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Academic paper writing skill with 12-agent pipeline. v2.5: Style Calibration (learn author's writing voice from past papers) + Writing Quality Check (writing quality checklist for natural prose). Supports IMRaD, literature review, theoretical, case study, policy brief, and conference paper structures. APA 7.0 (default), Chicago, MLA, IEEE, Vancouver citation formats. Bilingual abstracts (zh-TW + EN). Multi-format output (LaTeX, DOCX, PDF, Markdown). Triggers on: write paper, academic paper, paper outline, write abstract, revise paper, check citations, convert to LaTeX, guide my paper, parse reviews, revision roadmap, 寫論文, 學術論文, 論文大綱, 寫摘要, 修改論文, 檢查引用, 引導我寫論文, 帶我規劃論文, 逐章規劃, 論文架構, 審查意見, 修訂路線圖.

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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。

Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid + did_imputation + eventstudyinteract + sdid + rdrobust + rddensity + synth + synth_runner + psmatch2 + teffects + ebalance + coefplot + esttab + asdoc + binscatter. **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 Stata pipeline an applied economist runs on every paper — (1) data import & cleaning (use/import, destring, misstable, duplicates, merge assert), (2) variable construction (gen/egen/winsor2/xtile/xtset with L./F./D.), (3) descriptive statistics & Table 1 (tabstat/balancetable/asdoc), (4) classical diagnostic tests (sktest/swilk/hettest/imtest/xtserial/xttest3/vif/dfuller/kpss/hausman/estat overid), (5) baseline modeling (reg/xtreg/reghdfe/ivreg2/ivregress/csdid/did_imputation/eventstudyinteract/sdid/rdrobust/synth/psmatch2/teffects/heckman/qreg/ppmlhdfe), (6) robustness battery (bacondecomp/honestdid/rwolf/ritest/wildbootstrap/oster), (7) further analysis (subgroup/triple-diff/interactions/medsem/marginsplot/binscatter by group), (8) publication-ready tables & figures (esttab/outreg2/estout/coefplot/marginsplot/rdplot/twoway combined). **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation, IPTW + g-formula + TMLE doubly-robust triplet via `teffects ipw` / `teffects ipwra` / `teffects aipw` / `eltmle`, Mendelian randomization via `mrrobust` (IVW / Egger / weighted median) and `mregger` / `mrpresso`, KM / Cox / AFT / RMST survival via `sts` / `stcox` / `streg` / `strmst2`, E-value sensitivity via `evalue` (Linden-Mathur), principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `ddml` / `pdslasso`, S/T/X/R/DR meta-learners via `crforest` and `ddml interactive`, causal forest via `crforest` / `cforest`, BART/BCF via `bart` / `bartCause`-style externals, CATE distribution + policy tree via `crforest`, off-policy evaluation, conformal causal externals, fairness audit, DAG learning via `pcalg` / external Python callouts). Use when the user asks for a complete Stata empirical analysis, wants a reproducible .do-file pipeline, needs a Stata counterpart to the Python StatsPAI / Full-empirical-analysis-skill, or names a specific Stata step in isolation ("run reghdfe with two-way clustering", "csdid event study", "winsor2 at 1%", "esttab to LaTeX", "coefplot with CI", "ivreg2 weak-IV test", "synth_runner placebos", "teffects psmatch balance check"). Mode A triggers on "target trial emulation Stata", "teffects ipw aipw", "eltmle", "mrrobust", "mregger weighted median", "stcox AFT survival", "strmst2", "evalue Stata", "STROBE Stata", "公共健康 Stata", "流行病学 Stata". Mode B triggers on "ddml Stata", "pdslasso", "crforest causal forest Stata", "policy tree Stata", "因果机器学习 Stata".

