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/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

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de-anthropocentric-research-engine
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$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill engineering-config-grading --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/engineering-config-grading

Context preview

The summary Claude sees to decide when to auto-load this skill.

(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

SKILL.md

engineering-config-grading.SKILL.md
name: 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.
version: 1.0.0
category: paper-reading
type: sop
execution: subagent
prompt: ./prompt.md
input: 'source_path (string), meta_path (string), dispatched_tool (string)'
reads: 'method, experiments, and appendix sections — where configuration is reported'
output: 'grading_result (list of {item, grade, justification})'
dependencies:
  sops:
  - spawn-agent

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.

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Available SOPs

| SOP | When to use | | --- | --- | | spawn-agent | Spawn a customized CC subagent with full MCP tool access. |

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The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.

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Repo: yogsoth-ai/de-anthropocentric-research-engine