yogsoth-ai
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- ▌ First Pass Skim 2 · yogsoth-ai bundleFive-minute skim pass over a single academic paper (title, abstract, headings, figures, conclusion only) to classify the paper's type and draft candidate public-audience angles before any deep reading happens. Use this as the entry point whenever the user gives you one specific paper to summarize, explain, or turn into a WeChat/blog article, and you haven't classified the paper yet. Always run this before second-pass-grasp.
- ▌ Second Pass Grasp 2 · yogsoth-ai bundleFull-text deep-read of a paper's Introduction, Method, and Results sections to draft the structured bundle (problem, method, key results with hedge levels, limitations), each field carrying a precise source anchor. Use this after first-pass-skim has classified the paper and drafted candidate angles — this is the main content-extraction pass of the reading pipeline.
- ▌ Deep Insight Web Search · yogsoth-aiQuick web scanning for landscape understanding. Import of web-browsing/web-search skill. Snippets only — no conclusions from snippets alone.
- ▌ Provocation Generation · yogsoth-aiGenerate PO provocations and extract constructive movement. Orchestrates assumption surfacing → provocation creation → movement extraction → idea formation.
- ▌ Intake · yogsoth-aiLightweight intent clarification dialogue — only invoked when the entry point cannot determine the correct strategy from context alone. Maximum 3 questions. Pure routing, not profiling.
- ▌ Paper Overview · yogsoth-aiAbstract-level paper scanning for broad coverage. Import of literature-engine/literature-overview skill. Abstract-level only — no methodology conclusions from abstracts.
- ▌ Qalmri · yogsoth-ai bundleProduce a six-slot QALMRI worksheet (Question, Alternatives, Logic, Method, Results, Inference) as free-text notes on one paper — a structured note-taking format, not a scored evaluation. Use this whenever the user wants a QALMRI-style reading worksheet for a specific paper.
- ▌ Paper Fetch · yogsoth-ai bundleRetrieve one specified academic paper (by title, arXiv ID, DOI, URL, or a local .md/.txt/.pdf path the caller already has) and land it on disk as source.md plus a source.meta.json carrying a line-number section index. Checks context/papers/ for an existing copy first; local files and direct PDF URLs are read directly with no search at all, while other references use alphaxiv, Semantic Scholar routing, then bioRxiv/medRxiv. Use this as the mandatory first step whenever any other paper-reading SOP in this package needs the actual text of a paper — it is the sole entry point of the pipeline and every downstream SOP reads the files it lands, so do not bypass it even when the caller already has the file. If it returns not_found, halt immediately; do not fabricate content or guess at the paper's likely contents.
- ▌ Claim Writing · yogsoth-ai bundleBlind-rewrite a citing sentence (citance) from another paper into a single atomic, independently-verifiable claim (SciFact's annotation protocol) — never looking at the cited paper's content while rewriting. Use this when you have a specific citing sentence and want it decomposed into checkable atomic claims, as the first step before rationale-selection and claim-label-prediction.
- ▌ Star Awarding · yogsoth-ai bundleAward 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.
- ▌ First Pass Skim · yogsoth-ai bundleKeshav's first pass over one paper — a 5-10 minute skim of title, abstract, headings, figures, and conclusion only, producing skim notes and a read-deeper judgment. Use this as the first step whenever a paper is being read via the Keshav three-pass method; always precedes second-pass-grasp and never reads section bodies itself.
- ▌ Reforms Grading · yogsoth-aiTactic: Grade an ML/CS paper's reproducibility configuration reporting as complete, partial, or none after checking that clinical appraisal tools do not apply. Use when the question is whether the work can be rerun.
- ▌ Qalmri Worksheet · yogsoth-aiTactic: Fill a six-slot QALMRI worksheet for one paper: Question, Alternatives, Logic, Method, Results, and Inference. Use for a structured reading worksheet rather than a graded evaluation.
- ▌ Question Framing · yogsoth-ai bundleFill a slot-based question-framing schema (PICO, PECO, or SPIDER) from a paper's stated research question. Use this whenever the user wants a paper's research question structured into one of these standard clinical/qualitative-research question frames; this frames what question is being asked, it does not read or evaluate the paper's content otherwise.
- ▌ Acu Nugget Recall · yogsoth-aiTactic: 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.
