501 件 ・ 関連度順
概要と使いどころ
Frame an epic as a testable hypothesis with target user, expected outcome, and validation method. Use when defining a major initiative before roadmap, discovery, or delivery planning.
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Feature planning and implementation with 4 adaptive phases (Specify, Design, Tasks, Execute). Auto-sizes depth by complexity. Writes testable requirements in EARS notation, atomic tasks, atomic Conventional Commits, and requirement traceability. Ships deterministic Python validation scripts so structural gates are enforced by code, not memory. Features an independent Verifier (author != verifier, evidence-or-zero), a discrimination sensor, a decision log (STATE.md), a test-coverage matrix, and a self-improving lessons layer. Stack-agnostic and tool-agnostic. Use when (1) planning features, (2) implementing with verification and atomic commits, (3) validating an implementation against a spec. Triggers on "specify feature", "discuss feature", "design", "tasks", "implement", "validate", "verify work", "UAT", "record decision", "pause work", "resume work". Do NOT use for pure architecture decomposition analysis or standalone technical design documents.
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Assess release health from Sentry errors, regressions, affected users, crash evidence, and comparison with the previous stable release.
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OKX Agentic Wallet — the single skill for the user's wallet and on-chain execution. Use it whenever the user wants to operate their wallet or execute an on-chain action, including: login & accounts, balance / holdings, wallet address / deposit / receive, send / transfer, contract calls (approve / deposit / withdraw), transaction history & status, message signing, wallet export & policy; pay gas with a stablecoin (Gas Station, Solana); swap / trade / buy / sell / convert, get a quote; cross-chain bridge & track arrival; limit orders (buy dip / take profit / stop loss / buy above) plus cancel / list / resume them; broadcast / gas / simulate / track a transaction; look up any public address's holdings; security scanning (token / honeypot 蜜罐 / 貔貅, DApp phishing, tx & signature checks, approvals); audit log. Once matched, follow this skill's Intent Routing to dispatch to the exact action.
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Complete guide for Squads Protocol - Solana's leading smart account and multisig infrastructure. Covers Squads V4 Multisig for team treasury management, Smart Account Program for account abstraction and programmable wallets, and Grid for stablecoin rails and fintech infrastructure.
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Explain code, concepts, or system behavior with adjustable depth levels
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ALWAYS USE when asked to migrate, replace, or make testable existing C# static calls with a named wrapper or built-in abstraction: DateTime.UtcNow/Now or DateTimeOffset.UtcNow to TimeProvider/IClock, File.* to IFileSystem or an existing store such as ITextFileStore, and Environment.* to an existing reader such as IEnvironmentReader. Covers scoped files/projects, constructor injection, replacing temp-file or process-environment tests with fakes, "already registered" abstractions, and static classes whose callers/signatures must stay unchanged. Preserves DateTimeKind and call count. DO NOT USE for finding statics (detect-static-dependencies), choosing/designing a new wrapper (generate-testability-wrappers), behavior tests with no chosen seam (testability-obstacle), or test-framework migration.
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Explore a codebase to find opportunities for architectural improvement, focusing on making the codebase more testable by deepening shallow modules. Use when user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more AI-navigable.
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Assesses a research paper outline (.qmd file) against structured criteria from Kosuke Imai's empirical research guide. Evaluates the abstract and section outline for: research question clarity, stated contributions, hypotheses and testable implications, data description, planned results and inferential approach, and robustness checks. Detects causal papers from the abstract and conditionally assesses identification strategy only when causal framing is present. Produces a structured markdown report with PASS/PARTIAL/FAIL assessments and actionable suggestions. Use when asked to assess, evaluate, review, or check a paper outline, abstract-and-outline, or research plan in a .qmd file.
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Generate testable hypotheses. Formulate from observations, design experiments, explore competing explanations, develop predictions, propose mechanisms, for scientific inquiry across domains.
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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".
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Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic.
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Promote existing research into a stable-status canonical article under `articles/` in a Knowledge Base project (the `knowledge-base` starter pack). Read when a decision has actually been made and the team wants the source-of-truth written down, or when asked to consolidate, canonicalize, promote research, or supersede an older article. Carries the decision-confirmation gate, the `supersedes:` chain that keeps the evidence trail intact, and the canonical voice. Does not conduct new research — that is the sibling `research-with-sources` skill.
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Cut and maintain Observal release branches, prepare alpha, beta, RC, stable, and patch releases, backport merged main PRs, inspect release status, verify published artifacts, and recover failed release operations. Use for Observal maintainer release tasks and release-process changes, not for installing or upgrading an existing Observal deployment.
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Build a composable CLI for Codex from API docs, an OpenAPI spec, existing curl examples, an SDK, a web app, an admin tool, or a local script. Use when the user wants Codex to create a command-line tool that can run from any repo, expose composable read/write commands, return stable JSON, manage auth, and pair with a companion skill.
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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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Implement and review React, React Native, and TypeScript UI for maximum practical render performance through leaf-local updates, stable identities, honest Hook semantics, and minimal effects. Use for components, hooks, callbacks, context, external stores, memoization, list rows, large dependency arrays, callback or state fanout, re-render reduction, and React Compiler-aware performance work.
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Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic.
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Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.
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Create evidence-based Meta ad creative briefs, copy angles, and testable asset concepts for Facebook and Instagram. Use when asked for Meta ad creative, Facebook ads copy, Instagram ad assets, UGC concepts, creative briefs, new ad concepts, or creative refreshes.
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Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.
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Guidelines for writing and reviewing Insiders and Stable release notes for Visual Studio Code.
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Open-ended scientific ideation partner. Use for research gaps, mechanism exploration, interdisciplinary connections, assumptions, possible research directions, and lightweight literature matrix or A+B paper-combination idea mapping. For structured testable hypotheses and validation plans, use hypothesis-generation instead.
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Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Also owns explicit HypoGeniC-style or automated LLM-driven hypothesis generation/testing requests inside this single skill. For open-ended ideation use scientific-brainstorming.
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