249 件 ・ 関連度順
概要と使いどころ
Read and write a Zotero library from the shell with the `zotero-cli` command - search papers by keyword or meaning, read PDF full text and page ranges, get and set metadata, manage collections, tags, notes and annotations, add items by DOI/URL/ISBN, and export bibliographies. Use whenever the user asks about their Zotero library, references, citations, papers they have saved, or their reading notes.
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Humanization Quality Verifier - Ensures transformation integrity and quality Validates that humanization preserves meaning, citations, and academic standards Use when: after G6 transformation, before final export, for quality assurance Triggers: verify humanization, check transformation, validate changes
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Manual for the marginaleffects R and Python package, and guide to the book "Model to Meaning". Use when users ask about predictions, comparisons, slopes, marginal effects, average treatment effects (ATE/ATT/CATE), hypothesis testing, contrasts, counterfactuals, risk ratios, odds ratios, causal inference with G-computation, or need help with marginaleffects functions like predictions(), comparisons(), slopes(), hypotheses(), datagrid(), avg_predictions(), avg_comparisons(), avg_slopes(), or plot functions.
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Analyzes your top performing ads, identifies what's working in the hooks, CTAs, messaging angles, and formats, then generates new variants that follow the same winning patterns while introducing enough variation to test meaningfully. Platform: Google and Meta.
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Write or rewrite text in plain, layman-readable English in the spirit of ASD-STE100 Simplified Technical English: short sentences, active voice, simple tenses, one word one meaning, condition before command, every technical term defined at first use, no AI slop. Default mode is Plain. Strict mode applies full STE vocabulary compliance when the user names STE, ASD-STE100, or compliance. Use for documentation, READMEs, runbooks, procedures, error messages, release notes, incident reports, API guides, and explanations for readers outside the field. Also use when the user says "STE", "Simplified Technical English", "ASD-STE100", "plain English", "layman's terms", "explain it simply", "no jargon", "de-slop", "make this readable", "write for non-native readers", or asks for docs that translate well. The same rules govern the reply: answer first, prose only.
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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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Explaining how the business is doing, meaning why a metric moved, which segment drove it, pace against a comparable, progress against goals the operator has stated, and questions over sales, traffic, conversion, and customers or this operation's equivalents that the snapshot alone does not answer. Not needed when the snapshot's headline figures and their change on the prior period answer the question, how a period went included.
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Critique a rendered screen's colour — contrast ratios, palette coherence, and semantic meaning. Use when reviewing one screen. For a product-wide WCAG audit use `accessibility-audit` (design-systems); for building the palette use `color-system` (ui-design).
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Reduce defensive disclaimers, stacked hedging, and self-protective narration when drafting, rewriting, or auditing proposals, articles, case studies, captions, and other reader-facing prose. Use when defensive wording is requested for review or appears in a draft, including wording the assistant adds. Preserve meaningful limits; do not perform general style cleanup or ordinary code work.
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Run any question, idea, or decision through a council of 5 AI advisors who independently analyze it, peer-review each other anonymously, and synthesize a final verdict. Based on Karpathy's LLM Council methodology. MANDATORY TRIGGERS: 'council this', 'run the council', 'war room this', 'pressure-test this', 'stress-test this', 'debate this'. STRONG TRIGGERS (use when combined with a real decision or tradeoff): 'should I X or Y', 'which option', 'what would you do', 'is this the right move', 'validate this', 'get multiple perspectives', 'I can't decide', 'I'm torn between'. Do NOT trigger on simple yes/no questions, factual lookups, or casual 'should I' without a meaningful tradeoff (e.g. 'should I use markdown' is not a council question). DO trigger when the user presents a genuine decision with stakes, multiple options, and context that suggests they want it pressure-tested from multiple angles.
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Plans and delivers a single project — scope, estimation, scheduling, critical path, tracking, and recovering when it slips. Use this to plan a project, build or challenge a schedule, estimate credibly, track progress meaningfully, or recover a project that is late.
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Polish reviewer-response letters by softening defensive language, preserving factual meaning, and keeping responses professional, concise, and publication-appropriate.
