--- name: yogsoth-ai-engineering-config-grading description: "(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." license: "Apache-2.0" metadata: decimalai-skill-id: "1c1f2599-91f5-480e-addf-4a084c9a2ab2" decimalai-version-id: "5b019444-f3c1-4a6d-b468-9f4b72f62fc7" decimalai-version: "1" decimalai-body-sha256: "eee8299976dbbc3af64cb22fa0f9593cd0d142d247c9219211c5be659620a89b" decimalai-name: "yogsoth-ai/engineering-config-grading" decimalai-format: "1" decimalai-invocation: "model" decimalai-category: "research" decimalai-source: "https://app.decimal.ai/s/yogsoth-ai-engineering-config-grading@1/SKILL.md" --- # 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. |