545 件 ・ 関連度順
概要と使いどころ
Use when creating, refreshing, packaging, inspecting, promoting, or deprecating a named per-seat starting point — Agent Starter manifest authoring, the 6-state lifecycle (captured → named → inspectable → used → promoted → deprecated), provenance honesty, and refusal rules. NOT a VM image; a managed starting point composed from agent role + startup context + optional native session source + provenance.
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 opening OpenRig fleet terminals into cmux — turning a rig, pod, mission, slice, or saved view into live agent tiles via `rig terminal --provider cmux`, or driving cmux on an agent's request. Same OpenRig view semantics as openrig-herdr (the verbs, honest-partial/degrade, read-only cross-rig, scroll/copy, same-size-only duplicates); cmux is the **best-effort** provider (herdr is the default and the proof-gated one). Prefer openrig-herdr unless cmux is specifically wanted.
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 reasoning about the rig lifecycle operations family (create / start / stop / resume / restore / snapshot / release / unclaim / destroy), reading or trusting `rig ps` / lifecycle projections after recovery, or designing proof for a lifecycle scenario. Covers the 4 failure modes (auto-restore creates partial rig; projections report healthier than reality; provider auth treated as impl work; resume succeeds for one runtime fails another) plus the restore-honesty rule (failed resume is FAILED loudly — no auto fresh fallback).
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。
Detects misplaced classes and fixes component hierarchy problems — finds code that should belong inside a component but sits at the root level. Use when asking "clean up component structure", "find orphaned classes", "fix module hierarchy", "flatten nested components", or analyzing why namespaces have misplaced code. Do NOT use for dependency analysis (use coupling-analysis) or domain grouping (use domain-identification-grouping).
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 work out of 10 against a clear rubric, then improve it round by round until every item reaches the target score the user named (for example "get it to 8 out of 10", "bring everything under 6 up to 6+"). Covers anchored rubrics, measurable criteria, independent or blind judging, before/after scorecards, and honest reporting of anything still short. Use whenever the user asks to score, rate, grade or judge something out of 10, names a target score, or asks "what would make this a 9?".
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 redesign SaaS and AI product landing pages where a real workflow, interface, or deterministic demo is the central proof. Use for pale atmospheric shells, product UI in the hero, prompt-to-output stories, audience tabs, compact feature modules, honest pricing comparisons, and FAQ handoffs.
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。
Art-direct and implement distinctive, motion-rich marketing, editorial, portfolio, and landing websites with original reference-inspired imagery, standout heroes, GSAP choreography, one smooth-scroll engine, optional Three.js shaders, honest icon and logo sourcing, photo avatars, accessibility, and performance safeguards. Use when a user asks for an Awwwards-quality, premium, cinematic, interactive, high-concept, or motion-led website, or explicitly requests this visual and motion system.
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。
Detect, suggest, and evaluate GoF design patterns in TypeScript/JavaScript codebases. Use when refactoring code, applying singleton/factory/observer/strategy patterns, reviewing pattern quality, or finding stack-native alternatives for React, Angular, NestJS, and Vue.
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 helps users extract structured data from Reddit posts and comments via BrowserAct API. Agent should proactively apply this skill when users express needs like analyzing competitor mentions on Reddit, tracking brand sentiment in Reddit comments, extracting Reddit discussions for market research, finding popular Reddit posts by keywords, monitoring community feedback on specific topics, gathering user reviews from Reddit threads, searching for Reddit posts within a specific date range, sorting Reddit discussions by relevance or hotness, compiling nested Reddit comments for deep analysis, building a structured dataset of Reddit conversations, discovering trending topics in specific subreddits, or monitoring social media activity for specific brands on 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。
Collects every tweet in an X (Twitter) conversation thread given a conversation id (root tweet id) — the focal tweet plus all replies, sub-replies, and quote chains — and returns normalized per-tweet data with text, author, engagement counts, media, hashtags, mentions, in_reply_to mapping, and cursor for pagination. Use when user mentions Twitter conversation, X conversation thread, conversation_id, thread scraper, scrape Twitter replies, all replies to a tweet, replies under a tweet, sub-replies, nested replies, thread harvester, get replies of a tweet, scrape comments on Twitter, scrape comments on X, full thread extraction, conversation export, conversation tree, reply chain, thread dump, X tweet thread, twitter thread scrape, comment scraping twitter, comment scraping x, focal tweet plus context, root tweet plus replies. Also applies to sentiment analysis on a single viral tweet, controversy mapping, harvesting community Q&A threads, capturing AMA threads, recovering long-running discussions, and any paginated bulk reply collection driven by a conversation id.
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。
MUST USE for C#/.NET deterministic tests that require the smallest production seam for DateTime/Task.Delay/File/Environment/Guid/Random, static API preservation, nested/parallel overrides, or no real I/O. USE ONLY when the target workspace contains C# source plus a .csproj or .sln. DO NOT USE for audits, bulk migration, code that already has an injectable seam, or an explicit migration to a user-named existing abstraction (migrate-static-to-wrapper). Use instead of general test generation when the requested test is impossible without a production edit and seam selection is still open.
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 TLS/SSL and DNS misconfigurations — missing HSTS (downgrade attack), weak cipher suites, expired/invalid certificates, mTLS bypass, missing SPF/DKIM/DMARC (email spoofing), DNS Zone Transfer (AXFR), dangling CNAME subdomain takeover, CAA records. Most of these are Info/Low on their own — this skill is opinionated about which findings actually pay (spoofable DMARC with delivered-to-inbox proof, AXFR returning internal hosts, dangling-CNAME takeover) versus which get rejected as best-practice noise (missing CAA, missing HSTS with no MitM position). Use during recon to find infrastructure weaknesses, and to TRIAGE them honestly before reporting.
