading/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: yogsoth-ai (https://skillmd.com/u/yogsoth-ai) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/yogsoth-ai/engineering-config-grading --- # Engineering Config Grading (Proposal) Graded (not binary) reproducibility-config quality judgment. Fills the quality-judgment × engineering-metadata gap in the evaluative-stance × content-layer matrix (spec §2, matrix-generation phase). Per coverage-audit M14: an earlier draft folded this into dual-column-self-check via a value-domain toggle alone, which dropped the actual judgment-defining action (establishing what "complete" means) that distinguishes this from a binary checklist. ## Execution Subagent — spawned via spawn-agent skill. ## Proposal Status — Read Before Modifying No primary-source precedent (unlike NOS, which it's structurally modeled after but applies to a different content layer). Keep "(Proposal, unverified)" in the description until real usage validates the method. ## Available SOPs | SOP | When to use | | --- | --- | | spawn-agent | Spawn a customized CC subagent with full MCP tool access. |