「facts」の検索結果

Generate reusable multi-step agent workflow blueprints. Use for trigger/action orchestration, deterministic workflow definitions, and automation handoff artifacts.

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 sitemap and wireframe specifications from project-config.json and produce UX planning artifacts.

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。

Define legal scope, rules of engagement, and authorization artifacts for downstream pentest execution.

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 reproducible ML experiment runs with explicit parameters, metrics, and artifacts. Use before model training to standardize tracking-ready experiment definitions.

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。

Retest remediated findings, detect regressions, and generate remediation status and certification artifacts.

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 orchestrate authorized Nmap host discovery, port and service enumeration, NSE profiling, and reporting artifacts for in-scope targets.

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。

Prepare deployment configs, final checks, and pre-launch readiness artifacts for production release.

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。

Actively enumerate in-scope hosts and services, then produce normalized network mapping artifacts.

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。

Builds and improves task-specific decision artifacts from application requirements and labeled records. Use for data assembly, primitive and base-model selection, fitting, export/reload, inference policy, or bounded data/model/program improvement. Includes rules and no-training outcomes; does not require a rung ladder or training a general Jev.

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。

Generic read-only checklist for preparing an application deployment by verifying host, artifacts, backups, service state, configuration, dependencies, and rollback readiness before any write action.

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 editing Chinese web fiction, narrative prose, or story drafts that sound AI-generated, overly polished, formulaic, explanatory, or too symmetrical; use to detect AI-writing fingerprints and revise only wording while preserving plot, facts, characters, names, and intent.

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。

GStack design-review method for reviewing rendered UI, landing pages, decks, or visual artifacts for hierarchy, layout, accessibility, and polish.

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 splitting content work between a raw drafting model/window, an editor-in-chief, and the user's final taste review. It keeps prompts loose enough for judgment while preserving facts, account boundaries, and publish gates.

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-only workflow for discovering uploaded deployment artifacts, comparing them with current deployment state, and preparing a safe update and rollback 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。

Design and implement unit, conformance, integration, timing, boot, trace, video, audio, and regression tests for EMULATORS processors, chips, peripherals, and complete machines. Use when creating a verification strategy for a new emulator, converting hardware documentation into tests, adding diagnostic programs or golden artifacts, measuring behavioral coverage, or proving that components interact correctly.

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。

SOP for the automated refinement loop (/refine) workflow. Trigger when: - Exhaustively auditing and optimizing pre-existing codebase artifacts. - Applying iterative contraction mapping to reduce code entropy. - Prompt contains: /refine, refinement loop, contraction mapping, fixed-point sweep.

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Day-to-day operation runbook for nuxt-crouton apps — boot any app/POC locally with auth working, seed and inspect the local database (including the miniflare-vs-.data split that makes seeded data "disappear"), turn on the review overlay / eruda, observe deployed staging (tail, smoke, row counts), and capture screenshots. Use when asked to "run the app", "boot velo/fanfare/a poc locally", "why can't I log in locally", "seed the database", "the seed data doesn't show up", "look at the local db", "check what's on staging", "screenshot the app", or "where do the artifacts land".

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。

Author a complete both-seats coaching lesson for a BBA-NATIVE bidding scenario and produce all served artifacts in one consistent pass: curate the pool → tokenized coaching-curated/<scn>.pbn → gated promote to served coaching/<scn>.pbn → resolve to South=student coaching-non-rotated/<scn>.pbn (bridge-classroom) → update both toc.json files. Invoke as `/coach-lesson <Scenario>` (e.g. `/coach-lesson Fourth_Suit_Forcing`). Use ONLY for conventions BBA bids natively (its bba/<scn>.pbn [Auction] is the answer key — Basics, New_Minor_Forcing, Fourth_Suit_Forcing). For conventions BBA CANNOT bid (Spiral Raises), use the py/spiral_auction.py substitution path instead — not this skill.

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。

Resolve, validate, and audit manuscript integrity and applicable requirements for academic manuscripts and paper artifacts. Use when Codex needs to determine active paper rules from manuscript state, paper type, venue, submission stage, or workflow mode; lint a LaTeX paper project; assess submission readiness; create or update a paper policy profile; explain why a rule is active; or validate the paper-policy registries. Do not use as the primary skill for prose drafting, peer review, rebuttal writing, citation discovery, or figure/table creation.

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。

Draft and revise evidence-grounded academic manuscripts with adaptable author preferences. Use for section writing, implementing structural edits, claim calibration, citation-integrated prose, prose cleanup, standalone review excerpts, and LaTeX template migration. Use paper-review for critique or author responses, and paper-figures-tables for final visual artifacts.

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。

Draws an interactive HTML Architecture Viewer for any code flow the user names. Walks through seven research-driven steps (flow, name, repo research, lens colors, evidence flags, lens+mechanism per stop, build) and verifies links, claims, and generated artifacts before delivery.

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Default material, information-density hierarchy, clean-rendering, negative-prompt hygiene, and evidence-preserving photo-repair layer for IM2 / GPT Image 2 / gpt-image-2 / image_gen. Use for clean generation/redraws, artifact cleanup, focal-detail control, atmospheric depth, physically coherent light paths, air scattering, volumetric light, light ratio, material realism, architecture/terrain/hard-surface scene mass and structural grounding, and 企业/公司活动/门店/团队合影/商品/餐饮/证件文档照片修复. Preserve identity, headcount, placement, product facts, text, logo, QR, date, price, and scene evidence; prefer residual blur over invented restoration detail.

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。

Conduct reproducible web searches and decision-oriented research across official sites, search engines, news, papers, forums, social platforms, and public datasets. Use when Codex must investigate a person, organization, product, market, technical resource, reputation, career choice, or other current topic; compare candidates; verify claims; collect dated citations; distinguish facts from inference and anecdote; or write an evidence-backed research report.

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 distinctive, production-grade frontend interfaces with high design quality. Generates creative, polished code that avoids generic AI aesthetics. Use when the user asks to build web components, pages, artifacts, posters, or applications, or when any design skill requires project context.

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。