「ods」の検索結果

Grade curated individual tests for readiness, A-F quality and improvements. ALWAYS USE FOR: grade tests, review only a named test, per-test readiness or quality bands for supplied methods, bodies, file spans or bounded PR diffs, including existing tests. Return Pass/Failed/Uncertain/Not applicable; unresolved/empty scopes omit grades. Compose read-only per-test evidence. Polyglot. DO NOT USE FOR: suite audits (test-engineer/test-anti-patterns), concrete fix/feature detection proof (test-value-evidence), writing/fixing tests, or coverage.

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。

Implement and test Go codemods for the gh aw fix command.

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。

Reference for GitHub MCP server tools, methods, and usage patterns.

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。

Rank tracking without a tracker subscription — position movers between any two periods from real Search Console data. Use when asked "how are my rankings", "what moved this week/month", "track my keywords", or "did the update hit me".

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。

Hunt gRPC vulnerabilities — server reflection enabled (enumerate all services/methods), missing authentication / metadata-stripping on internal endpoints, plaintext gRPC over HTTP/2, internal endpoint disclosure, proto file leakage, gRPC-Web/grpc-gateway transcoding injection, and HTTP/2 Rapid Reset DoS (CVE-2023-44487). Use when target exposes port 50051 / 443 / 8443 / 9090 with HTTP/2, when grpcurl/grpcui detects reflection, when an Envoy or grpc-gateway proxy is fronting a microservice, or when recon reveals a microservice architecture.

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。

Score, embed, and generate DNA sequences with Evo 2, a long-context genomic foundation model. Use this skill when: (1) Computing per-nucleotide or per-sequence likelihoods for variant effect scoring, (2) Embedding genomic windows for downstream classification, (3) Generating DNA conditioned on a prefix, (4) Scoring regulatory or coding regions across species.

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。

Probabilistic single-cell RNA-seq with scvi-tools — scVI for a batch-corrected latent space, scANVI for semi-supervised label transfer, and Bayesian differential expression. Reach for this skill to integrate scRNA-seq batches, embed cells for clustering, transfer annotations from a reference onto a query, or score differentially expressed genes per cluster. For spatial deconvolution / mapping use the cell2location, DestVI, or Tangram methods instead.

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。

Chinese-first research paper writing, revision, polishing, section drafting, rebuttal, peer-review response, thesis prose improvement, and manuscript argument planning. Use when the user asks to write or revise论文正文, abstracts, introductions, methods, results, discussion, conclusions, related work, responses to reviewers, LaTeX/Overleaf text, or academic prose. Preserve formulas, English paper titles, terms, citations, and measured results.

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 log files to troubleshoot errors, identify peak error periods, and produce error clustering, frequency statistics, and time distribution reports. Supports JSON, syslog, and Nginx formats with automatic detection. Use when a user uploads a .log file and asks to analyze errors, find patterns, debug issues, or get distribution stats.

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。

This skill should be used when the user asks to "escalate privileges", "get root access", "become administrator", "privesc techniques", "abuse sudo", "exploit SUID binaries", "Kerberoasting", "pass-the-ticket", "token impersonation", or needs guidance on post-exploitation privilege escalation for Linux or Windows systems.

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。

E1-Quantitative Analysis Guide with Code Generation & Sensitivity Analysis VS-Enhanced with Full 5-Phase process: Avoids obvious analyses, explores innovative methodologies Expanded to include qualitative analysis (thematic, grounded theory, content, narrative) Absorbed E4 (Analysis Code Generator) and E5 (Sensitivity Analysis - Primary Study) capabilities Use when: selecting statistical/qualitative methods, interpreting results, checking assumptions, generating code, sensitivity analysis Triggers: statistical analysis, ANOVA, regression, t-test, power analysis, assumption checking, effect size, thematic analysis, grounded theory, content analysis, narrative analysis, NVivo, ATLAS.ti, coding, qualitative data, R code, Python code, SPSS syntax, sensitivity analysis, robustness check

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。

Agent E3 - Mixed Methods Integration Specialist - Qual-Quant data integration and meta-inference. Covers joint display creation, integration strategies, and legitimation techniques.

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 the proofreading protocol on academic writing — papers or manuscripts. Checks grammar, typos, layout issues, consistency, and academic writing quality. Produces a report without editing files. Make sure to use this skill whenever the user wants surface-level writing errors found — not substantive academic critique. Triggers include: "proofread", "check for typos", "grammar check", "look for errors in my draft", "proofread all", "polish this", "check my writing", "are there any mistakes", "proofread before I send this", or when the user wants a clean-up pass rather than feedback on arguments or methods.