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Designs primary, secondary, and exploratory endpoints for biomedical and clinical research protocols. Always use this skill when a user needs to translate study aims into operational endpoint definitions with event rules, assessment timing, composite logic, interpretability, and protocol-stage auditability. Focus on endpoint precision, feasibility, clinical meaning, ambiguity reduction, and implementation readiness rather than generic study design advice.
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Finds translational opportunities that connect basic-research discoveries to clinically meaningful use cases such as diagnosis, stratification, prognosis, treatment response prediction, monitoring, or therapeutic development. Use this skill when a user wants to turn a mechanism finding, pathway signal, cellular phenotype, experimental observation, or omics discovery into a stronger translational research direction. Always separate mechanistic relevance from translational usability, and never present a basic finding as clinically actionable unless the evidence supports that level.
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Identifies translationally meaningful paths for bioinformatics findings by mapping omics or computational discoveries to diagnosis, stratification, prognosis, treatment-response, monitoring, or target-nomination use cases, while auditing bridge evidence, assayability, and validation burden. Use this skill when a user wants to know whether a bioinformatics finding can be framed as a stronger translational topic without overclaiming clinical relevance. Always separate statistical signal from translational value, and never imply clinical utility, targetability, or validation depth without explicit evidence support.
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Converts existing manuscript content into LaTeX format aligned with a target journal, conference, or template while preserving manuscript meaning and structural integrity.
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Improves medical English precision without changing the underlying facts, evidence boundaries, or intended scientific meaning.
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Converts biomedical table content into clear manuscript or presentation narrative by prioritizing meaningful patterns, contrasts, and interpretation boundaries rather than restating every number.
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Use this skill to implement hybrid search combining BM25 keyword search with semantic vector search using Reciprocal Rank Fusion (RRF). **Trigger when user asks to:** - Combine keyword and semantic search - Implement hybrid search or multi-modal retrieval - Use BM25/pg_textsearch with pgvector together - Implement RRF (Reciprocal Rank Fusion) for search - Build search that handles both exact terms and meaning **Keywords:** hybrid search, BM25, pg_textsearch, RRF, reciprocal rank fusion, keyword search, full-text search, reranking, cross-encoder Covers: pg_textsearch BM25 index setup, parallel query patterns, client-side RRF fusion (Python/TypeScript), weighting strategies, and optional ML reranking.
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Classify environmental and meteorological variables into driver categories for attribution analysis. Use when you need to group multiple variables into meaningful factor categories.
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Modify living Roblox Studio projects safely: inspect Explorer and execution locations, preserve existing structure, reuse modules/remotes, create meaningful Instances, apply Attributes and CollectionService tags, test server/client and multiple devices, inspect Output, and remove experiments/debug objects. Use when an agent edits a place or rbxl/rbxlx/Rojo project, operates in Studio, adds Scripts/LocalScripts/ModuleScripts, or must verify a Roblox change in context.
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Use when asked 把这段黑话翻译成人话, 这句话到底什么意思, 帮我把周报去黑话, 用大厂黑话改写这段, 互联网黑话词典, 对齐抓手闭环是什么意思, or translate Chinese internet-company jargon (互联网黑话) into plain words and back. Produces a line-by-line translation of 黑话 into plain Chinese with what each sentence actually commits to (who, what, by when), a rewrite at the chosen tone (白话, 得体职场, or 满分大厂味 parody), a 40-plus term glossary with plain meanings, and a 周报去黑话 mode that turns a jargon-heavy weekly report into one a reader can act on.
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Evaluate performance fairly when output is AI-assisted — what still measures the human, what now measures the tooling, and how to run the review conversation. Use when reviewing someone whose work is heavily AI-assisted, when output volume stopped meaning anything, when calibrating a team with uneven AI adoption, or when writing review criteria for the AI era. Produces review guidance: a what-measures-whom analysis, rewritten criteria, calibration rules for mixed-adoption teams, and conversation scripts. For the general review document use performance-review; for redesigning the role itself use role-redesign-for-ai.
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Translate or revise localized UI strings and prose while preserving meaning, terminology, placeholders, and structure.
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。