- ▌ Keshav Three Pass · yogsoth-aiTactic: Read one paper by Keshav's three-pass method — a shallow skim, a contribution-grasping full read, then a deep virtual re-implementation. Use when the goal is understanding a paper rather than extracting a fixed schema.
- ▌ Second Pass Grasp · yogsoth-ai bundleKeshav's second pass — a careful full read (ignoring proof/derivation detail) producing prose-level understanding sufficient to explain the paper's main contribution and evidence to a colleague. Use this after first-pass-skim, as the main content-grasping pass of the Keshav three-pass method; do not force its output into a structured data schema.
- ▌ Unit Segmentation · yogsoth-ai bundleSplit 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.
- ▌ Worst Case Lookup · yogsoth-ai bundleTake 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.
- ▌ Qasper Evidence QA · yogsoth-ai bundleAnswer a specific question about a paper, grounding the answer in exact quoted evidence spans from the text (QASPER-style question-driven QA with span-level evidence, no schema categorization). Use this whenever the user asks a specific factual question about a paper and wants the answer traceable to exact text spans.
- ▌ Atomic Unit Writing · yogsoth-ai bundleExtract (ACU-style) or freshly author (Nugget-style) a list of atomic content units from a paper, optionally tagged vital/okay for importance. Use this as the first step whenever building a reference set of atomic facts for later recall-checking a summary or abstract against the paper — always precedes atomic-unit-matching.
- ▌ Rationale Selection · yogsoth-ai bundleSelect the minimal set of 1-3 verbatim sentences from a candidate paper/abstract sufficient to entail or refute an atomic claim (SciFact's rationale-selection step). Use this after claim-writing has produced an atomic claim, as the evidence-gathering step before claim-label-prediction; an empty rationale set is a valid outcome, not an error.
- ▌ Unit Classification · yogsoth-ai bundleClassify 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).
- ▌ Argumentative Zoning · yogsoth-aiTactic: Label every sentence of one paper with its rhetorical role using Argumentative Zoning. Use when fixed rhetorical labels and cross-paper alignment matter.
- ▌ Atomic Unit Matching · yogsoth-ai bundleJudge, per atomic content unit, whether a target text (summary, abstract, or other candidate text) contains it — binary present/absent (ACU) or ternary support/partial_support/not_support (Nugget), per caller's value domain. Use this after atomic-unit-writing has produced the reference units, as the matching step before recall aggregation.
- ▌ Third Pass Deep Read · yogsoth-ai bundleKeshav'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.
- ▌ Domain Level Judgment · yogsoth-ai bundleFold 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.
- ▌ Sum Threshold Scoring · yogsoth-ai bundleSum 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.
- ▌ Template Slot Filling · yogsoth-ai bundleFill 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).
- ▌ Claim Label Prediction · yogsoth-ai bundleJudge a three-way SUPPORTS/REFUTES/NOINFO label for an atomic claim, based only on its selected rationale sentences (SciFact's final classification step). Use this after rationale-selection has produced the evidence sentences — this is the terminal step of the SciFact chain, producing the complete (claim, abstract, label, rationale) tuple.
- ▌ Dual Column Self Check · yogsoth-ai bundleRun one of the ML/CS reproducibility checklists (ML Reproducibility Checklist, REFORMS, NeurIPS Paper Checklist, Model Cards, Datasheets for Datasets) against a paper as a reader-side audit, producing a category (Yes/No/NA) plus free-text reason per item. Use this whenever the user wants a reproducibility/completeness self-check run on an ML or CS paper — invoke this directly, it has no study-design gate in this package since these checklists are engineering self-audits, not clinical-study tools.
- ▌ Study Design Tool Gate · yogsoth-ai bundleClassify 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-config-grading — these tools are all study-design-conditional and picking the wrong variant produces meaningless results. It is entirely correct and common for this gate to determine that none of these medically-descended tools applies (e.g. most CS/ML papers) — that is a valid, complete answer, not a failure.
- ▌ Engineering Config Grading · yogsoth-ai bundle(Proposal, unverified) Grade reproducibility-relevant engineering configuration items (hyperparameter search range, compute budget, seed handling, dataset splits) on a complete/partial/none scale, requiring the grader to first define what "complete" means per item before judging against it. Use this after study-design-tool-gate has dispatched an ML/CS engineering paper here; this is a graded QUALITY judgment, distinct from dual-column-self-check's binary Yes/No/NA self-audit checklists.