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 modern tidyverse + econometrics R ecosystem — dplyr + tidyr + haven + fixest + sandwich + lmtest + clubSandwich + AER + ivreg + did + bacondecomp + HonestDiD + eventstudyr + rdrobust + rddensity + Synth + gsynth + synthdid + MatchIt + WeightIt + cobalt + ebal + grf + DoubleML + mediation + marginaleffects + modelsummary + kableExtra + gt + ggplot2 + ggpubr + cowplot + binsreg. **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 R pipeline an applied economist runs on every paper — (1) data import & cleaning (read_dta/read_csv, naniar, janitor, validate-merges), (2) variable construction (mutate/across/winsorize/group_by + lag/lead with dplyr), (3) descriptive statistics & Table 1 (gtsummary, modelsummary::datasummary, tableone), (4) classical diagnostic tests (shapiro/jarque.bera.test/bptest/dwtest/bgtest/vif/adf.test/kpss.test/Hausman), (5) baseline modeling (fixest::feols, ivreg, did::att_gt, eventstudyr, sun_ab, did_imputation, synthdid, rdrobust, MatchIt, WeightIt, grf::causal_forest, DoubleML, mediation), (6) robustness battery (modelsummary stack, clubSandwich CRSE, fwildclusterboot, ri2, robomit Oster, bacondecomp, HonestDiD), (7) further analysis (interactions + marginaleffects, mediation::mediate, gsem via lavaan, dose-response splines, grf CATE), (8) publication-ready tables & figures (modelsummary, kableExtra, gt, stargazer, texreg, flextable to LaTeX/Word/HTML; ggplot2 + ggpubr + cowplot + binsreg + iplot for figures). **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation, IPTW + g-formula + TMLE doubly-robust triplet via `WeightIt` / `gfoRmula` / `tmle` / `ltmle`, Mendelian randomization via `MendelianRandomization` / `TwoSampleMR` / `MRPRESSO`, KM / Cox / AFT / RMST survival via `survival` / `survminer` / `flexsurv`, E-value sensitivity via `EValue`, principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `DoubleML`, S/T/X/R/DR meta-learners via `causalweight` / `grf`, causal forest via `grf::causal_forest`, BART/BCF via `bartCause` / `bcf`, matrix completion via `MCPanel`, CATE distribution + policy tree via `policytree`, off-policy evaluation, conformal causal via `conformalInference` / `cfcausal`, fairness audit via `fairmodels`, DAG learning via `pcalg` / `bnlearn` / LLM-assisted). Use when the user asks for a complete R empirical analysis, wants a tidyverse-style reproducible R script / Quarto workflow, prefers fixest over reghdfe, needs the R counterpart to StatsPAI / 00.1 / 00.2, or names a specific R step in isolation ("feols with cluster", "MatchIt nearest neighbor", "bacondecomp in R", "gtsummary table 1", "modelsummary to Word"). Mode A triggers on "target trial emulation R", "tmle ltmle", "MendelianRandomization", "TwoSampleMR", "MRPRESSO", "survival cox AFT", "STROBE R", "EValue R", "公共健康 R", "流行病学 R". Mode B triggers on "DoubleML R", "grf causal forest", "policytree", "bartCause bcf", "conformal causal R", "fairmodels", "pcalg NOTEARS", "因果机器学习 R".

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。

Complete security architecture overhaul for claude-flow v3. Addresses critical CVEs (CVE-1, CVE-2, CVE-3) and implements secure-by-default patterns. Use for security-first v3 implementation.

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。

Plan and build 2D game maps and scenes - top-down and side-scrolling levels, tilemaps, prop packs, parallax backgrounds and HD-2D battle or story plates - with collision, navigation and reachability checks, environment motion, a playable HTML preview and export to Tiled, Godot 4 or LDtk. Map art (terrain, tiles, props, plates, parallax layers) is image generation by default, through generate2dmedia route_media.py (the API when a key is configured, else the local Codex or Grok CLI); map data (layout, collision, exits, spawns) is written as data. Use for any map, level, room, scene, background, terrain or prop-kit request. Not for characters, creatures, items or FX (generate2dsprite), character animation (video2dsprite), code-drawn tiles or maps (codeart2d, only on request) or calling an image or video API by itself (generate2dmedia).

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Explicit-only last resort. Draws game art from code, with no image model - pixel sprites from a PixelSpec, flat vector props, UI and icons from portable SVG, rigged characters with planted feet, FX with a JS runtime, seam-proven autotiles, playable top-down layouts with collision, stylized parallax and ambient plate motion - palette-exact, with QA and review sheets. Use only when the user explicitly asks for code-drawn art, or when generate2dmedia route_media.py finds no image or video route (no-route, exit 3). Never the default - image generation (generate2dsprite, generate2dmap, video2dsprite) comes first for characters, props, maps and FX. Not for painterly, detailed or likeness art, HD-2D plates or realistic fire, smoke or water. Always disclose the result as code-drawn.

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。

Turn one approved master still into a character's whole animated sprite set - one image-to-video clip per action (idle, walk, run, attack, jump, hurt, cast and so on), all from that still, generated through generate2dmedia route_media.py (the xAI API when a key is configured, else the local Grok CLI) - then gate each take, key it with a soft matte, register it to the master, pick loops or retime actions, finish (HD by default, pixel on request) and package fixed-canvas frames, alpha WebM, packed MP4 for iPhone and a PNG fallback with engine metadata and a JS runtime. Also processes a supplied clip. Use for character and creature animation, fluid motion and any video-to-sprite work. Not for FX, icon or prop sheets (generate2dsprite), maps or plate loops (generate2dmap), code-drawn animation (codeart2d, only on request) or calling a video API by itself (generate2dmedia).

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。

Mark an opened scene as the Editor's active scene (the one new GameObjects are added to and that's used as the default for many operations). Use 'scene-list-opened' to enumerate opened scenes first.

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Compiles and executes C# code dynamically using Roslyn. Supports a full-code mode (default) and a body-only mode — see the skill body for the difference and for how to pass Unity object references as parameters.

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List the fully-qualified C# type names of every concrete `UnityEngine.Component` subclass available in the project. Paginated (default 5/page, max 500). Use this to find a valid `componentName` for 'gameobject-component-add'.

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Create a new Material asset with default parameters at a given 'Assets/'-rooted path ending in '.mat'. Creates intermediate folders if missing. Use 'assets-shader-list-all' to find a valid `shaderName`.

