SKILL_INDEX.json

{
  "schema_version": "0.2",
  "description": "ParaSkill routing table for ParaOrchestra. GENERATED by tools/check_paraskills.py from SKILL.md frontmatter — do NOT hand-edit; rerun the checker instead.",
  "skill_count": 28,
  "skills": [
    {
      "name": "check-claim-evidence-alignment",
      "description": "Check whether each claim in a paper is supported by appropriate evidence, and identify unsupported claims, weak evidence, or overclaims. Use when auditing abstract, introduction, experiments, conclusion, or reviews for claim-evidence mismatch. Output is an alignment report with fix suggestions.",
      "owner_model": "ParadoxGPT-Checker-4B",
      "skill_family": "checker",
      "output_type": "diagnosis",
      "version": "0.1",
      "default_language": "zh",
      "trigger_keywords": [
        "claim evidence",
        "alignment",
        "unsupported claim",
        "overclaim",
        "evidence",
        "支撑",
        "证据",
        "claim"
      ],
      "required_inputs": [
        "abstract",
        "introduction",
        "method_summary",
        "experiment_summary"
      ],
      "optional_inputs": [
        "conclusion",
        "reviewer_concerns",
        "limitations"
      ],
      "handoff_to": [
        "detect-overclaim-missing-closure",
        "map-claims-to-experiments",
        "rewrite-abstract-with-commitments"
      ],
      "path": "checker/check-claim-evidence-alignment/SKILL.md"
    },
    {
      "name": "check-section-logic-consistency",
      "description": "Check whether abstract, introduction, method, experiments, and conclusion use consistent terminology, claims, assumptions, and closure structure. Use when a paper feels internally inconsistent, has claim drift, or different sections appear to promise different contributions. Output is a consistency report and repair plan.",
      "owner_model": "ParadoxGPT-Checker-4B",
      "skill_family": "checker",
      "output_type": "diagnosis",
      "version": "0.1",
      "default_language": "zh",
      "trigger_keywords": [
        "logic consistency",
        "section consistency",
        "claim drift",
        "terminology",
        "contradiction",
        "前后一致",
        "逻辑"
      ],
      "required_inputs": [
        "paper_sections"
      ],
      "optional_inputs": [
        "claim_list",
        "terminology_list",
        "reviewer_concerns"
      ],
      "handoff_to": [
        "check-claim-evidence-alignment",
        "build-intro-argument-chain",
        "rewrite-abstract-with-commitments"
      ],
      "path": "checker/check-section-logic-consistency/SKILL.md"
    },
    {
      "name": "detect-limitation-laundering",
      "description": "Detect limitation-laundering moves: reframing a real scarcity/weakness as design intent when it is not true, planting decoy limitations to steer criticism, and the core ARGAR failure — equating the APPEARANCE of having addressed an issue with actually having resolved it. Defensive checker counterpart to ARGAR's attack. Use to audit whether weaknesses are honestly acknowledged vs cosmetically dissolved.",
      "owner_model": "ParadoxGPT-Checker-4B",
      "skill_family": "checker",
      "output_type": "diagnosis",
      "version": "0.1",
      "default_language": "zh",
      "trigger_keywords": [
        "limitation laundering",
        "decoy limitation",
        "scarcity as design",
        "appearance vs resolution",
        "weakness hiding",
        "洗白局限",
        "诱饵局限",
        "掩盖弱点"
      ],
      "required_inputs": [
        "paper_context",
        "limitations",
        "acknowledged_weaknesses"
      ],
      "optional_inputs": [
        "reviews"
      ],
      "handoff_to": [
        "detect-presentation-gaming",
        "map-claims-to-experiments",
        "reframe-limitations-honestly"
      ],
      "path": "checker/detect-limitation-laundering/SKILL.md"
    },
    {
      "name": "detect-overclaim-missing-closure",
      "description": "Detect overclaims and missing closure between paper commitments and available evidence, then propose safer wording or needed experiments. Use when abstract, introduction, conclusion, or reviews indicate claims may exceed evidence. Output focuses on claim calibration and closure, not general paper review.",
      "owner_model": "ParadoxGPT-Checker-4B",
      "skill_family": "checker",
      "output_type": "diagnosis",