- ▌ Quality Appraisal Checklist · yogsoth-ai bundleRun CASP (8 study-type variants), JBI (~6 variants), or AMSTAR-2 quality-appraisal checklists — each ending in the tool's own required integrated judgment, not just item tallies. Also runs a proposal "rhetorical-completeness-check" mode (entry_mode="completeness_check") that instead diffs unit-classification's rhetorical labels against a target checklist's expected label set. Use this after study-design-tool-gate has dispatched to CASP/JBI/AMSTAR-2 (mode a), or directly after unit-classification when checking for missing argumentative moves (mode b, proposal/unverified).
- ▌ Research Question Appraisal · yogsoth-ai bundleJudge a paper's stated research question against the FINER criteria (Feasible, Interesting, Novel, Ethical, Relevant) — five independent judgments with justification, evaluating the question itself, not the paper's results. Use this whenever the user wants to know whether a paper is asking a good research question, distinct from whether it answered that question well.
- ▌ Atomic Unit Recall Aggregate · yogsoth-ai bundleAggregate per-unit ACU/Nugget match judgments into a final recall score — normalized length-penalized recall for ACU, or V_strict/A_strict (+ run-level ranking, with an explicit per-topic-unreliability caveat) for Nugget. Use this as the final step of the atomic-unit chain, after atomic-unit-matching; this SOP's existence closes a gap the original pipeline design was missing — without it, per-unit match judgments were never actually summed into the score the source methodologies report.
- ▌ Reporting Standard Checklist · yogsoth-ai bundleCheck whether a paper reports each item from PRISMA, CONSORT, STROBE, ARRIVE, SPIRIT, or TRIPOD (per whichever study-design-tool-gate dispatched to), citing where each item is or isn't addressed — including a/b sub-item hierarchy where the standard defines one. Use this after study-design-tool-gate has dispatched to one of these 6 reporting standards; this checks report completeness (did they say where), not methodological quality (was the study done well) — there is no overall synthesis step, judgment per item is the terminal output.
- ▌ Rhetorical Structure Quality · yogsoth-ai bundle(Proposal, unverified) Judge whether argumentative relations between unit-classification's rhetorical labels actually hold in a paper (e.g. is an AIM label adequately substantiated by BACKGROUND labels) — a second-order quality judgment over already-classified units, not raw text. Use this after unit-classification has labeled a paper's units with a rhetorical/argumentative label set, when the user wants to know if the paper's argument structure is actually sound, not just what role each sentence plays.
- ▌ Signalling Question Answering · yogsoth-ai bundleAnswer 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.
- ▌ Multi Stage Cascade Extraction · yogsoth-ai bundleRun a multi-stage extraction cascade (mention detection, document-level coreference clustering, optional saliency judgment, N-ary relation/triple extraction) directly over a paper's full text — covers SciERC, SciREX, and NLP Contribution Graph. Use this whenever cross-sentence or document-level entity/relation extraction is needed (e.g. SciREX-style Task-Dataset-Metric-Score tuples); do NOT use unit-classification for this, since these methods reason over the whole document's mentions, not independently-classified sentence units.
- ▌ Deep Read · yogsoth-aiDeep-reading strategy that turns a single specified academic paper into a structured, source-anchored bundle (problem, method, key results, limitations) ready for fact-checking and article drafting. Use this whenever the user names one specific paper (by arXiv ID, URL, or title) and wants it read, summarized, explained, or turned into content — this is the entry point of the paper-reading pipeline.
- ▌ Hook Crafting · yogsoth-ai bundleWrite a WeChat article's opening 1-3 sentence hook using one of four formula families (curiosity, story, value, contrarian), grounded in the verified bundle so the hook doesn't overpromise. Use this after angle-selection has chosen the article's framing, before section-drafting-with-style writes the full piece.
- ▌ Marketing Led · yogsoth-aiMass-audience 公众号 writing path — selects an angle, crafts a hook, drafts the article with a pre-set style constraint, and revises via a 3-sweep editing pass. Use this as the default (and, in v1, only) audience-first-writing tactic whenever the target is a public WeChat account audience rather than a professional/academic readership.
