「poc」の検索結果

Use when training a LightGBM model on tabular data in R and returning model metrics, feature importance ranking tables, and feature importance plots.

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。

Use when validating an existing prognostic risk signature on an external bulk expression cohort with survival outcomes, producing risk scores, Kaplan-Meier curves, risk distribution plots, heatmap, and time-dependent ROC curves. NOT for: model training, feature selection, nomogram construction, calibration analysis, or single-cell data.

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。

Use when building a binary classification model from an expression matrix or other omics feature matrix with LASSO logistic regression, cross-validation, and coefficient path visualization. NOT for: multiclass classification, survival/Cox models, or ordinary linear regression.

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。

Optimizes manuscript titles and abstracts for information density, factual accuracy, and submission fit in biomedical research writing.

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。

Organizes biomedical figures, analyses, and result blocks into a clear Results section structure with disciplined narrative ordering and evidence-aware presentation.

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。

Use when filtering genes with high missingness and then imputing missing values in a bulk expression matrix with group-aware KNN through DMwR2, where donor samples are restricted by one annotation column before imputation. For strata with 10 or fewer samples, the script falls back to row-wise direct filling with mean or median. NOT for: single-cell data, multi-column stratification, non-tabular inputs, network access, or interactive workflows.

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。

Checks consistency across title, abstract, methods, results, figures, tables, and supplements to identify internal contradictions and version drift in biomedical manuscripts.

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。

Checks biomedical manuscripts against reporting guidelines such as CONSORT, STROBE, PRISMA, and TRIPOD to identify missing or weak reporting elements before submission or revision.

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。

A comprehensive auditor for any agent skill — including Manus, OpenClaw/ClawHub, Claude, LobeHub, or custom SKILL.md-based skills. Use this skill whenever a user wants to evaluate, audit, review, score, or quality-check an agent skill before publishing, updating, or deploying. Covers two hard veto gates (structural redlines + research integrity redlines), static quality scoring across 25 criteria (ISO 25010 + OpenSSF + Agent), dynamic test input generation, multi-mode execution testing, multi-layer output evaluation with five specialized category rubrics (Evidence Insight / Protocol Design / Data Analysis / Academic Writing / Other), a Research Veto that applies to all four research categories, human eval viewer generation, actionable P0/P1/P2 optimization recommendations, and automatic skill improvement that outputs a polished, production-ready SKILL.md. Also use whenever a user says "audit my skill", "evaluate my skill", "improve my skill", or wants a corrected version after evaluation.

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。

Run a submission-readiness preflight on a manuscript before arXiv upload. Use when the user is preparing an arXiv submission, asks to check a paper before uploading, mentions hallucinated or fake references, leftover LLM meta-comments / prompts in text, placeholder data (TODO, TBD, XX%), AI-use disclosure, scholarly integrity, research integrity, or arXiv moderation risk — even if they don't say "preflight". Also trigger on phrases like "check my paper before arXiv", "verify my references", "scan for AI artifacts", "scan for LLM residue", "is my submission ready", or "review .tex/.bib before submit".

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。

Turns reviewer comments into structured, professional point-by-point responses linked to manuscript revisions, clarifications, rebuttals, and additional analyses.

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。

Calibrates manuscript claim strength so wording matches the actual evidence level, study design, and validation status.

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。

Use when selecting predictive genes or other molecular features from bulk expression matrices for binary case-vs-control classification with elastic net logistic regression, including coefficient path and cross-validation plots. Trigger keywords: elastic net, glmnet, feature selection, binary classification, lambda.min, lambda.1se. NOT for: survival/Cox modeling, multiclass outcomes, single-cell data, or non-expression tables.

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。

Condenses a full study into conference-submission abstract format. Use when adapting a manuscript abstract or study summary to meet a specific conference's word limit, structured format (Background/Methods/Results/Conclusion), character limits, or required section headings. Also triggers on "adapt my abstract for [conference]", "shorten my abstract to 250 words", "reformat for ASCO/ASGCT/SfN/AACR", "I need a conference abstract", or "cut my abstract to fit the word limit".