      "version": "0.1",
      "default_language": "zh",
      "trigger_keywords": [
        "overclaim",
        "missing closure",
        "claim calibration",
        "limitation",
        "承诺过强",
        "没闭合",
        "降措辞"
      ],
      "required_inputs": [
        "claims",
        "experiment_summary"
      ],
      "optional_inputs": [
        "results",
        "limitations",
        "method_summary",
        "target_venue"
      ],
      "handoff_to": [
        "map-claims-to-experiments",
        "rewrite-abstract-with-commitments",
        "identify-fatal-concerns"
      ],
      "path": "checker/detect-overclaim-missing-closure/SKILL.md"
    },
    {
      "name": "detect-presentation-gaming",
      "description": "Detect whether a paper's perceived strengths come from genuine substance or from presentation gaming — strength inflation, parroting of self-positioning language, or \"first/fundamental/comprehensive\" claims without evidence. The defensive flip of ARGAR: turn its attack knowledge into a detector so authors and reviewers can catch repackaging. Use to audit a draft (or a revision) before submission.",
      "owner_model": "ParadoxGPT-Checker-4B",
      "skill_family": "checker",
      "output_type": "diagnosis",
      "version": "0.1",
      "default_language": "zh",
      "trigger_keywords": [
        "presentation gaming",
        "strength inflation",
        "parroting",
        "overclaim detection",
        "repackaging",
        "gaming AI review",
        "包装",
        "夸大",
        "gaming"
      ],
      "required_inputs": [
        "paper_context",
        "claimed_strengths",
        "evidence"
      ],
      "optional_inputs": [
        "reviews"
      ],
      "handoff_to": [
        "detect-limitation-laundering",
        "enforce-content-preservation-zones",
        "identify-fatal-concerns"
      ],
      "path": "checker/detect-presentation-gaming/SKILL.md"
    },
    {
      "name": "enforce-content-preservation-zones",
      "description": "Audit that a revision preserved the paper's scientific content — nothing in the FIXED zone (experiments, tables, figures, equations, proofs, numerical results) was altered, nothing fabricated, no new unsupported claims entered the FREE zone. Combines ARGAR's three-zone model (free/limited/fixed) with XtraGPT's behavior rules (no invented results/citations/equations; no numerical changes unless requested). Use to verify any revision is content-preserving before it ships.",
      "owner_model": "ParadoxGPT-Checker-4B",
      "skill_family": "checker",
      "output_type": "diagnosis",
      "version": "0.1",
      "default_language": "zh",
      "trigger_keywords": [
        "content preservation",
        "edit zone",
        "fixed zone",
        "no number change",
        "no fabrication",
        "scientific content fixed",
        "保科学内容",
        "越界检查",
        "数字未改"
      ],
      "required_inputs": [
        "original_paper",
        "revised_paper"
      ],
      "optional_inputs": [
        "edit_diff"
      ],
      "handoff_to": [
        "detect-overclaim-missing-closure",
        "detect-presentation-gaming",
        "revise-by-criterion"
      ],
      "path": "checker/enforce-content-preservation-zones/SKILL.md"
    },
    {
      "name": "generate-idea-variants",
      "description": "Generate distinct conservative, ambitious, low-cost, and high-risk/high-reward variants of a research idea seed, with recommendation and rationale. Use when the user already has a plausible idea but needs alternatives across risk, cost, and novelty levels. Output variants must differ substantively, not just by wording.",
      "owner_model": "ParadoxGPT-Creator-4B",
      "skill_family": "creator",
      "output_type": "generation",
      "version": "0.1",
      "default_language": "zh",
      "trigger_keywords": [
        "idea variants",
        "conservative",
        "ambitious",
        "low-cost",
        "high-risk",
        "变体",
        "低成本",
        "高风险"
      ],
      "required_inputs": [
        "idea_seed"
      ],
      "optional_inputs": [
        "research_context",
        "constraints",
        "target_venue"
      ],
      "handoff_to": [
        "interactively-refine-idea",
        "identify-fatal-concerns",
        "map-claims-to-experiments"
      ],
      "path": "creator/generate-idea-variants/SKILL.md"
    },
    {