- ▌ Reproducibility Third Party Verification · yogsoth-ai bundle(Proposal, unverified) Attempt to verify a paper's reported results by actually executing its released code/scripts against its own reported configuration — the only SOP in this package whose action type is code execution rather than text reading/judgment. Use this after unit-classification has extracted the paper's reported configuration/hyperparameters as classified units; "not_attempted" is a correct, common output when the paper's own reporting is too incomplete to run, not a failure of this SOP.
- ▌ Angle Selection · yogsoth-ai bundlePick which candidate angle to write the article around, cross-checked against the verified bundle so the chosen angle is actually substantiated by the paper (not just appealing at the skim stage). Use this as the first step of drafting, after quality-assurance has passed, whenever you have candidate_angles from first-pass-skim and a verified bundle.
- ▌ Failure Routing · yogsoth-ai bundleFixed lookup table mapping a detected verification failure type (precision_fail, recall_fail, drift_fail, none) to the specific upstream pipeline step to route back to. Use this immediately after pre-write-precision-check, pre-write-recall-check, or post-write-drift-check report a result, to decide whether and where to loop back rather than proceeding.
- ▌ Quality Assurance · yogsoth-aiVerification strategy that independently re-checks a paper bundle's precision (no unsupported claims) and recall (nothing important omitted) against the original source, before any article drafting begins, then re-checks the finished draft for drift. Use this after deep-read produces a bundle and before audience-first-writing drafts an article — this is the mandatory middle strategy of the paper-reading pipeline.
- ▌ Third Pass Verify · yogsoth-ai bundleTargeted re-read of only the bundle fields flagged as uncertain by second-pass-grasp, using precise PDF queries rather than a full re-read. Use this immediately after second-pass-grasp whenever its uncertain_fields output is non-empty; skip straight to extract-structured-bundle when it's empty.
- ▌ Progressive Passes · yogsoth-aiFull 3-pass progressive deep-reading of a paper — skim, grasp, targeted verify — producing a structured bundle ready for fact-checking and article drafting. This is the default reading tactic for the paper-reading package; use it whenever no angle is already decided and you're starting from a bare paper reference.
- ▌ Seven Sweeps Revision · yogsoth-ai bundleRevise a drafted WeChat article through 3 focused editing passes (Clarity, Prove It, Specificity), each checking one dimension before the next runs. Use this after section-drafting-with-style produces the initial article_draft, before the post-write drift check. Named after the copy-editing "Seven Sweeps" framework this is adapted from — v1 implements 3 of the 7 sweeps; the rest are deferred until real usage shows they're needed.
- ▌ Audience First Writing · yogsoth-aiWriting strategy that turns a verified paper bundle into a finished public-audience article — selecting an angle, applying a pre-set style constraint during drafting (never a post-hoc rewrite), and checking the result for drift against the bundle. Use this after quality-assurance has passed on the bundle — this is the final strategy of the paper-reading pipeline, producing the WeChat article itself.
- ▌ Dual Gate Verification · yogsoth-aiStrict verification path — runs an exhaustive precision check and an exhaustive recall check against the bundle before drafting, then a drift check after drafting, routing any failure back to the appropriate upstream step. This is the default (and, in v1, only) quality-assurance tactic; use it for every run unless a future spot-check or human-in-loop tactic is explicitly requested.
- ▌ Post Write Drift Check · yogsoth-ai bundleBack-summarize the finished article draft and diff it against the already-verified bundle to catch drift (new errors or omissions introduced during drafting/styling) — even when the bundle itself already passed precision and recall checks. Use this immediately after any drafting sop (section-drafting-with-style, or any future content-faithful/hybrid drafting sop) produces an article_draft, before considering the article final. Shared by both quality-assurance and audience-first-writing.
- ▌ Pre Write Recall Check · yogsoth-ai bundleIndependently extract a paper's most important atomic facts directly from the raw source (before looking at the bundle at all), then check whether the bundle covers each one — catching content the bundle omitted. Use this before any article drafting begins, alongside pre-write-precision-check, immediately after extract-structured-bundle produces the bundle. This is a distinct check from precision-check — it catches omissions, not wrong claims — and must never be merged into it.
- ▌ Extract Structured Bundle · yogsoth-ai bundleFinalize a paper's verified reading output into the exact bundle schema (problem/method/key_result/limitation, each with a source_anchor) that all downstream fact-checking and article-drafting sops consume. Use this as the last step of the deep-read strategy, after third-pass-verify, whenever you need to hand off a finished bundle to quality-assurance or audience-first-writing.
