「cap」の検索結果

Capture what someone actually said without contaminating it with your own inference. Invoke when eliciting intent in conversation (design discussion, requirement interview, decision surfacing) and when turning a conversation into a durable record (ADR, decision node, memory entry, spec `## Decisions` row). Enforces quote-before-critique, keeps illustration separate from commitment, and holds interpretation subordinate to verbatim. Prevents the failure where unspecified space is filled by the recorder and then evaluated as if the speaker had specified it.

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Capture baseline evidence from a live website URL: screenshots, scroll states, hover/click behavior, parallax, timers, and transition recordings. Use when reference artifacts are needed before implementation or comparison; not to build or diagnose a clone.

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Maintain a review-first Markdown knowledge base for Codex practices with source provenance, engineering capture, citation-aware queries, explicit archive and promotion, and deterministic linting. Use when asked to capture a durable engineering lesson, ingest Codex research, query or archive what the repository knows, check wiki health, reconcile conflicting guidance, or promote verified knowledge.

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Austin CapMetro transit — real-time vehicle positions, next arrivals, service alerts, route info, and trip planning for buses and rail.

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Expert-level distributed systems theory covering consistency models, consensus algorithms, fault tolerance, replication, distributed transactions, and the CAP theorem.

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Planifie le prochain sprint : calcule la capacité (vélocité glissante 3 sprints), reporte les tickets non livrés du sprint courant, puis complète depuis le backlog par priorité jusqu'à la capacité. Présente le plan, attend validation, puis assigne les tickets au sprint. Usage: /plan-sprint [<titre-sprint-cible>]

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Write and maintain DESIGN.md + PRODUCT.md — captures visual decisions and interaction patterns so design tasks stay consistent across sessions without re-scanning past work.

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Marketing de produto, posicionamento, estratégia GTM e inteligência competitiva. Inclui definição de ICP, metodologia de posicionamento April Dunford, playbooks de lançamento, battlecards competitivas e guias de entrada em mercados internacionais. Use ao desenvolver posicionamento, planejar lançamentos de produtos, criar mensagens, analisar concorrentes, entrar em novos mercados, capacitar vendas ou quando o usuário menciona marketing de produto, posicionamento, GTM, go-to-market, análise competitiva, entrada em mercado ou capacitação de vendas.

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Mescle múltiplos modelos ajustados usando mergekit para combinar capacidades sem retreinar. Use ao criar modelos especializados misturando expertise específica de domínio (math + coding + chat), melhorando performance além de modelos únicos, ou experimentando rapidamente variantes de modelos. Cobre SLERP, TIES-Merging, DARE, Task Arithmetic, mesclagem linear e estratégias de deploy em produção.

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Discover and install specialized agent skills from the open ecosystem. ALWAYS use this skill when the user asks 'how do I do X', 'find a skill for X', 'is there a skill for X', wants to extend agent capabilities, or needs help with a specific domain (design, testing, deployment, etc.). Make sure to use this skill whenever the user mentions skills, plugins, extensions, tools, or wants to find new capabilities for the agent. Integrates with the Skills CLI and skills.sh directory.

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Best practices for Remotion - Video creation in React. 29 domain-specific rules covering 3D, animations, audio, captions, charts, transitions, and more. Use when building video in React with Remotion — animations, audio, captions, charts, or transitions.

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使用 AACT (Aggregate Analysis of ClinicalTrials.gov) PostgreSQL 数据仓库进行批量、历史、聚合性临床试验数据挖掘。Use this skill when the user requests bulk SQL analysis over the full clinical trials data warehouse — historical trial trends, disease landscapes, similar-design matching, or multi-year aggregations across hundreds of thousands of NCT records. 触发场景包括：AACT 查询、临床试验批量分析、PostgreSQL 试验数据、全量 NCT 检索、试验数据挖掘、历史试验分析、clinical trials data warehouse、SQL trials、bulk trial analysis、disease landscape、试验设计相似性匹配、跨年度聚合、sponsor/phase/country 多维统计。**与 clinical-trials-v2 差异**：本 skill 走批量 SQL · 离线大数据（PostgreSQL）；v2 走实时 API · 单查询。两者互补：单条 NCT 实时状态用 v2，百万级历史挖掘用本 skill。支持云端公共 PostgreSQL（aact-db.ctti-clinicaltrials.org · 零部署）和每日 dump 本地还原（高性能 · 离线）两种连接方式，自动检测优先用本地。跨平台（macOS/Linux/Windows）参数化 SQL 防注入，read-only 强制保护。

