Paper Argument & Evidence v2.0
Description
Move papers beyond “understanding” to traceable evidence: adaptively review full texts or excerpts, reconstruct argument chains, verify claim–evidence alignment, identify causal, extrapolation, statistical, reporting, and reproducibility boundaries, and provide a tiered remediation roadmap.
Related Skills
View all
ResearchAcademic Paper Writing v4.1
A full-process academic paper writing system that combines the Five Sources model with the AFP 3.1 three-layer architecture. Other AI tools help you "write" a paper; this one helps you "plug" every hole that could get it rejected—from one vague idea to a complete submission package in a single run through seven modules, with an anti-hallucination gate, archive-code resume, and four-core review. [10-second value] One sentence of an idea → a submittable, complete paper package. Real sources are enforced throughout, every citation is verifiable, and you can resume across sessions right where you left off. [Trigger phrases] write a paper / literature review / research method design / how to write the discussion / paper polishing / abstract & keywords / submission package [Seven modules, all fully executable instructions] Phase 1 Topic & Introduction → Phase 2 Literature Review → Phase 3 Research Method Design → Phase 4 Discussion → Phase 5 Conclusion → Phase 6 Abstract & Keywords → Phase 7 Full-Text Integration & Submission Package. Phase 0 entry routes A–G map one-to-one to the seven modules, with an R option to restore your last progress via an archive code or archived document. [v4.1 upgrades] 1. Evidence-chain gate (hard constraint): a material registry (M1/M2 numbering) + four citation elements (author / year / title / verifiable locator) + three-color marking (✅ user-supplied / ⚠️ to verify / 🚫 prohibited). If the material library is empty, the system outputs only an outline and search queries and refuses to generate body text with citations; generating references from memory or fabricating volume, issue, or page numbers is forbidden. 2. Archive-code protocol: each module ends with a structured archive code that can be saved as a YouMind document. In your next session, simply reference it with @ to resume without re-requesting already locked information. 3. Reproducible scoring: the C core anchors all dimensions to behavioral standards scored at 4/6/8/10 with stated rationale; the ratchet mechanism only moves scores upward, never downward. 4. Four-discipline configuration library (1A Humanities & Arts / 1B Social Sciences / 2A Science & Engineering / 2B Agriculture, Medicine & Life Sciences), with automatic switching of theory libraries, method toolboxes, and disciplinary norms; Category 2B runs ethics review checkpoints at Phases 0, 3, and 7. 5. Three-paradigm routing (quantitative / qualitative / theoretical): literature review narrative strategies and research method design follow different paths. 6. Anti-cliché checklist and prohibitions: discussion sections using phrases like "limited time and energy" or "pending further research," conclusions introducing new data or literature, and abstracts containing claims unsupported by the body text are all sent back by the B core. 7. Adaptivity: user profile (beginner / intermediate / proficient) + paper type (journal / thesis / conference / course) + pacing mode (default / fast-forward / slow-motion) + multi-task isolation. 8. Submission package & compliance: seven consistency checks, a six-link logic chain, reference formatting (GB/T 7714 / APA 7 / Vancouver / IEEE), ethics approval number, data availability statement, generative AI use disclosure draft, and cover letter draft. [How to use] Tell the system: your discipline, target journal / paper type / word count, and which stage you're at. The system advances module by module, pausing after each one to deliver the section, review report, scorecard, and archive code, then waits for your confirmation before continuing. [Responsibility boundaries] The system handles sources 1–4 (structure, material organization, style, integration); source 5, human calibration, is yours: academic judgment, fact-checking, and innovation decisions. All ⚠️-marked citations and data must be verified by you item by item before submission; this skill does not promise any acceptance or pass outcome.
Argument Rigor Upgrade V2
A professional argument review assistant based on the critical thinking principles in *Asking the Right Questions*. It applies a fixed 10-step questioning process to any viewpoint, article, copy, or speech, identifying logical fallacies, evidence gaps, data issues, missing information, and causal errors. It outputs a comprehensive argument map, a complete list of weaknesses, and constructive optimization or rewriting suggestions.
ResearchArguScope: Argument Insight
ArguScope is a professional argument analysis tool designed to help users deeply understand the argument quality of any text or audio/video content (requires a transcript), and determine whether the strength of its conclusions matches the evidence provided. This tool is suitable for content creators, researchers, commentators, and any user looking to improve critical thinking and argument analysis skills. It can identify the core claims, supporting evidence, implicit assumptions, and reasoning processes in a text, and evaluate the strength of the conclusions. Users can thus understand the strengths and weaknesses of arguments, enhancing their ability to discern the truthfulness of information and the validity of arguments. ArguScope's core features include extracting the argument skeleton, analyzing the text type, reasoning method, and evidence level; identifying five key logical issues: evidence gaps, causal gaps, conceptual gaps, generalization gaps, and value gaps; and performing fact-checking as needed. Finally, it provides a detailed argument analysis report, including an overall score, argument strength assessment, key issue analysis, and a fact-check checklist, to help users comprehensively evaluate the argument quality of the content.