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Verify assumptions before implementing large or risky changes to APIs, provider data, billing, pricing, quotas, or defaults. Use when a mistake could affect customers or the user asks if a change is safe to ship. Checks real-world impact beyond passing tests.

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Turn almost any file into Markdown plus metadata — PDF, Office, HTML, email, archives, images, audio/video, 1000+ formats — powered by Apache Tika, either via the tika-app CLI (zero setup, one file) or a running tika-server (curl, warm process, many calls). Leads with rmeta (structured, embedded-item-aware output) as the default operation rather than flat concatenated text, since you can't tell whether a file has embedded content from its extension. Covers metadata-only triage, type and language detection, OCR, and output-size discipline for agent context. Use whenever a task involves reading the content of a file whose format you don't want to hand-parse.

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。

Queue microsoft/apm pull requests labelled `panel-review` (or an explicit named list) and fan them out through an isolated pool (default 2) of autopilot-pr-review-worker sessions. Advisory only. Never implements. Never composes autopilot-pr-merge-worker. Never review every open PR. Works in a local session, Copilot App automation, Cloud Agent, Remote Agent, or Agentic Workflow. Does not triage issues or open greenfield PRs.

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。

Queue open microsoft/apm pull requests (community fixes, PRs without an issue, or PRs that close an issue) and fan them out through an isolated pool (default 2) of autopilot-pr-triage-worker sessions. Advisory only. Never merges, never assigns, never requests reviewers, never runs autopilot-pr-review-worker. Activate on a mixed PR list or queue-open when the job is PR triage. Works in a local session, Copilot App automation, Cloud Agent, Remote Agent, or Agentic Workflow.

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 this skill to queue maintainer-accepted microsoft/apm issues (`status/accepted`) and fan them out through an isolated pool (default 2) of autopilot-issue-delivery-worker sessions. Any accepted type is eligible. Advisory `triage/recommended` is not authorization. Does not triage. Does not review PRs. Works in a local session, Copilot App automation, Cloud Agent, Remote Agent, or Agentic Workflow.

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。

Drive ONE already selected open pull request in microsoft/apm to mergeable. Classifies copilot-pull-request-reviewer[bot] inline review, runs autopilot-pr-review-worker, folds (by default) every recommendation inside the PR's stated scope, pushes to the head branch or a superseding PR that preserves authorship via commit trailers, watches CI to green (`agent-merge` when that skill or `<agent_merge_state>` is present; otherwise `gh pr checks --watch`), and iterates under fixed caps until ready-to-merge, advisory-with-deferred, superseded, or blocked. Plan before any fold, push, or reviewer request. Never spawned by autopilot-pr-review-scheduler. That scheduler is advisory only. Invoke this skill by name when the caller asked to drive a PR to merge.

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。

Queue open microsoft/apm issues and fan them out through an isolated pool (default 2) of autopilot-issue-triage-worker sessions. Advisory only. Never implements, never assigns, never requests reviewers. Activate on a mixed issue list or queue-all when the job is triage. Works in a local session, Copilot App automation, Cloud Agent, Remote Agent, or Agentic Workflow.

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。

Refactor and review SwiftUI view files with strong defaults for small dedicated subviews, MV-over-MVVM data flow, stable view trees, explicit dependency injection, and correct Observation usage. Use when cleaning up a SwiftUI view, splitting long bodies, removing inline actions or side effects, reducing computed `some View` helpers, or standardizing `@Observable` and view model initialization patterns.

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Backlink / off-page SEO audit. Works WITHOUT a paid tool by default — it uses the Google Search Console Links report (your top linking sites, most-linked pages, and anchor text) plus any backlink export the user provides (Ahrefs / Semrush / Majestic / DataForSEO CSV). Analyzes referring-domain profile, anchor-text distribution (over-optimized / spammy patterns), most-linked pages vs. money pages (internal-linking opportunity), toxic/spam link signals, and link-building priorities. Use this skill when the user asks about backlinks, link building, off-page SEO, referring domains, anchor text, toxic/spam links, disavow, or their link profile. Trigger on: "backlinks", "backlink audit", "link building", "off- page SEO", "referring domains", "anchor text", "toxic links", "spam links", "disavow file", "who links to me", "link profile", "domain authority". NOTE: rich third-party metrics (DR/DA, full link index) require a paid data source; this skill is explicit about what it can and can't see without one.

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。

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.

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 the user asks to create or edit videos end-to-end (script→video, auto-cut/jumpcut, captions/subtitles, polishing for Shorts/Reels/TikTok). Current implemented backend: local FFmpeg (probe/render/jumpcut/burn-subtitles/polish). Planned/optional backends: Remotion (motion graphics templates), VectCutAPI (CapCut/剪映 timeline editing), and video-audio-mcp (MCP tool wrapper) when available. Produces a finished video artifact (MP4 by default) from assets + copy + a design/storyboard plan.

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。

Perform language and framework specific security best-practice reviews and suggest improvements. Trigger only when the user explicitly requests security best practices guidance, a security review/report, or secure-by-default coding help. Trigger only for supported languages (python, javascript/typescript, go). Do not trigger for general code review, debugging, or non-security tasks.

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。