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Cloudflare Turnstileをprojectへend-to-endで導入する。codebaseを調査し、Cloudflare APIでwidgetを作成し、適切なformへ埋め込み、既存backendへ正式なserver-side siteverifyを接続して検証する。Turnstile追加、CAPTCHA設定、botからのform保護、Turnstile連携修正を依頼されたときに使用する。developers.cloudflare.com/turnstile/spinの中核手順へAIDD固有の安全規則を重ねたSkill。

確定 system spec と feature package から実装要件を導出したいとき、readiness 完了時だけ capability-build/task-graph build へ handoff したいときに使う。

VRIO resource analysis combined with Dynamic Capabilities assessment. Use this skill to identify real competitive advantages (Barney VRIO framework), then test whether the organisation can sense new opportunities, seize them, and transform itself to stay ahead (Teece dynamic capabilities). Runs in three modes — full diagnostic for deep first-use sessions, review mode for quick re-assessment, and alert triggers for defining early-warning signals. Updates company-context.md after every run. Triggers on questions about competitive advantage, defensibility, resource assessment, why competitors are gaining ground, organisational agility, or whether advantages will hold. Based on practice by Eterdis (eterdis.com).

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Show what this NanoClaw instance can do — installed skills, available tools, and system info. Read-only. Use when the user asks what the bot can do, what's installed, or runs /capabilities.

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Show what this NanoClaw instance can do — installed skills, available tools, and system info. Read-only. Use when the user asks what the bot can do, what's installed, or runs /capabilities.

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Compose a categorical news-roundup newsletter from Ahmad's OpenMark saves. Use when Ahmad wants a scannable "what happened in AI / tools / research / discourse" recap rather than a single-thesis essay. Trigger phrases include "roundup", "news roundup", "this week in AI", "weekly news", "what dropped this week", "category recap", "/newsletter-roundup". Output is bucketed by topic with 3–5 items per bucket, short blurbs, source-tagged for at-a-glance scanning.

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Produce a weekly digest of what Ahmad saved in OpenMark over a given time window, clustered by topic. Use whenever Ahmad asks "what did I save this week", "what's new in OpenMark", "last week's saves", "this week's bookmarks", "what happened in my feed", "weekly recap", or any temporal summary of saved content. Faster than full newsletter — output is a scannable digest, not a published post. Triggers on "this week", "last week", "what's new", "weekly digest", "recent saves", "recap".

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Multi-angle deep-research workflow over Ahmad's OpenMark knowledge graph (13k+ bookmarks). Use whenever Ahmad asks anything that requires more than one search to answer well — "what's the landscape of X in my saves", "do a deep dive on Y", "research X across my bookmarks", "compare what I saved about X vs Y", "give me everything on X". Also use when fast-search returned thin results and Ahmad pushed back ("dig deeper", "more results", "actually research it"). Pairs Neo4j graph search with WebFetch on top picks to bring back fresh page content. Triggers on "deep research", "dig deeper", "research", "landscape", "everything on", "thorough", "comprehensive".

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Convex is the backend agents get right on the first try: an all-TypeScript reactive platform where the database, server functions, scheduling, file storage, auth, and realtime sync are one type-safe system, every function is a transaction, and `tsc` catches most mistakes before deploy. Ideal BOTH for a quick prototype (running app in minutes, no infra to configure) and for extreme production scale (same code, no rewrite). Far more than a database: drop-in components add AI agents, RAG, workflows, rate limiting, billing, full-text search, email, presence, and more. Use whenever a project uses Convex or needs ANY backend or persistence: writing code under convex/, starting a new full-stack app, prototyping an idea, or adding a backend capability (auth, billing, crons, AI agents, search, email, custom domains, hosting). Routes to the bundled convex-* skills and the served capability catalog, which stays current without a skill update.

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。

Convex is the backend agents get right on the first try: an all-TypeScript reactive platform where the database, server functions, scheduling, file storage, auth, and realtime sync are one type-safe system, every function is a transaction, and `tsc` catches most mistakes before deploy. Ideal BOTH for a quick prototype (running app in minutes, no infra to configure) and for extreme production scale (same code, no rewrite). Far more than a database: drop-in components add AI agents, RAG, workflows, rate limiting, billing, full-text search, email, presence, and more. Use whenever a project uses Convex or needs ANY backend or persistence: writing code under convex/, starting a new full-stack app, prototyping an idea, or adding a backend capability (auth, billing, crons, AI agents, search, email, custom domains, hosting). Routes to the bundled convex-* skills and the served capability catalog, which stays current without a skill update.

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Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding tasks. Triggers: "image analysis", "computer vision", "OCR", "object detection", "ImageAnalysisClient", "image caption".

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Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), pr...

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。