/answering-sequence-design
SOP: Design the optimal answering order for sub-questions
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill answering-sequence-design --agent claude-codeHow it fires
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/answering-sequence-design
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SOP: Design the optimal answering order for sub-questions
SKILL.md
answering-sequence-design.SKILL.mdname: answering-sequence-design
description: 'SOP: Design the optimal answering order for sub-questions'
version: 1.0.0
category: hypothesis-formation
type: sop
campaign: research-question
input: Sub-question list + dependency graph
output: Execution sequence + rationale + parallelization opportunities
dependencies:
skills:
- subagent-spawning
Answering Sequence Design
Design the optimal answering order for sub-questions — based on dependency relationships and resource efficiency.
HARD-GATE
<HARD-GATE> Input must contain: sub-question list + dependency graph (from dependency-mapping). </HARD-GATE>
Pipeline
1. **Precondition check**: is the dependency graph acyclic 2. **Topological sort**: determine the basic order based on dependency relationships 3. **Parallel grouping**: identify sub-questions that can proceed simultaneously 4. **Resource optimization**: adjust the order considering resource constraints 5. **Risk ordering**: prioritize high-risk/high-uncertainty items (fail fast) 6. **Final sequence**: determine the optimal sequence by integrating the above factors 7. **Output**: execution sequence + phased plan + parallelization opportunities
Output Format
Phase 1 (parallel): [SQ1, SQ3] — no mutual dependencies
Phase 2 (sequential): [SQ2] — depends on SQ1
Phase 3 (parallel): [SQ4, SQ5] — depend on SQ2
Rationale: [why this order is optimal]
Risk note: [which sub-questions, if they fail, will affect subsequent ones]
</output>
Read more
name: answering-sequence-design description: 'SOP: Design the optimal answering order for sub-questions' version: 1.0.0 category: hypothesis-formation type: sop campaign: research-question input: Sub-question list + dependency graph output: Execution sequence + rationale + parallelization opportunities dependencies: skills: - subagent-spawning
Answering Sequence Design
Design the optimal answering order for sub-questions — based on dependency relationships and resource efficiency.
HARD-GATE
<HARD-GATE> Input must contain: sub-question list + dependency graph (from dependency-mapping). </HARD-GATE>
Pipeline
1. **Precondition check**: is the dependency graph acyclic 2. **Topological sort**: determine the basic order based on dependency relationships 3. **Parallel grouping**: identify sub-questions that can proceed simultaneously 4. **Resource optimization**: adjust the order considering resource constraints 5. **Risk ordering**: prioritize high-risk/high-uncertainty items (fail fast) 6. **Final sequence**: determine the optimal sequence by integrating the above factors 7. **Output**: execution sequence + phased plan + parallelization opportunities
Output Format
Phase 1 (parallel): [SQ1, SQ3] — no mutual dependencies Phase 2 (sequential): [SQ2] — depends on SQ1 Phase 3 (parallel): [SQ4, SQ5] — depend on SQ2 Rationale: [why this order is optimal] Risk note: [which sub-questions, if they fail, will affect subsequent ones]
</output>
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