      "name": "interactively-refine-idea",
      "description": "Turn a vague research idea seed into a clearer research direction through clarification, axes, assumptions, risks, and next-step recommendations. Use when a user has an early idea and needs an idea partner rather than a list of ten brainstormed topics. Output is interactive guidance with questions and 2-3 concrete directions.",
      "owner_model": "ParadoxGPT-Creator-4B",
      "skill_family": "creator",
      "output_type": "interactive",
      "version": "0.1",
      "default_language": "zh",
      "trigger_keywords": [
        "idea seed",
        "refine idea",
        "research direction",
        "clarify",
        "brainstorm",
        "交互",
        "科研想法",
        "创新"
      ],
      "required_inputs": [
        "user_idea_seed"
      ],
      "optional_inputs": [
        "constraints",
        "research_context",
        "target_venue",
        "user_goal"
      ],
      "handoff_to": [
        "generate-idea-variants",
        "identify-fatal-concerns",
        "map-claims-to-experiments",
        "build-intro-argument-chain"
      ],
      "path": "creator/interactively-refine-idea/SKILL.md"
    },
    {
      "name": "position-novel-idea",
      "description": "Frame a NEW research idea's novelty and positioning honestly, using what ARGAR showed actually moves reviewers (positioning/contribution framing, not hype). Separate genuinely-new contributions from incremental ones, draft a defensible positioning statement against prior work, and surface the novelty risks a reviewer would raise. Use at the idea-shaping stage, before any paper exists.",
      "owner_model": "ParadoxGPT-Creator-4B",
      "skill_family": "creator",
      "output_type": "generation",
      "version": "0.1",
      "default_language": "zh",
      "trigger_keywords": [
        "novelty",
        "positioning",
        "new idea",
        "contribution framing",
        "defensible novelty",
        "position",
        "新想法",
        "新颖性",
        "定位"
      ],
      "required_inputs": [
        "idea_seed",
        "research_context"
      ],
      "optional_inputs": [
        "related_work",
        "target_venue"
      ],
      "handoff_to": [
        "reverse-engineer-paper-idea",
        "generate-idea-variants",
        "reposition-related-work"
      ],
      "path": "creator/position-novel-idea/SKILL.md"
    },
    {
      "name": "reverse-engineer-paper-idea",
      "description": "Reverse-engineer a paper's research idea as a retrospective formation path: problem setting, prior landscape, unresolved gap, hidden assumption, tension, observation, core insight, method move, nontriviality, experimentability, and reviewer defensibility. Use when building ParaCreator annotations or learning how a finished paper's idea could have been constructed. Do not claim to know the authors' true thought process.",
      "owner_model": "ParadoxGPT-Creator-4B",
      "skill_family": "creator",
      "output_type": "diagnosis",
      "version": "0.1",
      "default_language": "zh",
      "trigger_keywords": [
        "idea reconstruction",
        "reverse engineer",
        "core insight",
        "gap",
        "hidden assumption",
        "idea_annotation",
        "反推",
        "科研想法"
      ],
      "required_inputs": [
        "paper_context"
      ],
      "optional_inputs": [
        "reviewer_concerns",
        "target_schema",
        "venue"
      ],
      "handoff_to": [
        "interactively-refine-idea",
        "generate-idea-variants",
        "map-claims-to-experiments"
      ],
      "path": "creator/reverse-engineer-paper-idea/SKILL.md"
    },
    {
      "name": "design-baselines-and-ablations",
      "description": "Design baseline and ablation plans that test a method's central claims and isolate which components matter. Use when a paper has a method sketch, claims, and research area but needs comparison structure, component ablations, and expected findings. Output is an experiment design plan, not a list of random benchmark names.",
      "owner_model": "ParadoxGPT-Designer-4B",
      "skill_family": "designer",
      "output_type": "generation",
      "version": "0.1",
      "default_language": "zh",
      "trigger_keywords": [
        "baseline",
        "ablation",
        "component",
        "experiment design",
        "对比实验",
        "消融",
        "基线"
      ],
      "required_inputs": [
