/worst-case-lookup
Take the single most severe domain/item judgment as the overall verdict, for RoB2 (3-value), ROBINS-I (5-value), or AMSTAR-2 (pre-filtered by critical-domain status before worst-case). Use this after domain-level-judgment (for RoB2/ROBINS-I) or quality-appraisal-checklist (for
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill worst-case-lookup --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
/worst-case-lookup
Context preview
The summary Claude sees to decide when to auto-load this skill.
Take the single most severe domain/item judgment as the overall verdict, for RoB2 (3-value), ROBINS-I (5-value), or AMSTAR-2 (pre-filtered by critical-domain status before worst-case). Use this after domain-level-judgment (for RoB2/ROBINS-I) or quality-appraisal-checklist (for
SKILL.md
worst-case-lookup.SKILL.mdname: worst-case-lookup
description: Take the single most severe domain/item judgment as the overall verdict, for RoB2 (3-value), ROBINS-I (5-value), or AMSTAR-2 (pre-filtered by critical-domain status before worst-case). Use this after domain-level-judgment (for RoB2/ROBINS-I) or quality-appraisal-checklist (for AMSTAR-2) has produced per-domain/item judgments — this SOP has two structurally distinct upstream callers and must identify which value domain it received before applying the matching lookup rule. QUADAS-2 never reaches this SOP; it terminates one step earlier at domain-level-judgment.
version: 1.0.0
category: paper-reading
type: sop
execution: subagent
prompt: ./prompt.md
input: 'domain_judgments (list of {domain, judgment}, from RoB2/ROBINS-I) OR checklist_result (from AMSTAR-2) — exactly one of the two'
output: 'overall_judgment (string), which_algorithm (string)'
dependencies:
sops:
- spawn-agentWorst Case Lookup
Overall verdict = most severe domain/item value, on the caller's own scale. Merges what were originally 3 separate SOPs (RoB2-aggregate, ROBINS-I-aggregate, AMSTAR-2-aggregate) per coverage-audit M10's finding that they share one algorithm (worst-case-taking) differing only in value domain and, for AMSTAR-2, an extra pre-filter step — the same parameterization principle already used for unit-classification's label_set parameter, applied consistently here.
Execution
Subagent — spawned via spawn-agent skill.
Two Distinct Callers — Do Not Assume Which
Unlike most SOPs in this package, this one is called from two different places in the graph with two different input shapes (`domain_judgments` vs `checklist_result`). The prompt's Step 1 instruction to identify which was received before proceeding is load-bearing — applying RoB2's worst-case rule to AMSTAR-2's input (or vice versa) silently produces a wrong answer, not an error.
<!-- 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: worst-case-lookup
description: Take the single most severe domain/item judgment as the overall verdict, for RoB2 (3-value), ROBINS-I (5-value), or AMSTAR-2 (pre-filtered by critical-domain status before worst-case). Use this after domain-level-judgment (for RoB2/ROBINS-I) or quality-appraisal-checklist (for AMSTAR-2) has produced per-domain/item judgments — this SOP has two structurally distinct upstream callers and must identify which value domain it received before applying the matching lookup rule. QUADAS-2 never reaches this SOP; it terminates one step earlier at domain-level-judgment.
version: 1.0.0
category: paper-reading
type: sop
execution: subagent
prompt: ./prompt.md
input: 'domain_judgments (list of {domain, judgment}, from RoB2/ROBINS-I) OR checklist_result (from AMSTAR-2) — exactly one of the two'
output: 'overall_judgment (string), which_algorithm (string)'
dependencies:
sops:
- spawn-agentWorst Case Lookup
Overall verdict = most severe domain/item value, on the caller's own scale. Merges what were originally 3 separate SOPs (RoB2-aggregate, ROBINS-I-aggregate, AMSTAR-2-aggregate) per coverage-audit M10's finding that they share one algorithm (worst-case-taking) differing only in value domain and, for AMSTAR-2, an extra pre-filter step — the same parameterization principle already used for unit-classification's label_set parameter, applied consistently here.
Execution
Subagent — spawned via spawn-agent skill.
Two Distinct Callers — Do Not Assume Which
Unlike most SOPs in this package, this one is called from two different places in the graph with two different input shapes (`domain_judgments` vs `checklist_result`). The prompt's Step 1 instruction to identify which was received before proceeding is load-bearing — applying RoB2's worst-case rule to AMSTAR-2's input (or vice versa) silently produces a wrong answer, not an error.
<!-- 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