- ▌ Pre Write Precision Check · yogsoth-ai bundleIndependently fact-check every claim in a paper's bundle against the raw source text (Factored Verification method), flagging any claim the source doesn't actually support at a correctness score below 0.8. Use this before any article drafting begins, immediately after extract-structured-bundle produces the bundle — never skip this even if the bundle looks obviously correct, since research shows even top models hallucinate subtly when summarizing papers.
- ▌ Section Drafting With Style · yogsoth-ai bundleDraft the full WeChat article from the verified bundle, chosen angle, and hook, applying a pre-set style guide and paragraph/figure-placement structural rules from the start — never as a post-hoc rewrite pass. Use this after hook-crafting has produced the opening hook, as the main drafting step of the marketing-led tactic.
- ▌ Gap Prioritization · yogsoth-aiStrategy for ranking unexplored combinations by novelty, feasibility, and potential impact.
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- ▌ Gap Identification · yogsoth-ai bundleIdentify what the literature has NOT addressed — missing methods, untested combinations, unexplored applications, contradictions without resolution. Used by all strategies.
- ▌ Claim Decomposition · yogsoth-aiIndependent/dependent claim parsing, element extraction, and feature mapping to technical domains
- ▌ Deep Insight · yogsoth-aiDeep Insight Engine with 5 campaigns (gap-analysis, insight, boundary-analysis, sensitivity-analysis, problem-reformulation). Use this skill whenever a user needs to deeply analyze research gaps, understand root causes, probe method boundaries, assess assumption sensitivity, or reformulate research problems. Pre-condition: north-star-crystallization complete + knowledge-acquisition campaign has produced initial findings.
- ▌ Jtbd Mapping · yogsoth-ai bundleMap stakeholder Jobs-to-be-Done — functional, emotional, and social jobs for each affected party. Identifies unserved jobs as opportunity signals.
- ▌ Gap Synthesis · yogsoth-ai bundleCompile all gap analysis intermediate products into a coherent final report with executive summary, detailed findings, and research agenda.
- ▌ Re Derivation · yogsoth-ai bundleRe-derive conclusions under a negated assumption, tracking where the derivation diverges from the original.
- ▌ Clr Validation · yogsoth-ai bundleApply Goldratt's 8 Categories of Legitimate Reservation to validate causal claims. Tests clarity, existence, sufficiency, and logical integrity.
- ▌ Gap Validation · yogsoth-aiValidate gap authenticity via cross-database verification, temporal sensitivity testing, and false-gap filtering. Ensures gaps are genuine absences, not search artifacts.
- ▌ Lateral Escape · yogsoth-aide Bono lateral escape sequence — identify dominant idea, generate provocations (escape/reversal/exaggeration/distortion), follow consequences to extract new framings. Breaks paradigm lock-in.
- ▌ Tension Mining · yogsoth-aiIdentify opposing forces that keep gaps open. Uses evaporating cloud to expose hidden assumptions behind conflicts and polarity mapping for unresolvable tensions.
- ▌ Catwoe Analysis · yogsoth-ai bundleApply Checkland's CATWOE analysis from a specific stakeholder perspective to reveal how the problem looks from that viewpoint.
- ▌ Csh 12 Question · yogsoth-ai bundleApply Ulrich's Critical Systems Heuristics 12 questions across 4 dimensions (motivation, control, expertise, legitimacy) comparing is vs ought.
- ▌ Hmw Formulation · yogsoth-ai bundleGenerate "How Might We" questions at different scope levels (narrow, medium, broad). Ensures each is actionable without being prescriptive.
- ▌ Assumption Audit · yogsoth-aiSurface all assumptions, classify by vulnerability (load-bearing × likely-false), validate causal logic. Focus on dangerous assumptions — high load-bearing + non-explicit.
- ▌ Cross Validation · yogsoth-aiMulti-source cross-validation of gap authenticity — cross-database search, temporal sensitivity testing, false-gap filtering, stakeholder confirmation.
- ▌ Egm Construction · yogsoth-ai bundleBuild structured Evidence Gap Maps — define axes (intervention × outcome or method × domain), place gaps in cells, annotate with evidence density and quality.
- ▌ Evidence Grading · yogsoth-ai bundleAssess evidence quality using GRADE/SOE framework. Rates certainty level and identifies downgrade reasons.
