「audit」の検索結果

Repurpose existing content into a native LinkedIn post. Take a tweet, thread, YouTube video, blog, or newsletter and rebuild it for LinkedIn: re-hook before the fold, expand to the 900 to 1300 char sweet spot, add whitespace and a CTA, move links to the first comment, run the humanizer, publish via Publora on approval. Not for writing from scratch (use linkedin-post-writer), not for auditing a draft (use linkedin-humanizer --mode audit).

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。

Reddit Ads campaign analysis detecting community targeting issues, creative fatigue, bid inefficiencies, and subreddit performance problems. Use when pasting Reddit Ads data, analyzing subreddit targeting, or auditing Reddit advertising spend for B2B or B2C campaigns. Platform: Reddit.

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。

LinkedIn Ads campaign analysis for B2B marketers detecting CTR issues, audience quality problems, lead gen form friction, and budget inefficiencies. Use when pasting LinkedIn campaign exports, analyzing B2B ad performance, or auditing LinkedIn advertising spend. Platform: LinkedIn.

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。

Audit how visible your brand is inside AI answers (ChatGPT, Claude, Gemini, Perplexity, AI Overviews). Claude builds a prompt panel for your category, scores where you show up vs competitors, and turns the gaps into a prioritized fix list. Platform: AI visibility.

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。

CRO analysis for landing pages evaluating headline clarity, CTA placement, trust signals, mobile friction, and conversion killers. Use when uploading a landing page screenshot, pasting a URL for review, requesting conversion rate optimization feedback, or auditing page effectiveness prioritized by impact. Platform: Google and Meta.

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。

Meta/Facebook/Instagram Ads campaign structure analysis detecting creative fatigue, audience overlap, scaling opportunities, and iOS tracking verification issues. Use when pasting Meta account data, analyzing Facebook ad performance, reviewing Instagram campaigns, or requesting audit of social advertising spend. Platform: Meta.

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。

Deep analysis of Git history: identify frequently changed hotspot files, analyze code ownership by contributor, and scan for leaked secrets. Triggered when users ask about Git analysis, code hotspots, who owns what code, secret scanning, security audits of commit history, or optimizing code review assignments.

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。

Audit Terms of Service, user agreements, and privacy policies for consumer risks, producing a structured report that flags unfair clauses, data traps, and liability issues. Trigger when a user asks to review, audit, or analyze a ToS, privacy policy, or user agreement, or mentions specific concerns like auto-renewal or data authorization.

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。

Recommend the right TRES Finance report for any user question. Trigger this skill whenever a user asks which report to use, what report contains certain data, how to get specific information out of TRES, or compares two reports. Also trigger when the user describes a goal (e.g. "month-end close", "auditor needs proof", "tax filing", "reconciliation") without naming a specific report. Trigger phrases include "which report", "what report", "how do I export", "where can I find", "I need a report for", "what's the difference between", "best report for", "auditor asked for", or any question about getting data out of TRES Finance.

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。

Validate the replication package for the sewage-house-prices project. Runs 10 checks covering script execution, file integrity, output freshness, dependency verification, data provenance, and README completeness (AEA format). This skill should be used when asked to "audit replication", "check the package", or "verify reproducibility".

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。

Causal inference design audit for the sewage-house-prices project. Runs a 4-phase review (claim identification, design validity, inference, polish) covering hedonic pricing, repeat sales, long difference, DiD/event studies, upstream/downstream, and dry spill strategies. This skill should be used when asked to "check the econometrics", "audit the identification", "review the strategy", or when verifying that code matches the stated design.

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。

VS-Enhanced Academic Style Auditor - Academic Writing Quality Analysis Identifies 24+ writing patterns that reduce scholarly quality, adapted from Wikipedia AI Cleanup guidelines Use when: checking drafts before submission, improving academic writing quality, preparing for style improvement Triggers: writing quality, style audit, pattern check, writing review, academic style 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。

Run the R code review protocol on R scripts. Checks code quality, reproducibility, domain correctness, and professional standards. Produces a report without editing files. Make sure to use this skill whenever the user wants their existing R code evaluated or audited — not when they want new analysis written. Triggers include: "review my R script", "check my R code", "is my code replication-ready", "audit this R file", "does this code follow conventions", "will this reproduce", "check my analysis script", "code review", "review-r", or when the user has an existing .R file and wants quality feedback rather than new code.

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。

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."

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。

Audit economic analysis outputs (fiscal briefings, macro briefings, market research, longlists, and other quantitative economic documents) against methodology standards, academic literature, and common errors. Runs structured checks across core categories including counterfactual, additionality, discounting, double counting, distributional analysis, Aqua Book RIGOUR, and Flyvbjerg-style strategic misrepresentation detection. Returns a RAG scorecard with issues ranked by severity.