        "method_summary",
        "core_components",
        "claims"
      ],
      "optional_inputs": [
        "research_area",
        "related_work",
        "constraints",
        "current_experiments"
      ],
      "handoff_to": [
        "map-claims-to-experiments",
        "design-diagnostic-analysis",
        "identify-fatal-concerns"
      ],
      "path": "designer/design-baselines-and-ablations/SKILL.md"
    },
    {
      "name": "design-defensive-experiments",
      "description": "Design experiments that make a paper's claims ROBUST to reviewer skepticism and to presentation-gaming attacks — i.e., evidence that pre-empts the exact concerns ARGAR exploits (overclaim, missing closure, mechanism-doubt). Maps each anticipated concern to an experiment that would settle it, without inventing data. Use when planning experiments to harden a paper before submission, grounded in ARGAR's defense goal and XtraGPT's \"experimental support for main innovations\" criterion.",
      "owner_model": "ParadoxGPT-Designer-4B",
      "skill_family": "designer",
      "output_type": "mapping",
      "version": "0.1",
      "default_language": "zh",
      "trigger_keywords": [
        "defensive experiment",
        "pre-empt concern",
        "robustness",
        "harden paper",
        "rebuttal-proof",
        "mechanism ablation",
        "防御性实验",
        "预判质疑",
        "加固"
      ],
      "required_inputs": [
        "claims",
        "method_summary",
        "anticipated_concerns"
      ],
      "optional_inputs": [
        "current_experiments"
      ],
      "handoff_to": [
        "map-claims-to-experiments",
        "design-baselines-and-ablations",
        "design-diagnostic-analysis"
      ],
      "path": "designer/design-defensive-experiments/SKILL.md"
    },
    {
      "name": "design-diagnostic-analysis",
      "description": "Design diagnostic analyses that explain why a method works, when it fails, and which mechanism supports the paper's claims. Use when main results are present but the causal story, mechanism, or failure modes are unclear. Output is an analysis protocol with metrics and expected interpretations.",
      "owner_model": "ParadoxGPT-Designer-4B",
      "skill_family": "designer",
      "output_type": "generation",
      "version": "0.1",
      "default_language": "zh",
      "trigger_keywords": [
        "diagnostic analysis",
        "mechanism",
        "why it works",
        "failure mode",
        "analysis experiment",
        "机制分析",
        "诊断实验"
      ],
      "required_inputs": [
        "method_claim",
        "main_results",
        "uncertain_mechanism"
      ],
      "optional_inputs": [
        "method_summary",
        "current_experiments",
        "constraints"
      ],
      "handoff_to": [
        "design-baselines-and-ablations",
        "map-claims-to-experiments",
        "check-claim-evidence-alignment"
      ],
      "path": "designer/design-diagnostic-analysis/SKILL.md"
    },
    {
      "name": "map-claims-to-experiments",
      "description": "Map a paper's claims to the experiments that should support them, and flag claims that are under-supported or not closed by any experiment. Use when planning what evidence a paper needs, or auditing whether a paper's existing experiments actually back its claims. ParaDesigner is a claim-to-evidence planner, not a benchmark/baseline encyclopedia — it reasons about which experiment type closes which claim, not which dataset name to pick.",
      "owner_model": "ParadoxGPT-Designer-4B",
      "skill_family": "designer",
      "output_type": "mapping",
      "version": "0.1",
      "default_language": "zh",
      "trigger_keywords": [
        "claim",
        "experiment",
        "evidence",
        "ablation",
        "闭合",
        "支撑",
        "实验设计",
        "closure",
        "sufficiency"
      ],
      "required_inputs": [
        "paper_claims",
        "method_summary"
      ],
      "optional_inputs": [
        "current_experiments",
        "target_venue",
        "constraints"
      ],
      "handoff_to": [
        "design-baselines-and-ablations",
        "design-diagnostic-analysis",
        "detect-overclaim-missing-closure"
      ],
      "path": "designer/map-claims-to-experiments/SKILL.md"
    },
    {
      "name": "calibrate-rating-severity",
