/reforms-grading
Tactic: Grade an ML/CS paper''s reproducibility configuration reporting as complete, partial, or none after checking that clinical appraisal tools do not apply. Use when the question is whether the work can be rerun.
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill reforms-grading --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
/reforms-grading
Context preview
The summary Claude sees to decide when to auto-load this skill.
Tactic: Grade an ML/CS paper''s reproducibility configuration reporting as complete, partial, or none after checking that clinical appraisal tools do not apply. Use when the question is whether the work can be rerun.
SKILL.md
reforms-grading.SKILL.mdname: reforms-grading
description: 'Tactic: Grade an ML/CS paper''s reproducibility configuration reporting as complete, partial, or none after checking that clinical appraisal tools do not apply. Use when the question is whether the work can be rerun.'
version: 1.0.0
category: paper-reading
type: tactic
execution: tactic
input: 'paper_ref (string — title, arXiv ID, DOI, URL, or local .md/.txt/.pdf path)'
output: '01-study-design-tool-gate.json and 02-engineering-config-grading.json under context/papers/<dir>/reforms-grading/'
sops:
- paper-fetch
- study-design-tool-gate
- engineering-config-grading
dependencies:
sops:
- paper-fetch
- study-design-tool-gate
- engineering-config-grading
REFORMS Grading
Orchestration Pattern
1. Fetch the paper; stop on `not_found`. 2. Run `study-design-tool-gate` and write its verdict to `01-study-design-tool-gate.json`. 3. If it selects a clinical/review instrument, stop and name that tool. If it selects `engineering-config-grading` or returns `not_applicable`, proceed. 4. Run `engineering-config-grading`, using that tool name when the gate returned `not_applicable`, and write `02-engineering-config-grading.json`.
Record `proposal_sop: true`. Each justification must state what complete reporting would look like before grading the paper. Report the gate verdict, complete/partial/none counts, every `none` item, the unverified-proposal caveat, and both paths.
Read more
name: reforms-grading description: 'Tactic: Grade an ML/CS paper''s reproducibility configuration reporting as complete, partial, or none after checking that clinical appraisal tools do not apply. Use when the question is whether the work can be rerun.' version: 1.0.0 category: paper-reading type: tactic execution: tactic input: 'paper_ref (string — title, arXiv ID, DOI, URL, or local .md/.txt/.pdf path)' output: '01-study-design-tool-gate.json and 02-engineering-config-grading.json under context/papers/<dir>/reforms-grading/' sops: - paper-fetch - study-design-tool-gate - engineering-config-grading dependencies: sops: - paper-fetch - study-design-tool-gate - engineering-config-grading
REFORMS Grading
Orchestration Pattern
1. Fetch the paper; stop on `not_found`. 2. Run `study-design-tool-gate` and write its verdict to `01-study-design-tool-gate.json`. 3. If it selects a clinical/review instrument, stop and name that tool. If it selects `engineering-config-grading` or returns `not_applicable`, proceed. 4. Run `engineering-config-grading`, using that tool name when the gate returned `not_applicable`, and write `02-engineering-config-grading.json`.
Record `proposal_sop: true`. Each justification must state what complete reporting would look like before grading the paper. Report the gate verdict, complete/partial/none counts, every `none` item, the unverified-proposal caveat, and both paths.
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