778 件 ・ 関連度順
概要と使いどころ
Use when creating or editing a persona in src/agent-src/personas/ — voice / focus / unique questions / output expectations — even when the user just says 'add a reviewer voice for X'.
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。
Black-box evaluation of a shipped LLM feature — adversarial probes for hallucination, prompt-injection, and cost-runaway vs stated expectations. Not RAG/embedding. Triggers 'review my chatbot'.
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。
Quando o usuário quer escrever, reescrever ou melhorar copy de marketing para qualquer página — incluindo homepage, landing pages, páginas de preço, páginas de funcionalidade, páginas "sobre" ou páginas de produto. Também use quando o usuário disser "escreva copy para", "melhore essa copy", "reescreva essa página", "copy de marketing", "ajuda com headline" ou "copy de CTA". Para copy de email, veja email-sequence. Para copy de popup, veja popup-cro.
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。
Quando o usuário quer planejar, avaliar ou construir uma ferramenta gratuita para fins de marketing — geração de leads, valor em SEO ou conscientização de marca. Use também quando o usuário menciona "engenharia como marketing", "ferramenta gratuita", "ferramenta de marketing", "calculadora", "gerador", "ferramenta interativa", "ferramenta de geração de leads", "construir uma ferramenta para leads" ou "recurso gratuito". Esta competência conecta engenharia e marketing — útil para fundadores e profissionais de marketing técnico.
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。
Framework de pré-treinamento visão-linguagem que conecta codificadores de imagem congelados e LLMs. Use quando você precisar de legendagem de imagens, resposta a perguntas visuais, recuperação imagem-texto ou chat multimodal com desempenho zero-shot de última geração.
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。
Modelo da OpenAI que conecta visão e linguagem. Permite classificação de imagens com zero-shot, correspondência imagem-texto e recuperação cross-modal. Treinado em 400M pares imagem-texto. Use para busca de imagens, moderação de conteúdo ou tarefas visão-linguagem sem fine-tuning. Melhor para compreensão geral de imagens.
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。
Load this skill whenever the project must support speech input or voice control — dictation, "click by voice" grammars, Dragon NaturallySpeaking, Voice Control (iOS/macOS), Voice Access (Android), or any interface where users activate controls by speaking. Under no circumstances let an aria-label override a visible label without keeping the visible text intact and in order. Absolutely always ensure the accessible name contains the visible label, and load alongside keyboard/SKILL.md since speech tools commonly emulate keyboard and pointer 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。
Validate Commodore 1540/1541/1541C/1541-II compatible disk-drive behavior in EMULATORS, including IEC serial bus protocol, device numbers, KERNAL traps, CBM DOS commands/status, D64 image parsing, BAM/directory/file chains, PETSCII filenames, 6502 drive CPU/ROM/RAM expectations, 6522 VIA wiring, GCR/track/sector geometry, motor/head timing, and compatibility boundaries for 1541-like devices. Use when Codex reviews or edits C64, VIC-20, C264, COMMODORE disk, serial I/O, D64, CBM DOS, IEC, VIA, GCR, or 1541 drive code.
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 for any website, web application, frontend UI, component, layout, styling, refactor, or review. It enforces a self-contained editorial output standard: image-first composition, oversized typography, visible hairline grids, hard-edged rectangular framing, restrained copy, and review.
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。
Strategy map analysis using the Eterdis two-directional causal chain. Use this skill to diagnose whether strategy is coherent by building two chains — bottom-up from resources to results, top-down from expectations to requirements — and finding where they don't connect. Runs in three modes — full diagnostic for deep sessions, review mode for quick re-assessment, and alert triggers for early-warning signals. Updates company-context.md after every run. Triggers on questions about strategic alignment, why the strategy isn't working, what to invest in, how the business creates value, whether the plan is realistic, or where the biggest gap is. Based on practice by Eterdis (eterdis.com).
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 process one to 99 local exact 2:1 equirectangular panorama images with Realsee Argus, producing depth maps, a merged GLB point cloud, camera poses, optional intrinsics, and a validated local output index. Trigger for Argus panorama reconstruction, Argus ZIP input, or explicit Argus start/status/collect lifecycle requests. Do not trigger for panorama editing or stitching, arbitrary-photo 3D generation, existing GLB inspection, or research-only questions.