      "description": "Calibrate whether reviewer concerns justify a proposed rating or decision, and estimate how each concern should affect acceptance probability. Use when reviews, internal critiques, or generated concerns feel too harsh, too lenient, or inconsistent with the rating. Output is a severity and rating calibration, not a new full review.",
      "owner_model": "ParadoxGPT-Reviewer-4B",
      "skill_family": "reviewer",
      "output_type": "diagnosis",
      "version": "0.1",
      "default_language": "zh",
      "trigger_keywords": [
        "rating",
        "severity",
        "calibration",
        "score",
        "confidence",
        "reviewer",
        "分数",
        "严重程度"
      ],
      "required_inputs": [
        "paper_context",
        "concern_list"
      ],
      "optional_inputs": [
        "candidate_rating",
        "target_venue",
        "existing_review"
      ],
      "handoff_to": [
        "identify-fatal-concerns",
        "simulate-top-conference-review",
        "check-claim-evidence-alignment"
      ],
      "path": "reviewer/calibrate-rating-severity/SKILL.md"
    },
    {
      "name": "identify-fatal-concerns",
      "description": "Identify the few concerns most likely to cause rejection, prioritize them by severity and fixability, and separate fatal flaws from ordinary weaknesses. Use for pre-submission triage, rebuttal planning, or when a full review is too broad. Output is a focused risk diagnosis, not a complete peer review.",
      "owner_model": "ParadoxGPT-Reviewer-4B",
      "skill_family": "reviewer",
      "output_type": "diagnosis",
      "version": "0.1",
      "default_language": "zh",
      "trigger_keywords": [
        "fatal concern",
        "reject risk",
        "major weakness",
        "triage",
        "致命问题",
        "投稿风险",
        "拒稿"
      ],
      "required_inputs": [
        "paper_context",
        "target_venue"
      ],
      "optional_inputs": [
        "existing_review",
        "author_constraints",
        "rebuttal_window"
      ],
      "handoff_to": [
        "map-claims-to-experiments",
        "detect-overclaim-missing-closure",
        "rewrite-abstract-with-commitments"
      ],
      "path": "reviewer/identify-fatal-concerns/SKILL.md"
    },
    {
      "name": "judge-review-shift",
      "description": "Pairwise meta-judge that quantifies how a review CHANGED between two versions of the same paper, on two axes: perceived-strength delta and weakness-severity delta (each in [-10, +10]). Direct port of ARGAR's pairwise review judge. Use to measure whether a revision (or any change) actually moved the reviewer's judgment, separately tracking strengths vs weaknesses — NOT to judge whether the review is correct.",
      "owner_model": "ParadoxGPT-Reviewer-4B",
      "skill_family": "reviewer",
      "output_type": "mapping",
      "version": "0.1",
      "default_language": "zh",
      "trigger_keywords": [
        "review shift",
        "pairwise judge",
        "strength delta",
        "severity delta",
        "review diff",
        "meta-review",
        "评分变化",
        "审稿变化",
        "judge"
      ],
      "required_inputs": [
        "baseline_reviews",
        "candidate_reviews"
      ],
      "optional_inputs": [
        "paper_title",
        "abstract"
      ],
      "handoff_to": [
        "profile-reviewer-signals",
        "calibrate-rating-severity",
        "identify-fatal-concerns"
      ],
      "path": "reviewer/judge-review-shift/SKILL.md"
    },
    {
      "name": "judge-revision-winrate",
      "description": "Pairwise judge that decides which of two paragraph revisions (original vs candidate) better satisfies a given instruction, using LENGTH-CONTROLLED comparison to avoid verbosity bias. Port of XtraGPT's Length-Controlled Win Rate protocol. Use to compare two candidate rewrites of the same text, NOT to compare reviews (use judge-review-shift for that) and NOT to score a whole paper.",
      "owner_model": "ParadoxGPT-Reviewer-4B",
      "skill_family": "reviewer",
      "output_type": "mapping",
      "version": "0.1",
      "default_language": "zh",
      "trigger_keywords": [
        "win rate",
        "winrate",
        "pairwise revision",
        "compare revisions",
        "length-controlled",
        "LC winrate",
        "哪个改得好",
        "对比改写"
      ],
      "required_inputs": [
        "original_text",
        "candidate_text",
        "instruction"
      ],