- ▌ Evidence Mapping · yogsoth-aiSystematic evidence map construction — search, classify, locate gaps, visualize. Combines concept-matrix-construction, gap-keyword-extraction, evidence-grading, and egm-construction SOPs.
- ▌ Morris Screening · yogsoth-ai bundleMorris method screening — compute elementary effects to quickly identify important vs unimportant parameters.
- ▌ Polarity Mapping · yogsoth-ai bundleMap unresolvable tensions as Johnson polarities — 4 quadrants (positive/negative of each pole), early warnings, action steps for managing rather than solving.
- ▌ Reframing Matrix · yogsoth-ai bundleReframe the problem from 4 professional perspectives to reveal what each discipline would focus on.
- ▌ Scaling Frontier · yogsoth-aiAnalyze behavior across scales — detect regime changes, identify capacity limits, fit scaling laws within regimes.
- ▌ Socratic Probing · yogsoth-ai bundleApply 6 types of Socratic questions to test claims and assumptions. Exposes weaknesses and strengthens reasoning.
- ▌ Boundary Critique · yogsoth-aiApply CSH boundary critique — what is included/excluded, who benefits/is harmed, what expertise is privileged/marginalized. Identifies opportunities at the boundaries.
- ▌ Evaporating Cloud · yogsoth-ai bundleModel conflicts as Goldratt's Evaporating Cloud — expose hidden assumptions behind opposing needs to dissolve the conflict.
- ▌ Boundary Synthesis · yogsoth-ai bundleCompile all boundary analysis products into a coherent report — validity envelopes, robustness results, failure catalogs, scaling maps, safe operating conditions.
- ▌ Boundary Unfolding · yogsoth-aiSystematically expose hidden system boundaries — CSH 12-question is/ought comparison, identify excluded stakeholders, reveal blind spots. Combines csh-12-question, jtbd-mapping, and salience-classification SOPs.
- ▌ Evidence Synthesis · yogsoth-ai bundleSynthesize multi-source evidence into structured argumentation. Weaves findings from literature, web, and analysis into coherent evidence maps with explicit strength ratings.
- ▌ Failure Clustering · yogsoth-ai bundleGroup observed failures by mechanism (not symptom), identify common triggers per cluster, estimate frequency and severity.
- ▌ Five Whys Drilling · yogsoth-ai bundleIterative "Why?" questioning (5+ levels) to drill from surface phenomenon to actionable root cause. Each level verified against evidence.
- ▌ Fragility Flagging · yogsoth-ai bundleIdentify which specific assumption changes cause conclusion divergence. Rates fragility severity and plausibility of alternatives.
- ▌ Gap Classification · yogsoth-aiClassify identified gaps using Miles 7-type taxonomy and AHRQ 4-reason framework. Determines gap type (theoretical, methodological, empirical, etc.) and root cause of gap existence.
- ▌ Robustness Testing · yogsoth-aiTest conclusion robustness via multi-model convergence — enumerate assumptions, generate alternatives, compare results, flag fragile conclusions.
- ▌ Wickedness Scoring · yogsoth-ai bundleScore a problem against Rittel's 10 criteria to determine if it is tame, complex, or wicked.
- ▌ False Gap Filtering · yogsoth-ai bundleDetect false gaps — search failures, already-solved gaps, and inherently unanswerable questions masquerading as research gaps.
- ▌ Negation Definition · yogsoth-ai bundleDefine strongest plausible alternatives (negations) for each assumption to enable perturbation analysis.
- ▌ Parameter Screening · yogsoth-aiQuick Morris method screening to identify which parameters have large effects and which can be safely ignored.
- ▌ Root Cause Drilling · yogsoth-aiDrill from surface symptoms to root causes via 5 Whys, Ishikawa decomposition, and Current Reality Trees. Validates each causal link with literature evidence.
- ▌ Sobol Decomposition · yogsoth-ai bundleSobol variance decomposition — compute first-order and total-order sensitivity indices for precise variance attribution.
- ▌ Stakeholder Mapping · yogsoth-aiMap all affected parties using CSH 12-question framework, identify jobs-to-be-done, classify by salience. Reveals whose perspective is systematically excluded.
- ▌ Uncertainty Cascade · yogsoth-aiUncertainty cascade propagation — assign input distributions, sample via Monte Carlo, propagate through model, analyze output distribution, identify critical paths. Maps how input uncertainty flows to output uncertainty.