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。
Elite frontend image-direction skill for generating premium, conversion-aware website design references. CRITICAL OUTPUT RULE — generate ONE separate horizontal image FOR EVERY section. A landing page with 8 sections produces 8 images. Never compress multiple sections into one image. Enforces composition variety (not always left-text / right-image), background-image freedom, varied CTAs, varied hero scales (giant / mid / mini minimalist), narrative concept spine, second-read moments, and a single consistent palette across all images. Optimized for landing pages, marketing sites, and product comps that developers or coding models can accurately recreate.
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。
Estratega de Inteligencia de Dominio de Andru.ia. Analiza el nicho específico de un proyecto para inyectar conocimientos, regulaciones y estándares únicos del sector. Actívalo tras definir el nicho.
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。
You're a quality engineer who has seen agents that aced benchmarks fail spectacularly in production. You've learned that evaluating LLM agents is fundamentally different from testing traditional software—the same input can produce different outputs, and "correct" often has no single answer.
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。
Ingeniero de Sistemas de Andru.ia. Diseña, redacta y despliega nuevas habilidades (skills) dentro del repositorio siguiendo el Estándar de Diamante.
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 model stealing, model inversion, and membership inference performed through inference-API abuse by monitoring query patterns, applying output perturbation, and red-teaming your own model's extractability.
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。
Copy and text skill for antislop. Use when writing or editing prose: headlines, tone, CTAs, and anti-AI-writing patterns. Load with the core.
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。
Remove AI writing patterns from prose. Use when drafting, editing, or reviewing text to eliminate predictable AI tells.
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。
Knowledge base from the NASA Systems Modeling Handbook for Systems Engineering (NASA-HDBK-1009A Rev A, 2025). Use for model-based systems engineering (MBSE) wired into NASA's NPR 7123.1 SE processes: the three aspects of MBSE (language / methodology / framework), the NASA SE Engine and its two OOSEM-derived steps (Model Planning, Setting Up the Model), the Modeling Plan as a SEMP subset, the tool-agnostic NASA SE metamodel of elements and relationships across the four SysML pillars (structure / behavior / requirements / parametrics), worked SysML diagrams and tables for stakeholders, requirements, structure and V&V, generating SE work products (ConOps, MOE, MOP, TPM, V&V), the MBSE Grid framework, alternative modeling approaches (PBR, Scenario, System Specification, Verification), and the ConOps model-content template. Scope is bounded to four common technical processes (Stakeholder Expectation Definition, Technical Requirements Definition, Product Verification, Product Validation). Does NOT teach SysML itself, mandate a tool, define NPR 7123.1 processes, cover technical-management processes 10-17, or reproduce the OMG SysML specification.
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。
Knowledge base from the NASA Systems Engineering Handbook (NASA/SP-2016-6105 Rev 2). Use for NASA's systems-engineering process model — the SE Engine and 17 common technical processes, the program/project life cycle (Pre-Phase A through F), Key Decision Points and technical reviews (MCR/SRR/PDR/CDR/…), stakeholder expectations, technical requirements, design solution, product verification and validation, and crosscutting technical management (planning, risk, configuration, interfaces, data, assessment, decision analysis). Strong for aerospace/spaceflight project SE and lifecycle gating; NASA-specific terminology (KDPs, NPR 7123.1).
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。
Knowledge base from CISA Cross-Sector Cybersecurity Performance Goals 2.0. Use for prioritized IT/OT baseline cyber goals aligned to NIST CSF 2.0 (including GOVERN), critical-infrastructure hygiene, MSP/supply-chain expectations, and board-friendly outcome language. Covers CPG 2.0 goals only; does not replace full CSF programs, sector-specific goals (SSGs), or CSET tooling 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。
Use after installing tradeblazer_skills to collect user preferences for TBQ3/TB language work, including task focus, strategy type, document output location, and review expectations.
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 building custom agent backends, implementing the AG-UI protocol, debugging streaming issues, or understanding how agents communicate with frontends. Covers event types, SSE transport, AbstractAgent/HttpAgent patterns, state synchronization, tool calls, and human-in-the-loop flows.
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。
Ingeniero de Sistemas de Andru.ia. Diseña, redacta y despliega nuevas habilidades (skills) dentro del repositorio siguiendo el Estándar de Diamante.
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。