/signalling-question-answering
Answer per-domain signalling questions (5-value scale: Yes/Probably yes/Probably no/No/No information) for RoB2, ROBINS-I, or QUADAS-2, per whichever variant study-design-tool-gate dispatched to. Use this after study-design-tool-gate has dispatched to one of these three tools;
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill signalling-question-answering --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
/signalling-question-answering
Context preview
The summary Claude sees to decide when to auto-load this skill.
Answer per-domain signalling questions (5-value scale: Yes/Probably yes/Probably no/No/No information) for RoB2, ROBINS-I, or QUADAS-2, per whichever variant study-design-tool-gate dispatched to. Use this after study-design-tool-gate has dispatched to one of these three tools;
SKILL.md
signalling-question-answering.SKILL.mdname: signalling-question-answering
description: 'Answer per-domain signalling questions (5-value scale: Yes/Probably yes/Probably no/No/No information) for RoB2, ROBINS-I, or QUADAS-2, per whichever variant study-design-tool-gate dispatched to. Use this after study-design-tool-gate has dispatched to one of these three tools; this SOP produces only the raw signalling answers, not any domain-level or overall roll-up — that happens in domain-level-judgment next.'
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 and results sections — signalling questions ask what was done, not what it meant'
output: 'signalling_answers (list of {domain, question, answer})'
dependencies:
sops:
- spawn-agentSignalling Question Answering
Raw per-domain 5-value signalling answers for RoB2/ROBINS-I/QUADAS-2. First of two algorithmic levels these tools require — see domain-level-judgment for the second.
Execution
Subagent — spawned via spawn-agent skill.
Scope Note (per coverage-audit M8)
NOS's star-awarding and AMSTAR-2's checklist items are NOT this SOP's concern — an earlier graph draft conflated "answer a domain question" with "award a star" and "check a checklist item," but these are three structurally different actions (5-value signalling judgment vs. binary star-or-not vs. checklist Yes/No/Partial). Keep this SOP scoped to exactly RoB2/ROBINS-I/QUADAS-2's signalling questions.
<!-- 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: signalling-question-answering
description: 'Answer per-domain signalling questions (5-value scale: Yes/Probably yes/Probably no/No/No information) for RoB2, ROBINS-I, or QUADAS-2, per whichever variant study-design-tool-gate dispatched to. Use this after study-design-tool-gate has dispatched to one of these three tools; this SOP produces only the raw signalling answers, not any domain-level or overall roll-up — that happens in domain-level-judgment next.'
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 and results sections — signalling questions ask what was done, not what it meant'
output: 'signalling_answers (list of {domain, question, answer})'
dependencies:
sops:
- spawn-agentSignalling Question Answering
Raw per-domain 5-value signalling answers for RoB2/ROBINS-I/QUADAS-2. First of two algorithmic levels these tools require — see domain-level-judgment for the second.
Execution
Subagent — spawned via spawn-agent skill.
Scope Note (per coverage-audit M8)
NOS's star-awarding and AMSTAR-2's checklist items are NOT this SOP's concern — an earlier graph draft conflated "answer a domain question" with "award a star" and "check a checklist item," but these are three structurally different actions (5-value signalling judgment vs. binary star-or-not vs. checklist Yes/No/Partial). Keep this SOP scoped to exactly RoB2/ROBINS-I/QUADAS-2's signalling questions.
<!-- 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