Paper Argument & Evidence v2.0
Description
Move papers beyond “understanding” to traceable evidence: adaptively review full texts or excerpts, reconstruct argument chains, verify claim–evidence alignment, identify causal, extrapolation, statistical, reporting, and reproducibility boundaries, and provide a tiered remediation roadmap.
Related Skills
View allResearchAcademic Paper Writing v4.1
A full-process academic paper writing system that combines the Five Sources model with the AFP 3.1 three-layer architecture. Other AI tools help you "write" a paper; this one helps you "plug" every hole that could get it rejected—from one vague idea to a complete submission package in a single run through seven modules, with an anti-hallucination gate, archive-code resume, and four-core review. [10-second value] One sentence of an idea → a submittable, complete paper package. Real sources are enforced throughout, every citation is verifiable, and you can resume across sessions right where you left off. [Trigger phrases] write a paper / literature review / research method design / how to write the discussion / paper polishing / abstract & keywords / submission package [Seven modules, all fully executable instructions] Phase 1 Topic & Introduction → Phase 2 Literature Review → Phase 3 Research Method Design → Phase 4 Discussion → Phase 5 Conclusion → Phase 6 Abstract & Keywords → Phase 7 Full-Text Integration & Submission Package. Phase 0 entry routes A–G map one-to-one to the seven modules, with an R option to restore your last progress via an archive code or archived document. [v4.1 upgrades] 1. Evidence-chain gate (hard constraint): a material registry (M1/M2 numbering) + four citation elements (author / year / title / verifiable locator) + three-color marking (✅ user-supplied / ⚠️ to verify / 🚫 prohibited). If the material library is empty, the system outputs only an outline and search queries and refuses to generate body text with citations; generating references from memory or fabricating volume, issue, or page numbers is forbidden. 2. Archive-code protocol: each module ends with a structured archive code that can be saved as a YouMind document. In your next session, simply reference it with @ to resume without re-requesting already locked information. 3. Reproducible scoring: the C core anchors all dimensions to behavioral standards scored at 4/6/8/10 with stated rationale; the ratchet mechanism only moves scores upward, never downward. 4. Four-discipline configuration library (1A Humanities & Arts / 1B Social Sciences / 2A Science & Engineering / 2B Agriculture, Medicine & Life Sciences), with automatic switching of theory libraries, method toolboxes, and disciplinary norms; Category 2B runs ethics review checkpoints at Phases 0, 3, and 7. 5. Three-paradigm routing (quantitative / qualitative / theoretical): literature review narrative strategies and research method design follow different paths. 6. Anti-cliché checklist and prohibitions: discussion sections using phrases like "limited time and energy" or "pending further research," conclusions introducing new data or literature, and abstracts containing claims unsupported by the body text are all sent back by the B core. 7. Adaptivity: user profile (beginner / intermediate / proficient) + paper type (journal / thesis / conference / course) + pacing mode (default / fast-forward / slow-motion) + multi-task isolation. 8. Submission package & compliance: seven consistency checks, a six-link logic chain, reference formatting (GB/T 7714 / APA 7 / Vancouver / IEEE), ethics approval number, data availability statement, generative AI use disclosure draft, and cover letter draft. [How to use] Tell the system: your discipline, target journal / paper type / word count, and which stage you're at. The system advances module by module, pausing after each one to deliver the section, review report, scorecard, and archive code, then waits for your confirmation before continuing. [Responsibility boundaries] The system handles sources 1–4 (structure, material organization, style, integration); source 5, human calibration, is yours: academic judgment, fact-checking, and innovation decisions. All ⚠️-marked citations and data must be verified by you item by item before submission; this skill does not promise any acceptance or pass outcome.
Argument Rigor Upgrade V2
A professional argument review assistant based on the critical thinking principles in *Asking the Right Questions*. It applies a fixed 10-step questioning process to any viewpoint, article, copy, or speech, identifying logical fallacies, evidence gaps, data issues, missing information, and causal errors. It outputs a comprehensive argument map, a complete list of weaknesses, and constructive optimization or rewriting suggestions.
ResearchArguScope: Argument Insight
ArguScope is a professional argument analysis tool designed to help users deeply understand the argument quality of any text or audio/video content (requires a transcript), and determine whether the strength of its conclusions matches the evidence provided. This tool is suitable for content creators, researchers, commentators, and any user looking to improve critical thinking and argument analysis skills. It can identify the core claims, supporting evidence, implicit assumptions, and reasoning processes in a text, and evaluate the strength of the conclusions. Users can thus understand the strengths and weaknesses of arguments, enhancing their ability to discern the truthfulness of information and the validity of arguments. ArguScope's core features include extracting the argument skeleton, analyzing the text type, reasoning method, and evidence level; identifying five key logical issues: evidence gaps, causal gaps, conceptual gaps, generalization gaps, and value gaps; and performing fact-checking as needed. Finally, it provides a detailed argument analysis report, including an overall score, argument strength assessment, key issue analysis, and a fact-check checklist, to help users comprehensively evaluate the argument quality of the content.
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