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。

Audit and rewrite content to remove AI writing patterns ("AI-isms"). Use this skill when asked to "remove AI-isms," "clean up AI writing," "edit writing for AI patterns," "audit writing for AI tells," or "make this sound less like AI." Supports a detection-only mode that flags patterns without rewriting.

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。

Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step pipeline an applied economist or quantitative social scientist runs on every paper — (1) data cleaning, (2) variable construction & transformation, (3) descriptive statistics & Table 1, (4) statistical diagnostic tests, (5) baseline empirical modeling, (6) robustness battery, (7) further analysis (mechanism, heterogeneity, mediation, moderation), (8) publication-ready tables & figures. **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation via `zepid` / hand-rolled `pandas`, IPTW + g-formula + TMLE doubly-robust triplet via `zepid` / `econml` / `lifelines`, Mendelian randomization via `pymr` / `mrtool` (or `rpy2` → `MendelianRandomization`/`TwoSampleMR`), KM / AFT / Cox survival via `lifelines`, E-value sensitivity, principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `econml.dml` / `doubleml`, S/T/X/R/DR meta-learners via `econml.metalearners` / `causalml`, causal forest via `econml.grf` / `causalml`, Dragonnet / TARNet / CEVAE neural causal via `causalml`, BCF via `pymc-bart` / `bcf-py`, matrix completion, CATE distribution + policy tree via `econml.policy` / `policytree-py`, off-policy evaluation, conformal causal via `mapie`, fairness audit via `fairlearn`, DAG learning via `causal-learn` / `cdt` / LLM-assisted). Prescribes which library to reach for at each step, shows the canonical code, and links to deeper `references/` files for variant-specific patterns. Use when the user asks for a **complete empirical analysis** in Python, wants to replicate an applied-economics paper from scratch, needs a reproducible workflow that is NOT opinionated on any single vertical package (contrast with StatsPAI), wants explicit control over every estimator and diagnostic, or asks "how do I write a full empirical pipeline in Python?". Also triggers when the user names a specific classical step in isolation — "winsorize at 1/99%", "run Breusch-Pagan", "build a Table 1 balance table", "do a placebo test", "event study plot", "mediation analysis" — and wants it wired into the broader pipeline. Mode A triggers on "target trial emulation", "IPTW", "TMLE", "Mendelian randomization", "STROBE", "公共健康", "流行病学". Mode B triggers on "DML", "double machine learning", "causal forest", "meta-learner", "Dragonnet", "BCF", "policy tree", "conformal causal", "fairness audit", "因果机器学习".

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。

Audit an existing verification skill and feature map against source and the running app, then repair proven documentation or harness drift. Use for "maintain the verification skill", "audit the verify skill", or "refresh the verification feature map".

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。

Gate new or changed tests, and audit existing tests for low value, implementation coupling, duplication, and the test-only production seams they keep alive. Use when writing, changing, reviewing, or sweeping tests.

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。

Audit codebases for quality, consistency, and broken patterns — use for pre-release or tech debt 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。

Survey a whole React codebase as a senior React engineer, using React Doctor's scan as evidence, then produce a prioritized audit and self-contained implementation plans for other agents (or cheaper models) to execute. Read-only on source code — it plans improvements, it does not apply them. Use when the user asks to "improve the React code", "audit this codebase", "make this app faster / more robust", or wants a roadmap of fixes rather than a review of a single diff. For a regression check or a fix-it-now pass, use the `react-doctor` skill 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。

Audit and improve SwiftUI runtime performance from code review and architecture. Use for requests to diagnose slow rendering, janky scrolling, high CPU/memory usage, excessive view updates, or layout thrash in SwiftUI apps, and to provide guidance for user-run Instruments profiling when code review alone is insufficient.

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 a project's past Codex sessions, memory files, and existing local skills to recommend the highest-value skills to create or update. Use when a user asks what skills a project needs, wants skill ideas grounded in real project history, wants an audit of current project-local skills, or wants recommendations for updating stale or incomplete skills instead of creating duplicates.

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。

Image SEO audit — make a site's images discoverable in Google Images and stop them from dragging down Core Web Vitals. Audits alt text quality and coverage, descriptive file names, modern formats (WebP/AVIF), dimensions and compression, responsive srcset, lazy-loading, explicit width/height (CLS), image sitemaps, and ImageObject structured data. Use this skill whenever the user asks about image SEO, alt text, Google Images ranking, image optimization for search, image file size hurting page speed, WebP/AVIF, or image structured data. Trigger on: "image SEO", "alt text", "rank in Google Images", "optimize my images for search", "image alt tags", "WebP", "image compression SEO", "image sitemap", "ImageObject schema", or any image-discoverability / image-performance question. For overall page speed use /seo-analysis; for generating new images use the brand image skills.

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。