「aims」の検索結果

Verify that every quantitative claim in the paper is traceable to an analysis output file, and that no important output was omitted. Make sure to use this skill whenever the user wants to check that the paper and analysis are consistent before submission. Triggers include: "run the quality gate", "check the paper matches the analysis", "verify consistency", "does the paper match my results", "check my numbers", "are my tables right", "quality check before submission", "verify my claims", "make sure everything is consistent", "double-check the paper against my output files", or any pre-submission integrity check between paper text and computed results.

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Review research code for reproducibility and quality, extract the paper's main empirical claims, compare paper to code, and write a constructive markdown report. Designed for social science / economics projects with LaTeX papers and Stata, R, or Python code.

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Thoroughly verify all code, tables, figures, modeling decisions, and quantitative claims in an academic paper against its source R scripts and output files. Use this skill whenever you need to audit, replicate, or verify an academic research paper - including cross-checking LaTeX tables against R output, validating econometric modeling choices, ensuring sample sizes are consistent, building a verification manifest, and running automated replication tests. Trigger this skill for any mention of: paper verification, replication check, table audit, code-paper consistency, reproducing results, verifying estimates, checking coefficients, or any variant of "does the paper match the code."

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Causal inference framework for answering "does X cause Y?" beyond correlation. DoWhy (Microsoft Research) provides the identify-estimate-refute loop: define a causal graph (DAG), identify the causal effect using backdoor/frontdoor/instrumental variable criteria, estimate treatment effects with multiple estimators, and validate results with automated refutation tests. Use when: distinguishing causation from correlation, estimating treatment effects (ATE, ATT, CATE), designing and analyzing A/B tests with confounders, using instrumental variables, performing counterfactual reasoning ("what would have happened if..."), validating causal claims with sensitivity analysis, working with observational data where randomization is impossible, or any analysis where the question is "what is the CAUSAL effect of X on Y" rather than just "how do X and Y relate?"

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中英双语学术降 AIGC / bilingual academic de-AIGC skill. Removes AI-generated writing signatures from empirical papers in economics, management, and the social sciences — in both English and Chinese. Covers Turnitin AI, GPTZero, Originality.ai on the English side and 知网 AMLC, 万方, 维普 on the Chinese side. Uses a six-step loop (intake → audit → claim-evidence check → differentiated rewrite → five-dimension self-score → cold-reader recheck) with two pattern libraries (22 English + 17 Chinese patterns), section-by-section strategies for empirical papers, and hard protections that keep every number, coefficient, and citation intact. Since 2026-08 it also carries a provenance layer (去水印): deterministic, CJK-safe cleaning of invisible-character carriers and file metadata (docx / png / jpg / svg / pdf, incl. C2PA), an honest account of Claude's statistical text watermark, and an author "ownership pass" — it never claims a text is watermark-free.

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Evaluate research rigor. Assess methodology, experimental design, statistical validity, biases, confounding, evidence quality (GRADE, Cochrane ROB), for critical analysis of scientific claims.

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Reviews whether a design is SOUND — solving the right problem, derived from its stated goals and constraints — and emits ranked, evidence-backed findings, not edits. Read when asked to 'review this design', 'is this design sound', 'pressure-test this proposal', 'do a design review', 'does this solve the right problem', 'poke holes in this spec', 'should we build this', or to critique a proposal / spec / ADR / architecture or product decision. Do NOT read when the user wants to AUTHOR one of these — routing a new proposal is frame-a-proposal, a spec is write-a-spec, a decision record is record-a-decision, a postmortem is write-a-postmortem. Do NOT read for code review of a diff, or to fact-check individual claims (that is a correctness pass, a different job).

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Track how AI assistants describe your brand over time and catch wrong, outdated, or damaging claims. Claude builds your monitoring prompt set, compares runs week over week, and drafts the correction plan when answers drift. Platform: AI visibility.