      "optional_inputs": [
        "paper_context",
        "criterion"
      ],
      "handoff_to": [
        "revise-by-criterion",
        "judge-review-shift"
      ],
      "path": "reviewer/judge-revision-winrate/SKILL.md"
    },
    {
      "name": "profile-reviewer-signals",
      "description": "Read a set of reviews of the same paper and extract the recurring reviewer SIGNALS — each a recurring perception/concern annotated with its frequency and severity — plus the strengths reviewers explicitly recognized (to be protected). Direct port of ARGAR's attack-loop Profile stage, used here for honest prioritization. Use to turn a pile of reviews into a ranked, actionable signal list before planning revisions.",
      "owner_model": "ParadoxGPT-Reviewer-4B",
      "skill_family": "reviewer",
      "output_type": "diagnosis",
      "version": "0.1",
      "default_language": "zh",
      "trigger_keywords": [
        "review signals",
        "recurring concerns",
        "profile reviews",
        "prioritize concerns",
        "review summary",
        "审稿信号",
        "重复 concern",
        "汇总 review"
      ],
      "required_inputs": [
        "reviews"
      ],
      "optional_inputs": [
        "paper_context"
      ],
      "handoff_to": [
        "identify-fatal-concerns",
        "reframe-limitations-honestly",
        "amplify-genuine-strengths"
      ],
      "path": "reviewer/profile-reviewer-signals/SKILL.md"
    },
    {
      "name": "simulate-top-conference-review",
      "description": "Generate a complete top-conference-style peer review of a paper: summary, strengths, weaknesses, questions, rating, confidence, and recommendation rationale. Use when the user wants to know how a paper would fare at a target venue (ICLR/ICML/NeurIPS/ACL/EMNLP/AAAI) before submitting, or wants a full-dress review rather than just a risk scan. Reviewer concerns are paper-grounded and classified into the five concern types.",
      "owner_model": "ParadoxGPT-Reviewer-4B",
      "skill_family": "reviewer",
      "output_type": "generation",
      "version": "0.1",
      "default_language": "zh",
      "trigger_keywords": [
        "review",
        "reviewer",
        "审稿",
        "投稿",
        "rating",
        "reject",
        "accept",
        "strength",
        "weakness",
        "顶会"
      ],
      "required_inputs": [
        "paper_context",
        "target_venue"
      ],
      "optional_inputs": [
        "reviewer_lens",
        "author_claims",
        "constraints"
      ],
      "handoff_to": [
        "identify-fatal-concerns",
        "calibrate-rating-severity",
        "check-claim-evidence-alignment"
      ],
      "path": "reviewer/simulate-top-conference-review/SKILL.md"
    },
    {
      "name": "amplify-genuine-strengths",
      "description": "Make the paper's REAL strengths visible to the reviewer — articulate them clearly and place them where they count. Grounded in ARGAR's finding that highlighting strengths is the stable, effective lever (impress > convince), but applied HONESTLY: only amplify strengths that are backed by evidence in the paper; flag any \"strength\" with no supporting evidence so it is NOT inflated. Output is a placement/phrasing plan, not a rewrite of evidence.",
      "owner_model": "ParadoxGPT-Writer-4B",
      "skill_family": "writer",
      "output_type": "revision",
      "version": "0.1",
      "default_language": "zh",
      "trigger_keywords": [
        "strength",
        "strengths",
        "highlight",
        "放大优点",
        "contribution",
        "selling point",
        "impress",
        "卖点",
        "contribution list"
      ],
      "required_inputs": [
        "paper_context",
        "strengths_evidence"
      ],
      "optional_inputs": [
        "target_section"
      ],
      "handoff_to": [
        "revise-by-criterion",
        "enforce-content-preservation-zones",
        "diagnose-aha-moment"
      ],
      "path": "writer/amplify-genuine-strengths/SKILL.md"
    },
    {
      "name": "build-intro-argument-chain",
      "description": "Plan an introduction as an argument chain that moves the reviewer from known problem to gap, tension, insight, method move, and evidence commitments. Use when the user has an idea or paper context but the introduction lacks a clear paragraph-level reasoning path. Output is a paragraph plan, not polished prose.",
      "owner_model": "ParadoxGPT-Writer-4B",