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。

Drafts journal-ready cover letters for manuscript submission. Use when preparing a submission package, communicating the manuscript's contributions and journal fit to editors, or tailoring the novelty framing for a specific journal's scope. Also triggers on "write a cover letter for my paper", "draft a submission cover letter", "help me write to the editor", or "cover letter for [journal name]".

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。

Composes a Discussion around key findings, mechanisms, clinical relevance, and limitations. Use when writing or improving a Discussion section for any biomedical manuscript — including interpreting results, connecting to prior literature, addressing unexpected findings, framing limitations, and writing the conclusion. Also triggers on "write my discussion", "help me discuss my findings", "how do I compare to prior studies", "write the limitations paragraph", or "draft a discussion for my paper".

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。

Writes complete, publication-grade figure legends that can stand on their own. Use when writing or revising figure legends for any scientific figure — bar charts, line graphs, scatter plots, box plots, heatmaps, survival curves, flow cytometry plots, western blots, microscopy images, or schematic diagrams. Also triggers on "write a figure legend for", "help me describe this figure", "my figure needs a legend", "write Figure 1 legend", or "what should a figure legend include".

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。

Create or assess an evidence-grounded, falsifiable outcome hypothesis: a testable prediction of what measurable business result will change, for whom, by when, and how leading and lagging indicators will prove or disprove it. Use when framing measurable outcomes, turning an MVP, POC, feature, or technical initiative into a beneficiary result, defining targets and indicators, or judging whether evidence is strong enough to invest. Also applies to business outcome hypotheses, value hypotheses, and outcome statements.

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。

Use when asked 医保怎么报销, 异地就医怎么备案, 住院能报多少, 门诊报销比例, 手工报销要什么材料, 惠民保值不值得买, or work out what China's basic medical insurance (医保) will pay for a treatment. Produces the person's scheme (职工医保 or 居民医保), how a claim works for this case (direct settlement or manual reimbursement), an out-of-pocket estimate built from the city's own deductible, rate and cap, the cross-region registration (异地就医备案) steps, and the documents to keep.

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。

Performs structure-based virtual screening using AutoDock Vina, SMINA, GNINA (CNN scoring), and DiffDock-L hybrid workflows with explicit choice rules across rigid vs flexible docking, cross-docking vs self-docking, binding-site detection (P2Rank, fpocket), receptor preparation (PDB2PQR, PROPKA), ligand preparation (meeko, OpenBabel), and ultralarge-library screening (ZINC22, Enamine REAL). Use when screening chemical libraries against a protein target to find candidate binders, ranking docking poses, or selecting a docking workflow for a specific scenario.

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。

Analyze signal spectra in MATLAB using nonparametric and parametric estimators. Use when computing frequency content, PSD, power spectrum, spectral peaks, bandwidth, or streaming spectral analysis. Covers pspectrum, pwelch, periodogram, pmtm, pburg, pmusic, rootmusic, plomb, poctave, spectrumAnalyzer, dsp.SpectrumEstimator. TRIGGER: FFT, PSD, power spectrum, spectral analysis, frequency content, bandwidth measurement, spectral leakage, window selection, streaming spectrum, periodicity, resolve close frequencies, multitaper. DO NOT TRIGGER: filter design, spectrogram/time-frequency, audio features.

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。

GitHub Actions security review for workflow exploitation vulnerabilities. Use when asked to "review GitHub Actions", "audit workflows", "check CI security", "GHA security", "workflow security review", or review .github/workflows/ for pwn requests, expression injection, credential theft, and supply chain attacks. Exploitation-focused with concrete PoC scenarios.

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。

Keeps the engagement record for client work. Use when they name a client or stakeholder. Use when they debrief a meeting or paste notes. Use when they ask what was agreed. Use when they run a POC, change the client's codebase, prove it on their staging, go live, or need evals before a model acts. Use when they prep a readout, when trust shifts, or they say @fde. Route and run the local fde CLI (or npx --yes fdeops). Never ask them to type commands. Not for ordinary code edits in an unbound repo.

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。

Run a bounded customer proof of concept to test a consequential uncertainty. Use for a spike or pilot with a question and decision deadline, not a full rollout.

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。