/dual-column-self-check
Run one of the ML/CS reproducibility checklists (ML Reproducibility Checklist, REFORMS, NeurIPS Paper Checklist, Model Cards, Datasheets for Datasets) against a paper as a reader-side audit, producing a category (Yes/No/NA) plus free-text reason per item. Use this whenever the
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill dual-column-self-check --agent claude-codeHow 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
/dual-column-self-check
Context preview
The summary Claude sees to decide when to auto-load this skill.
Run one of the ML/CS reproducibility checklists (ML Reproducibility Checklist, REFORMS, NeurIPS Paper Checklist, Model Cards, Datasheets for Datasets) against a paper as a reader-side audit, producing a category (Yes/No/NA) plus free-text reason per item. Use this whenever the
SKILL.md
dual-column-self-check.SKILL.mdname: dual-column-self-check
description: Run one of the ML/CS reproducibility checklists (ML Reproducibility Checklist, REFORMS, NeurIPS Paper Checklist, Model Cards, Datasheets for Datasets) against a paper as a reader-side audit, producing a category (Yes/No/NA) plus free-text reason per item. Use this whenever the user wants a reproducibility/completeness self-check run on an ML or CS paper — invoke this directly, it has no study-design gate in this package since these checklists are engineering self-audits, not clinical-study tools.
version: 1.0.0
category: paper-reading
type: sop
execution: subagent
prompt: ./prompt.md
input: 'source_path (string), meta_path (string), item_set (string — name of the specific checklist)'
reads: 'full paper — a completeness self-audit asks whether each item appears anywhere'
output: 'checklist_result (list of {item, category, reason})'
dependencies:
sops:
- spawn-agentDual Column Self-Check
Category (Yes/No/NA) + free-text reason per item, across 5 ML/CS reproducibility checklists. Originally author-facing self-certification tools, reversed here for reader-side auditing — each item's framing must be flipped to a question before being answered.
Execution
Subagent — spawned via spawn-agent skill.
No upstream gate (intentional, not a gap)
Unlike `quality-appraisal-checklist`/`reporting-standard-checklist`, this SOP has no in-edge from `study-design-tool-gate` in the graph — its 5 checklists are ML/CS engineering self-audits, not tied to a clinical study design, so no study-design dispatch was ever drawn to it (spec §5's flagged note). Do not add a gate dependency here without revisiting that decision explicitly.
<!-- BEGIN available-tables (generated) -->
Available SOPs
| SOP | When to use | | --- | --- | | spawn-agent | Spawn a customized CC subagent with full MCP tool access. |
<!-- END available-tables (generated) -->
Read more
name: dual-column-self-check
description: Run one of the ML/CS reproducibility checklists (ML Reproducibility Checklist, REFORMS, NeurIPS Paper Checklist, Model Cards, Datasheets for Datasets) against a paper as a reader-side audit, producing a category (Yes/No/NA) plus free-text reason per item. Use this whenever the user wants a reproducibility/completeness self-check run on an ML or CS paper — invoke this directly, it has no study-design gate in this package since these checklists are engineering self-audits, not clinical-study tools.
version: 1.0.0
category: paper-reading
type: sop
execution: subagent
prompt: ./prompt.md
input: 'source_path (string), meta_path (string), item_set (string — name of the specific checklist)'
reads: 'full paper — a completeness self-audit asks whether each item appears anywhere'
output: 'checklist_result (list of {item, category, reason})'
dependencies:
sops:
- spawn-agentDual Column Self-Check
Category (Yes/No/NA) + free-text reason per item, across 5 ML/CS reproducibility checklists. Originally author-facing self-certification tools, reversed here for reader-side auditing — each item's framing must be flipped to a question before being answered.
Execution
Subagent — spawned via spawn-agent skill.
No upstream gate (intentional, not a gap)
Unlike `quality-appraisal-checklist`/`reporting-standard-checklist`, this SOP has no in-edge from `study-design-tool-gate` in the graph — its 5 checklists are ML/CS engineering self-audits, not tied to a clinical study design, so no study-design dispatch was ever drawn to it (spec §5's flagged note). Do not add a gate dependency here without revisiting that decision explicitly.
<!-- BEGIN available-tables (generated) -->
Available SOPs
| SOP | When to use | | --- | --- | | spawn-agent | Spawn a customized CC subagent with full MCP tool access. |
<!-- END available-tables (generated) -->
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.
Repo: yogsoth-ai/de-anthropocentric-research-engine
Other skills on de-anthropocentric-research-engine.
- /formated-results
Closing skill for the research-executor, loaded as the last step of formated-specs. Summarize the design just produced into one research-result JSON fenced block in your reply. Do not execute the research.
Open skill - /formated-specs
Spec-slot skill for the research-executor. Emit the 4-layer DARE orchestration of the assigned topic as one research-graph JSON fenced block in your reply. Replaces the generic spec-writing step.
Open skill - /injection-fidelity
Loss-1 judge (codex role). Given one sample's de-identified dialogue and its PolicyCard, decide axis-by-axis whether the user-simulator enacted the card's per-axis pressure. Judge enactment of the card, never whether the research is good.
Open skill - /ladder-quality-order
Loss-2 judge (codex role). Over one topic's 6 shuffled research-design samples, pairwise-rank by quality using the D1–D5 standard. Emit the pairwise log; the harness computes the order and the ladder verdicts. Judge quality difference, never against academic standards.
Open skill - /optimization-loop
The optimizer brain for the ladder-foundry pretraining loop. Runs the two-level nested batch loop, delegates gating to gate_eval, attributes a failing batch to one weight (attribute-first), and recovers from disk after compaction. Control flow is fully scripted; only the
Open skill - /acu-nugget-recall
Tactic: Extract atomic units from one paper and score how much of a caller-supplied summary covers. Use for ACU-style binary or Nugget-style ternary recall checks; cannot run without a target summary.
Open skill