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 expert academic paper summaries for social science research, particularly political science and applied statistics. Use when asked to summarize, review, or create a reading summary of an academic paper, PDF, or research article. Accepts a file (PDF or text format) or a directory of papers. Produces a structured markdown summary — approximately 400–600 words — covering primary contributions, major questions and answers with point estimates, methods and data, and limitations and robustness. Includes BibTeX citation retrieved from Google Scholar and keyword metadata.

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。

This skill covers causal machine learning methods in applied economics and quantitative social science. Use when implementing or choosing between modern ML-based causal estimators — including double machine learning, DML, partially linear models, interactive regression models, cross-fitting, Neyman orthogonality, debiased ML, causal forests, generalized random forest, GRF, honest causal trees, AIPW with machine learning, doubly robust with machine learning, DR-Learner, T-Learner, S-Learner, X-Learner, meta-learners, heterogeneous treatment effects, conditional average treatment effect, CATE, HTE, high-dimensional controls, LASSO controls, post-LASSO, post-double selection, Belloni-Chernozhukov-Hansen, Riesz representer, Chernozhukov, sample splitting, econml, DoubleML package, or any combination of machine learning and causal inference.

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。

Econometrics skill for machine learning methods in causal inference. Activates when the user asks about: "causal forest", "generalized random forest", "GRF", "double machine learning", "DML", "debiased machine learning", "LASSO for variable selection", "post-LASSO", "heterogeneous treatment effects", "CATE", "conditional average treatment effect", "BLP analysis", "CLAN analysis", "causal tree", "honest estimation", "因果森林", "双重机器学习", "异质性处理效应", "条件平均处理效应", "LASSO变量选择", "机器学习因果推断", "去偏机器学习"

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。

This skill covers applied microeconomic empirical methods and research design. Use when the user is selecting an identification strategy, comparing estimators, running diagnostics, designing a research study, or evaluating an empirical strategy. Triggers on "which method", "what estimator", "how to choose", "method comparison", "empirical strategy", "research design", "applied micro", "identification strategy", "power analysis", "design-based", "model-based", "minimum detectable effect", "specification".

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。

This skill covers game-theoretic methods in structural econometrics and industrial organization. Use when the user is working with strategic interactions, equilibrium analysis, or game-theoretic structural models — including entry games, conduct testing, auction models with strategic bidding, bargaining, or matching markets. Triggers on "Nash equilibrium", "subgame perfect", "best response", "strategic interaction", "entry game", "conduct testing", "auction", "mechanism design", "matching market", "bargaining", "BNE", "Bayesian Nash", "static game", "dynamic game", "repeated game", "multiple equilibria", "equilibrium selection", "discrete game", "oligopoly", "game-theoretic", "player", "payoff", "strategy", "dominant strategy", "Bresnahan-Reiss", "Ciliberto-Tamer", "partial identification", "set identification", or markup test.

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。

This skill covers causal inference methods in observational and quasi-experimental settings. Use when the user is implementing, choosing between, or debugging causal identification strategies — including instrumental variables, difference-in-differences, regression discontinuity, synthetic control, or matching estimators. Triggers on "causal effect", "identification strategy", "instrumental variable", "2SLS", "GMM", "difference-in-differences", "DiD", "staggered treatment", "regression discontinuity", "RDD", "synthetic control", "matching", "propensity score", "IPW", "AIPW", "doubly robust", "LATE", "ATT", "ATE", "parallel trends", "exclusion restriction", "first stage", "weak instruments", or "endogeneity".

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。

Activate when the user is drafting any academic economics content from scratch (e.g., outlines, abstracts, introductions, data/methods/identification sections, results narratives, conclusions, referee responses, table/figure captions) and needs economics-specific structure, phrasing options, and quality checks to produce publication-ready prose.

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。

This skill should be used when the user asks to "implement a DiD regression", "write a causal inference pipeline", "set up an event study", "implement instrumental variables", "run a regression discontinuity design", "build a synthetic control model", "implement propensity score matching", "write parallel trends test", "implement Bacon decomposition", or needs code templates for causal inference methods in Python, R, or Stata. Based on Scott Cunningham's Causal Inference: The Mixtape.

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 Iris to submit, inspect, debug, monitor, or recover jobs and tasks; diagnose scheduling and federation; deploy controllers; or reserve dev GPUs and TPUs. Use for ordinary Iris operations, including babysitting a job, controller rollouts, accelerator sessions, and stuck CoreWeave pods. Use a narrower production-run or Zephyr skill when that workflow is explicitly requested.

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。

Problem-solving strategies for numerical integration in numerical methods

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。

Problem-solving strategies for root finding in numerical methods

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。