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 Node.js specific vulnerabilities — Prototype Pollution → RCE chains (lodash/merge/assign), Express trust proxy misconfiguration, child_process/eval injection, template engine SSTI (EJS/Pug/Handlebars), path traversal in file servers, require() injection, environment variable exfil via /proc/self/environ. Use when target runs Node.js/Express/Fastify/NestJS/Koa.
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 structural econometric models. Use when the user is building, estimating, or debugging structural models — including BLP demand estimation, dynamic discrete choice, auction models, or any workflow involving moment conditions, nested fixed-point algorithms, or MPEC formulations. Triggers on "structural model", "moment conditions", "NFXP", "MPEC", "BLP", "random coefficients", "dynamic discrete choice", "CCP", "Rust model", "auction estimation", "GMM objective", "inner loop", "contraction mapping", or convergence/starting value problems in optimization-based estimation.
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 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。
Use when the user asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics paper (AER / QJE / JPE / ReStud / AEJ) with DID / RD / IV / SCM / DML / matching, written-out estimating equation + identifying assumption, Table 1 / Table 2 / event-study figure / robustness gauntlet — OR in epidemiology / public health style (target-trial emulation, IPTW + g-formula + TMLE triplet, Mendelian randomization, KM/AFT survival, E-value sensitivity, STROBE/TRIPOD reporting) — OR in ML causal inference style (DML, S/T/X/R/DR meta-learners, causal forest, Dragonnet/TARNet/CEVAE, BCF, CATE distribution, policy learning, conformal causal, fairness audit, causal discovery) — OR in distributional / gap-decomposition style (Oaxaca–Blinder `sp.oaxaca`, Kitagawa `sp.kitagawa_decompose`, DiNardo–Fortin–Lemieux `sp.dfl_decompose`, Gelbach `sp.gelbach`, Fairlie `sp.fairlie`, RIF / FFL `sp.rif_decomposition`, all reachable through the `sp.decompose` dispatcher). Also covers exporting multi-column regression tables to Word / Excel / LaTeX (Stata outreg2 / esttab / R modelsummary equivalent) and bundling an entire replication appendix into one .docx / .xlsx / .tex file. Triggers on keywords "StatsPAI", "statspai", "AER empirical analysis", "applied micro pipeline", "Table 1 balance", "event study", "first-stage F", "Oster bound", "honest_did", "spec_curve", "callaway_santanna", "dragonnet", "text as treatment", "outreg2 in Python", "regression table to Word/Excel", "sp.regtable", "sp.collect", "sp.paper_tables", "sp.feols", "summary_col", "modelsummary", "AER style table", "QJE style table", "epidemiology pipeline", "target trial emulation", "g-formula", "IPTW", "TMLE", "Mendelian randomization", "STROBE", "TRIPOD", "公共健康", "流行病学", "DML", "double machine learning", "causal forest", "meta-learner", "CATE", "conformal causal", "policy learning", "因果机器学习", "ML causal", "decomposition", "Oaxaca-Blinder", "Kitagawa", "DiNardo-Fortin-Lemieux", "DFL", "Gelbach", "RIF decomposition", "wage gap decomposition", "sp.decompose", "sp.oaxaca".
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。
中英双语学术降 AIGC / bilingual academic de-AIGC skill. Removes AI-generated writing signatures from empirical papers in economics, management, and the social sciences — in both English and Chinese. Covers Turnitin AI, GPTZero, Originality.ai on the English side and 知网 AMLC, 万方, 维普 on the Chinese side. Uses a six-step loop (intake → audit → claim-evidence check → differentiated rewrite → five-dimension self-score → cold-reader recheck) with two pattern libraries (22 English + 17 Chinese patterns), section-by-section strategies for empirical papers, and hard protections that keep every number, coefficient, and citation intact. Since 2026-08 it also carries a provenance layer (去水印): deterministic, CJK-safe cleaning of invisible-character carriers and file metadata (docx / png / jpg / svg / pdf, incl. C2PA), an honest account of Claude's statistical text watermark, and an author "ownership pass" — it never claims a text is watermark-free.
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。
Records an architecture decision as a Nygard/MADR-shaped ADR under decisions/ — capturing the context that forced the choice, the options weighed, the decision itself, and its consequences in both directions, plus the supersedes chain that keeps a decision log honest. Read when asked to record an architecture decision, write an ADR, log the decision we made, document why we chose X over Y, capture this decision for the record, or supersede an old decision with a new one. Do NOT read to frame a proposal or explore an idea not yet decided (frame-a-proposal), to write a spec or implementation plan (write-a-spec), to write an incident postmortem (write-a-postmortem), or to judge whether a design is sound (review-a-design). This skill records a decision already made; it does not make 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。
Frame a new design proposal (RFC-shape) under proposals/ — problem before solution, named beneficiary and observable change, real alternatives, honest drawbacks, and a live open-questions backlog. Read when asked to frame a proposal, write an RFC, propose a design, pitch a change, draft a PRD-style design doc, or open a design proposal for review. Do NOT read to record a decision after it is accepted (use record-a-decision), to write an implementation spec (use write-a-spec), to write a postmortem (use write-a-postmortem), or to review or critique an existing design (use review-a-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。
Generate a JSON Schema for a C# type name via reflection. Supports primitives, enums, arrays, generic collections, dictionaries, and complex objects. Knobs control inclusion of nested `$defs` and whether type-level / property-level descriptions are emitted.
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。
Destroy a GameObject (and all nested children) in the currently opened Prefab or active Scene. Returns the destroyed GameObject's name, path, and instance ID for confirmation. Use 'gameobject-find' to locate the target first.
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。
Read 微信公众号 articles from mp.weixin.qq.com links using available web or browser tools. Report verification walls honestly; reading alone does not modify notes or queues.
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。