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Use this skill to run a holistic regrounding pass on the entire microsoft/apm documentation corpus against current source code, page-by-page, and emit surgical fixes for stale claims. Activate when the maintainer wants a WHOLE-CORPUS audit (not per-PR review) -- typical triggers include "audit the docs", "reground the corpus", "check every page against code", "pre-release docs sweep", "the docs have drifted everywhere", or "we just reshaped the TOC, find dead links". Wave-batched and S7-verified; scales to the full ~112-page corpus in ~10 minutes wall-time. This is a SIBLING to docs-sync, not a replacement: docs-sync is per-PR (triggered by a diff); this skill is per-corpus (triggered by a maintainer ask). They share agent personas, schemas, and the docs index, but their triggers MUST NOT collide. Does NOT auto-merge, does NOT push without maintainer review, and does NOT replace per-PR drift detection.

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Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always

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Structured manuscript/grant review with checklist-based evaluation. Use when writing formal peer reviews with specific criteria methodology assessment, statistical validity, reporting standards compliance (CONSORT/STROBE), and constructive feedback. Best for actual review writing, manuscript revision. For evaluating claims/evidence quality use scientific-critical-thinking; for quantitative scoring frameworks use scholar-evaluation.

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Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-review.

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Investigate GitHub security incidents using tamper-proof GitHub Archive data via BigQuery. Use when verifying repository activity claims, recovering deleted PRs/branches/tags/repos, attributing actions to actors, or reconstructing attack timelines. Provides immutable forensic evidence of all public GitHub events since 2011.

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Verify completion and success claims with fresh evidence. Use before claiming a task is complete, a fix works, tests pass, or a feature is ready for GO.

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Completion-evidence route used before claiming work is complete, fixed, passing, committed, or PR-ready. Requires fresh verification output before success claims. Do not use for root-cause debugging, TDD implementation, test-report packaging, or review-feedback triage.

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Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-review.

日本語の概要は準備中です。原文の説明を表示しています。

Structured manuscript/grant review with checklist-based evaluation. Use when writing formal peer reviews with specific criteria methodology assessment, statistical validity, reporting standards compliance (CONSORT/STROBE), and constructive feedback. Best for actual review writing, manuscript revision. For evaluating claims/evidence quality use scientific-critical-thinking; for quantitative scoring frameworks use scholar-evaluation.

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Separate reported claims from impact inferences, with related assets and next checkpoints. Use when the user asks to news impact with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.

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Use when the user wants to analyze active ads from Meta/Facebook, Google, or LinkedIn ad libraries; tear down a competitor's messaging; extract hooks, offers, CTAs, video transcripts, landing page claims, and test ideas from public ads.

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Use when the user wants to summarize, analyze, or repurpose transcripts from TikTok, Instagram, YouTube, Facebook, X/Twitter, LinkedIn, Rumble, or Reddit video posts. Extracts hooks, claims, quotes, content atoms, themes, and reusable scripts.

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精读一本书或一篇文章：提取核心观点、论证结构与关键论据。当用户要求「精读/分析这篇文章」「提取核心观点」「梳理论证逻辑」「做知识地图」「用费曼读书法读书」时使用本技能。五种模式：quick 快速要点 / deep 深度精读 / map 知识地图 / feynman 费曼读书法 / book 全书精读。Deep-reading assistant for books and articles: extract core claims, arguments and evidence.

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Verify that a watermark-removal or "humanizer" step actually removed a statistical text watermark, by measuring a sample whose key the user holds. Use after any tool, script or service claims to have cleaned a text, or when the user asks whether such a tool works, which one to trust, or how to test one. Reports a detector score against fixed thresholds plus what the run cost in meaning, facts, verbatim overlap and length. This skill only measures and never removes a mark.

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End-to-end source processing -- seed, reduce, process all claims through reflect/reweave/verify, archive. The full pipeline in one command. Triggers on "/pipeline", "/pipeline [file]", "process this end to end", "full pipeline".

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Update old notes with new connections. The backward pass that /reflect doesn't do. Revisit existing notes that predate newer related content, add connections, sharpen claims, consider splits. Triggers on "/reweave", "/reweave [note]", "update old notes", "backward connections", "revisit notes".

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