/domain-level-judgment
Fold raw signalling-question answers into domain-level judgments for RoB2, ROBINS-I, or QUADAS-2, per each tool's own lookup rules — the first of two aggregation levels these tools define. QUADAS-2 is dual-axis (risk-of-bias AND applicability-concern per domain, D1-D3) and
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill domain-level-judgment --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
/domain-level-judgment
Context preview
The summary Claude sees to decide when to auto-load this skill.
Fold raw signalling-question answers into domain-level judgments for RoB2, ROBINS-I, or QUADAS-2, per each tool's own lookup rules — the first of two aggregation levels these tools define. QUADAS-2 is dual-axis (risk-of-bias AND applicability-concern per domain, D1-D3) and
SKILL.md
domain-level-judgment.SKILL.mdname: domain-level-judgment
description: Fold raw signalling-question answers into domain-level judgments for RoB2, ROBINS-I, or QUADAS-2, per each tool's own lookup rules — the first of two aggregation levels these tools define. QUADAS-2 is dual-axis (risk-of-bias AND applicability-concern per domain, D1-D3) and terminates here with no further rollup; RoB2/ROBINS-I continue on to worst-case-lookup for an overall verdict. Use this after signalling-question-answering has produced the raw answers.
version: 1.0.0
category: paper-reading
type: sop
execution: subagent
prompt: ./prompt.md
input: 'signalling_answers (list of {domain, question, answer}), dispatched_tool (string)'
output: 'domain_judgments (list of {domain, judgment} or, for QUADAS-2, {domain, risk_of_bias_judgment, applicability_concern_judgment})'
dependencies:
sops:
- spawn-agentDomain Level Judgment
Signalling answers → domain-level judgments via each tool's own lookup rules. QUADAS-2's dual-axis output (risk-of-bias + applicability-concern per domain) terminates here — no further rollup exists for it. RoB2/ROBINS-I continue to worst-case-lookup.
Execution
Subagent — spawned via spawn-agent skill.
Why This SOP Exists (coverage-audit S4)
The original graph connected signalling-question-answering directly to an overall-judgment node, with no place for the first-level domain rollup RoB2/ROBINS-I/QUADAS-2 all define algorithmically before any overall verdict — and no place at all for QUADAS-2's terminal dual-axis output, since QUADAS-2 never reaches a "worst case across domains" step the way RoB2/ROBINS-I do.
<!-- 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: domain-level-judgment
description: Fold raw signalling-question answers into domain-level judgments for RoB2, ROBINS-I, or QUADAS-2, per each tool's own lookup rules — the first of two aggregation levels these tools define. QUADAS-2 is dual-axis (risk-of-bias AND applicability-concern per domain, D1-D3) and terminates here with no further rollup; RoB2/ROBINS-I continue on to worst-case-lookup for an overall verdict. Use this after signalling-question-answering has produced the raw answers.
version: 1.0.0
category: paper-reading
type: sop
execution: subagent
prompt: ./prompt.md
input: 'signalling_answers (list of {domain, question, answer}), dispatched_tool (string)'
output: 'domain_judgments (list of {domain, judgment} or, for QUADAS-2, {domain, risk_of_bias_judgment, applicability_concern_judgment})'
dependencies:
sops:
- spawn-agentDomain Level Judgment
Signalling answers → domain-level judgments via each tool's own lookup rules. QUADAS-2's dual-axis output (risk-of-bias + applicability-concern per domain) terminates here — no further rollup exists for it. RoB2/ROBINS-I continue to worst-case-lookup.
Execution
Subagent — spawned via spawn-agent skill.
Why This SOP Exists (coverage-audit S4)
The original graph connected signalling-question-answering directly to an overall-judgment node, with no place for the first-level domain rollup RoB2/ROBINS-I/QUADAS-2 all define algorithmically before any overall verdict — and no place at all for QUADAS-2's terminal dual-axis output, since QUADAS-2 never reaches a "worst case across domains" step the way RoB2/ROBINS-I do.
<!-- 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