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Save durable decisions, findings, plans, and implementation knowledge from the visible Kimi Code conversation into a new or existing NeatContext context. Use only when the user explicitly invokes this skill or asks to preserve the current conversation as reusable 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。
Create a fresh NeatContext context from a user-defined behavioral profile and an existing local knowledge folder. Use only when the user explicitly invokes this skill or asks to create a new context rather than save the current conversation.
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。
Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, perform an action, and optionally produce structured JSON output.
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。
Set up, audit, and debug analytics tracking implementation — GA4, Google Tag Manager, event taxonomy, conversion tracking, and data quality. Use when building a tracking plan from scratch, auditing existing analytics for gaps or errors, debugging missing events, or setting up GTM. Trigger keywords: GA4 setup, Google Tag Manager, GTM, event tracking, analytics implementation, conversion tracking, tracking plan, event taxonomy, custom dimensions, UTM tracking, analytics audit, missing events, tracking broken. NOT for analyzing marketing campaign data — use campaign-analytics for that. NOT for BI dashboards — use product-analytics for in-product event analysis.
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。
Comprehensive REST API design review with automated linting, breaking-change detection, and design scorecards. Catches inconsistent conventions, missing versioning, and design smells before APIs ship. Use when reviewing a PR that adds or changes API endpoints, auditing an existing API for v2 migration, or establishing API standards for a team.
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。
Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
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。
/cs:aims-audit <scope> — ISO/IEC 42001 AIMS internal-audit 6-question forcing interrogation. Use before certification stage 1, before annual internal audit cycles, or when onboarding a new AI system into an existing AIMS.
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。
Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.
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。
Diff a live page's accessibility violations against a baseline — by default compares uncommitted changes (stash-based), or pass --branch [<name>] to diff against a branch. Reports only new violations introduced, violations fixed, and pre-existing count. Use `scan` for a full audit with no diffing.
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。
Map Delphi classes to a relational database using TMS Aurelius ORM attributes. Use when the user asks to create entity classes, add Aurelius mapping to existing classes, fix or review mapping attributes, explain how a class is mapped, or work with associations, inheritance, automapping, nullable fields, blobs, or composite identifiers. Triggers on requests like "create Aurelius entities for...", "map this class to Aurelius", "add ORM mapping", "fix the mapping on this class", "how do I map a one-to-many in Aurelius".
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。
Three-tier memory layer for the orchestrator. Routes scratch (file-tier), WHERE (QMD), and WHY (MemPalace) through the existing code-memory-router slash command. Never edits planning files directly. Every WHY-tier promotion goes through the HITL gate.
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。
Central entry point of the afonsoft agent harness. Use when starting a new project, resuming an existing one, planning features/Epics/releases, or running any multi-step agent-driven work. Validates and reconciles SPECs (SDD), audits the codebase and harness for gaps (security, architecture, performance, hygiene), proposes improvements, fragments work into GitHub Issues, delegates implementation/QA/review to specialized skills, and re-validates everything until delivery. Also use to review unapproved SPECs, reconcile open GitHub Issues with code, or run a final gap check before closing a 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。
Expert SwiftUI guidance for macOS apps — covers writing and reviewing code with runtime-level understanding of observation, concurrency, performance, and platform integration. Use when user asks to "build a SwiftUI view", "review my SwiftUI code", "fix observation issues", "bridge AppKit", "architect state management", "debug view updates", or works on any macOS SwiftUI project — building features, reviewing existing code, debugging view updates or performance, fixing concurrency issues, architecting state management, bridging AppKit, or making design decisions. Do NOT use for iOS/iPadOS-only SwiftUI, UIKit bridging, or basic Swift language questions. Focuses on non-obvious traps and deep patterns, not basics.
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。
Bilingual JD/CV/Resume tailoring and resume polish skill for job applications. Use when the user provides a job description, JD, role posting, existing CV/resume, LinkedIn profile, portfolio, or career notes and wants to optimize, rewrite, polish, or customize a resume/CV for ATS keywords, recruiter screening, interview-ready evidence, and role fit. 适用于根据招聘 JD、岗位描述、目标公司、现有简历/CV/LinkedIn/profile 进行简历优化、简历润色、岗位匹配、ATS 关键词映射、经历深挖、项目证据提炼和中英文求职简历定制。
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 briefs, mockups, and product context into Stitch UI screens, design systems, variants, prototypes, Tailwind-friendly HTML, and screenshots. Use when the user wants to explore a screen, edit an existing Stitch project, compare visual directions, build a multi-screen prototype, or save local design/code-export artifacts from natural-language input.
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 README.md for the current project by analyzing the codebase structure, package files, and existing documentation. Use when asked to write a README, create documentation, or document the project.
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 TaskBelay for substantial development that benefits from saved progress, verification reuse or interruption recovery, or when explicitly requested. Reuse existing user authority. Skip ordinary questions and small mechanical edits unless requested.
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。
[Workflow] Turn an idea or existing canonical specs into a reviewed provisional spec or reviewed, readiness-checked, prioritized planned tasks and stories; stop before implementation. --from=idea|spec --to=spec|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。
[Project Management] Use for explicit project-owned tracker guidance (--mode=guide), or portable-engine work records, placement, linked concerns, lifecycle, evidence, acceptance and local status. Existing engine modes and bare inspect remain supported.
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。
Build, extend, validate, and publish a ChatGPT Sites application whose durable database and backend functions run on Convex. Use when a user invokes $codex-sites-convex, selects this skill from the skill picker, asks for a ChatGPT Site backed by Convex, or asks to add Convex data, realtime queries, mutations, authentication, files, schedules, or actions to an existing ChatGPT Sites project.
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 whether a feature request fits the existing codebase — check for duplicates, contradictions, scope creep, and architectural misfit
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 extracting Voice-of-Customer themes from existing artefacts — GH issues, PR threads, Sentry patterns. Triggers on 'what are users saying', 'recurring complaints', 'top themes'.
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 condensing, decondenseing, refactoring, or improving existing skills. Covers the full skill lifecycle from verbose → sharp → maintained.
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 decomposing a company objective into team OKRs, auditing a draft OKR tree, or stress-testing an existing one for measurability and laddering.
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。