      "skill_family": "writer",
      "output_type": "generation",
      "version": "0.1",
      "default_language": "zh",
      "trigger_keywords": [
        "introduction",
        "intro",
        "argument chain",
        "paragraph plan",
        "motivation",
        "文章引言",
        "论证链"
      ],
      "required_inputs": [
        "problem",
        "gap",
        "insight",
        "method_summary",
        "experiment_summary"
      ],
      "optional_inputs": [
        "target_venue",
        "related_work_landscape",
        "reviewer_concerns"
      ],
      "handoff_to": [
        "diagnose-aha-moment",
        "map-claims-to-experiments",
        "check-section-logic-consistency"
      ],
      "path": "writer/build-intro-argument-chain/SKILL.md"
    },
    {
      "name": "diagnose-aha-moment",
      "description": "Diagnose whether a paper's abstract/introduction creates a clear reviewer \"Aha Moment\". Identify the central realization (原来是这样 / 原来还能这样), separate problem / motivation / insight, list the commitments the opening makes, and check whether the method and experiments actually close those commitments. Use when evaluating or revising a paper's abstract or introduction narrative before submission. Output is a DIAGNOSIS only — do not rewrite the text here; hand off to a revision skill for that.",
      "owner_model": "ParadoxGPT-Writer-4B",
      "skill_family": "writer",
      "output_type": "diagnosis",
      "version": "0.1",
      "default_language": "zh",
      "trigger_keywords": [
        "abstract",
        "introduction",
        "aha moment",
        "realization",
        "motivation",
        "insight",
        "commitment",
        "narrative",
        "投稿前检查"
      ],
      "required_inputs": [
        "title",
        "abstract",
        "introduction",
        "method_summary"
      ],
      "optional_inputs": [
        "experiment_summary",
        "reviewer_concerns"
      ],
      "handoff_to": [
        "rewrite-abstract-with-commitments",
        "detect-overclaim-missing-closure",
        "map-claims-to-experiments"
      ],
      "path": "writer/diagnose-aha-moment/SKILL.md"
    },
    {
      "name": "expand-analytical-discussion",
      "description": "Deepen the analysis / discussion of existing results — interpret what the results show, connect them to the paper's claims, and explain the mechanism — WITHOUT adding new experiments or numbers. Grounded in ARGAR's finding that analytical discussion expansion has the largest single-strategy impact on both perceived strength (+2.63) and reduced severity (-1.98). Honest: interpret existing evidence, never fabricate.",
      "owner_model": "ParadoxGPT-Writer-4B",
      "skill_family": "writer",
      "output_type": "revision",
      "version": "0.1",
      "default_language": "zh",
      "trigger_keywords": [
        "discussion",
        "analysis",
        "interpret results",
        "expand discussion",
        "mechanism",
        "why it works",
        "分析",
        "讨论",
        "解读结果"
      ],
      "required_inputs": [
        "paper_context",
        "results_summary"
      ],
      "optional_inputs": [
        "claims",
        "method_summary"
      ],
      "handoff_to": [
        "revise-by-criterion",
        "map-claims-to-experiments",
        "design-diagnostic-analysis"
      ],
      "path": "writer/expand-analytical-discussion/SKILL.md"
    },
    {
      "name": "reframe-limitations-honestly",
      "description": "Handle a paper's weaknesses/limitations WITHOUT gaming the reviewer. Grounded in ARGAR's strength-weakness asymmetry: trying to dissolve weaknesses backfires 31.6% of the time, so this skill only reframes a limitation when there is a LEGITIMATE design rationale, and otherwise honestly acknowledges it and hands off to add evidence. It explicitly watches for (and refuses) limitation-laundering moves like reframing scarcity as design intent when untrue.",
      "owner_model": "ParadoxGPT-Writer-4B",
      "skill_family": "writer",
      "output_type": "revision",
      "version": "0.1",
      "default_language": "zh",
      "trigger_keywords": [
        "limitation",
        "weakness",
        "reframe",
        "acknowledge",
        "rebuttal",
        "dissolving weakness",
        "局限",
        "弱点",
        "诚恳",
        "limitation"
      ],
      "required_inputs": [
        "paper_context",
