Research

Strategy for extracting claims from source material — identify propositions,

Strategy: Run BEFORE building any validator (sandbox/simulation/benchmark). Builds a non-circularity matrix of theory-claim × validator-assumption to detect when a validator would 'confirm' a theory only because it was built on the theory's own premises. A circular validator's PASS carries zero evidential weight. Methods: Cartwright nomological machines, Winsberg sanctioning-of-simulations, tautology detection.

Campaign for building causal models — identify variables, map mechanisms,

SOP for tracing causal chains — follow edges from cause to effect through

SOP for validating that candidate axes are independent and meaningful.

Tactic for systematically extracting axes of variation from literature

SOP for generating argument structure visualization — query graph for

Strategy for synthesizing argument positions — aggregate evidence, resolve

Campaign for mapping argument structures — extract claims, link evidence,

SOP: Run the external ARA rigor-reviewer (Seal Level 2, six-dimension semantic review) over ../ara/ and pass its level2_report.json to the user

Campaign: Compile a context/ research record into an ARA (Agent-Native Research Artifact) and run a Level-2 epistemic review — no LaTeX, no narrative paper

SOP: Turn the feeding plan into the compiler''s $ARGUMENTS and run the external ARA compiler once inline to produce ../ara/

SOP for detecting and resolving concept aliases — merge duplicate pages,

'Strategy: Dialectic engine retuned for truth-seeking, not survival.

Reconstruct a DARE skill repo's true use-dependency relations and render them as a self-contained, offline, Obsidian-style interactive HTML graph (pyvis / vis-network). Use this whenever the user wants to graph / map / visualize the skill dependencies of a repo or package, "画依赖图 / graph 化这个 repo / 把 skill 连边画出来 / 用 pyvis 出个图 / skill 关系图", or to audit how campaign→strategy→ tactic→sop skills connect. Trigger even if the user just says "给这个 package 做个图" without naming pyvis or HTML. Goes straig...

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 reache...

Split a paper's text into sentence- or clause-level units (with character offsets) for downstream classification, at a caller-specified granularity and scope (full text, abstract-only, or intro-only). Use this as the mandatory first step whenever any sentence/clause-level classification method (Argumentative Zoning, CoreSC, PubMed-RCT, CSAbstruct, Swales move analysis, CODA-19) needs its input pre-segmented — always precedes unit-classification.

Classify each pre-segmented text unit independently against a fixed label set (Argumentative Zoning, CoreSC, PubMed-RCT, Swales move/step, CODA-19, TDMS, or CSFCube's facet labels), single-layer with no cross-unit dependency. Use this after unit-segmentation has split the text, whenever a sentence- or clause-level rhetorical/functional classification is needed; do not use this for methods requiring document-level coreference reasoning (see multi-stage-cascade-extraction instead).

Keshav's third pass — the heaviest of the three, a full sentence-by-sentence re-read including proofs/derivations, attempting a virtual re-implementation of the paper to surface implicit assumptions and concrete improvement points. Use this after second-pass-grasp, as the terminal step of the Keshav three-pass method, whenever genuine mastery of a paper (not just a summary) is needed. This is not a skippable recap — treat "nothing new to add" as suspicious, not a default outcome.

Fill a paper's reported values into an already-given comparison-template attribute schema (e.g. Task/Dataset/Metric/Value) — the executable half of ORKG's comparison-template method. Use this when a template's attribute schema is already fixed and you need one paper's row filled in; this does NOT build new templates (that half is a human-curator task, out of scope).

Sum NOS's item-level stars and bucket into good (≥7)/fair (4-6)/poor (≤3) — a fixed threshold lookup, structurally distinct from worst-case-lookup's take-the-worst-value approach. Use this after star-awarding has produced the per-item stars; this is NOS's terminal step.

Classify a paper's study design (RCT, cohort, case-control, diagnostic-accuracy, systematic-review, animal-study, prediction-model, etc., or not_applicable) and dispatch to the correct downstream bias-risk/quality/reporting tool and specific variant (CASP has 8 variants, JBI ~6, RoB2 has parallel/cluster/crossover versions). Use this as the mandatory first step before running ANY of CASP, JBI, AMSTAR-2, NOS, RoB2, ROBINS-I, QUADAS-2, CONSORT, STROBE, ARRIVE, SPIRIT, TRIPOD, or engineering-con...

Award NOS's (Newcastle-Ottawa Scale) stars item-by-item across Selection (up to 4), Comparability (up to 2), and Outcome/Exposure (up to 3) — a binary award-or-not action per item, distinct from a 5-value signalling judgment. Use this after study-design-tool-gate has dispatched to NOS, as the first step before sum-threshold-scoring.

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.