        "limitations",
        "weaknesses"
      ],
      "optional_inputs": [
        "target_venue"
      ],
      "handoff_to": [
        "map-claims-to-experiments",
        "detect-limitation-laundering",
        "rewrite-abstract-with-commitments"
      ],
      "path": "writer/reframe-limitations-honestly/SKILL.md"
    },
    {
      "name": "reposition-related-work",
      "description": "Reframe the related-work / prior-art discussion so the paper's contribution and the gap it fills become visible. Grounded in ARGAR's finding that related-work repositioning is the single most sustained-effective presentation strategy (49.3% accepted exposure). Applied honestly: position relative to prior work accurately, do not strawman or overclaim novelty. Output is a repositioning plan + positioning statement, not invented comparisons.",
      "owner_model": "ParadoxGPT-Writer-4B",
      "skill_family": "writer",
      "output_type": "revision",
      "version": "0.1",
      "default_language": "zh",
      "trigger_keywords": [
        "related work",
        "prior work",
        "positioning",
        "contribution framing",
        "gap",
        "novelty positioning",
        "related work",
        "定位",
        "前作",
        "gap"
      ],
      "required_inputs": [
        "paper_context",
        "related_work",
        "contribution"
      ],
      "optional_inputs": [
        "target_venue"
      ],
      "handoff_to": [
        "revise-by-criterion",
        "build-intro-argument-chain",
        "check-section-logic-consistency"
      ],
      "path": "writer/reposition-related-work/SKILL.md"
    },
    {
      "name": "revise-by-criterion",
      "description": "Revise a selected paragraph to satisfy ONE explicit academic writing criterion, using the full paper context T to stay globally consistent. The model returns the revised text plus a short note of what changed and what was deliberately preserved (numbers, citations, equations, results). This is the improved port of XtraGPT's native paper-revision skill. Use for instruction-driven, context-aware, single-paragraph revision. Do NOT invent results or change numerical claims.",
      "owner_model": "ParadoxGPT-Writer-4B",
      "skill_family": "writer",
      "output_type": "revision",
      "version": "0.1",
      "default_language": "zh",
      "trigger_keywords": [
        "revise",
        "rewrite",
        "polish",
        "criterion",
        "criteria",
        "improve clarity",
        "strengthen motivation",
        "reduce overclaim",
        "align",
        "consistency",
        "修改",
        "润色",
        "按 criteria 改"
      ],
      "required_inputs": [
        "paper_context",
        "selected_content",
        "criterion",
        "instruction"
      ],
      "optional_inputs": [
        "section_name",
        "venue_style",
        "reviewer_feedback"
      ],
      "handoff_to": [
        "amplify-genuine-strengths",
        "reframe-limitations-honestly",
        "enforce-content-preservation-zones"
      ],
      "path": "writer/revise-by-criterion/SKILL.md"
    },
    {
      "name": "rewrite-abstract-with-commitments",
      "description": "Rewrite a paper abstract so it makes clear, defensible commitments rather than vague promises or overclaims. Use when an abstract diagnosis already identifies weak realization, unclear contribution, unsupported claims, or poor claim calibration, and the user needs a revised abstract with rationale. Output is a revision plus sentence-level reasoning; it does not redesign the method or invent missing experiments.",
      "owner_model": "ParadoxGPT-Writer-4B",
      "skill_family": "writer",
      "output_type": "revision",
      "version": "0.1",
      "default_language": "zh",
      "trigger_keywords": [
        "abstract",
        "rewrite",
        "commitment",
        "overclaim",
        "claim calibration",
        "摘要",
        "改写",
        "承诺"
      ],
      "required_inputs": [
        "old_abstract",
        "paper_context"
      ],
      "optional_inputs": [
        "diagnosis",
        "target_realization",
        "claim_constraints",
        "target_venue"
      ],
      "handoff_to": [
        "diagnose-aha-moment",
        "detect-overclaim-missing-closure",
        "build-intro-argument-chain"
      ],
      "path": "writer/rewrite-abstract-with-commitments/SKILL.md"
    }
  ]
}