495 件 ・ 関連度順
概要と使いどころ
Automatically generate TLA+ specifications from program code, repositories, or system implementations. Use when asked to generate TLA+ spec, create TLA+ specification from code, convert program to TLA+, formalize system in TLA+, extract TLA+ model from code, or when working with formal specification of concurrent systems, distributed systems, protocols, algorithms, or state machines that need to be verified.
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。
Structured logging with proper levels, context, PII handling, centralized aggregation. Use for application logging, log management integration, distributed tracing, or encountering log bloat, PII exposure, missing context errors.
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 microservices architectures with service boundaries, event-driven communication, and resilience patterns. Use when building distributed systems, decomposing monoliths, or implementing microservices.
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。
Species abundance estimation using Bracken with Kraken2 output. Redistributes reads from higher taxonomic levels to species for more accurate estimates. Use when accurate species-level abundances are needed from Kraken2 classification 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。
Allocate launch channels using the Owned / Rented / Borrowed (ORB) framework during PRD v0.9 Go-to-Market. Triggers on requests to pick launch channels, distribute the offer, build a channel portfolio, or when user asks "where do we launch?", "what channels?", "ORB", "owned vs rented vs borrowed", "channel allocation", "channel mix", "Corey Haines launch". Outputs GTM-* entries with Type=Channel, a channel-mix matrix, and a launch sequence.
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。
Elixir is a functional language on the Erlang VM (BEAM) for concurrent, fault-tolerant and distributed systems. Use when someone asks to "write Elixir", "build a Phoenix app", "Phoenix LiveView", "GenServer", "supervision tree", "Ecto schema or changeset", "mix project", or needs lightweight processes, "let it crash" reliability and real-time web UIs.
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 are an expert in Deno Deploy, the globally distributed serverless platform by Deno. You help developers deploy TypeScript/JavaScript applications to 35+ edge locations with zero cold starts, built-in KV storage, BroadcastChannel for real-time, cron scheduling, and npm compatibility — running code within milliseconds of users worldwide without managing infrastructure.
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 globally distributed apps with Azure Cosmos DB. Work with multiple data models (document, key-value, graph), configure global replication with tunable consistency levels, manage throughput with RU/s, and query with SQL API.
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 at making systems observable and debuggable. Covers structured logging, metrics collection, distributed tracing, error tracking, and alerting. Knows how to find the needle in the haystack when production breaks at 3 AM. Use when "observability, logging, metrics, tracing, monitoring, error tracking, Sentry, Datadog, OpenTelemetry, debugging production, observability, logging, metrics, tracing, monitoring, sentry, prometheus, opentelemetry" mentioned.
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。
Site reliability specialist for Prometheus metrics, distributed tracing, alerting strategies, and SLO designUse when "observability, monitoring, prometheus, grafana, alerting, slo, sli, metrics, tracing, logging, on-call, incident, observability, prometheus, grafana, tracing, jaeger, alerting, slo, sli, metrics, logging, sre, ml-memory" mentioned.
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。
Patterns for designing, building, and operating microservices architectures. Covers service decomposition, inter-service communication, resilience patterns, data consistency, and observability in distributed systems. Use when ", " mentioned.
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 training models across multiple GPUs or nodes, handling large models that don't fit in memory, or optimizing training throughput - covers DDP, FSDP, DeepSpeed ZeRO, model/data parallelism, and gradient checkpointingUse when ", " mentioned.
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。
World-class backend engineering - distributed systems, database architecture, API design, and the battle scars from scaling systems that handle millions of requestsUse when "backend, api, database, postgres, mysql, mongodb, redis, graphql, rest, authentication, authorization, caching, queue, background job, webhook, migration, transaction, n+1, rate limit, server, node.js, python, go, backend, api, database, architecture, performance, reliability, security" mentioned.
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。
End-to-end content creation workflow that orchestrates research, editorial review, and social distribution agents in sequence. Use when the user wants to create, review, and distribute content through a multi-stage pipeline, or says "/content-pipeline".
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。
End-to-end content creation pipeline from research through editorial review to social distribution. Orchestrates a 3-stage workflow: research, draft/edit, and distribute. Supports blog posts, LinkedIn, Twitter threads, newsletters, and essays.
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。
Coordinate debugging across multiple services. Use when a senior developer needs to trace a distributed issue.
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。
Runs a multi-step procedure as a Python DAG, so ordering, branching and retries are enforced by the runner rather than described in prose a model can generate past. Use for "run these steps in order and retry the flaky one until the check passes", "build a pipeline that fetches, validates, then skips the upload when nothing changed", "make sure these steps cannot be skipped", "resume from where it broke instead of redoing the expensive early stages", "run these independent calls at once and merge the results", or any procedure of 3+ steps with branches, input contracts, or side effects that must not block the critical path. Primitives are depends_on, when=, validate=, retry_until=, detached= and journal_path=. Not for a single sequential call, for steps needing reasoning between them that no predicate captures, or for async and distributed work. To audit whether one verification check can actually go red, use gating. To fan work out across many subagents, use a dynamic workflow.
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。
Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI systems.
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。
When the user wants to create, plan, or optimize a lead magnet for email capture or lead generation. Also use when the user mentions "lead magnet," "gated content," "content upgrade," "downloadable," "ebook," "cheat sheet," "checklist," "template download," "opt-in," "freebie," "PDF download," "resource library," "content offer," "email capture content," "Notion template," "spreadsheet template," or "what should I give away for emails." Use this for planning what to create and how to distribute it. For interactive tools as lead magnets, see free-tools. For writing the actual content, see copywriting. For the email sequence after capture, see emails.
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。
Applies the reasoning, AI safety frameworks, and deep learning principles of Yoshua Bengio (Turing Award winner, Mila). Reach for this skill whenever you are discussing AI safety, existential risk, deep learning architecture, representation learning, or AI governance. Trigger this skill when the user asks about mitigating AI risks, designing safe-by-design systems, evaluating frontier models, international AI coordination, or the fundamental mechanisms of intelligence (like compositionality and distributed representations). Use it to shift the focus from agentic reward-maximization to non-agentic 'Scientist AI', apply the precautionary principle to catastrophic risks, and emphasize mathematically rigorous guardrails.
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。
Applies the reasoning of Zhong Lin Wang (nanotechnology pioneer, Georgia Tech) to problems involving energy harvesting, IoT power scaling, sensor networks, and fundamental physics applications. Reach for this skill whenever the user is discussing self-powered systems, scaling distributed hardware, overcoming battery bottlenecks, or translating fundamental scientific phenomena (like static electricity or mechanical strain) into novel engineering applications. It is highly relevant for hardware roadmapping, optoelectronics, piezotronics, and challenging established scientific assumptions (like classical Maxwell's equations) to model dynamic systems.
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。
Applies the engineering and research philosophies of Jeff Dean, Chief Scientist at Google DeepMind and Google Research. Reach for this skill whenever you are designing large-scale distributed systems, optimizing latency and energy efficiency, or making architectural decisions about machine learning infrastructure. It should trigger automatically for topics involving hardware-ML co-design, model distillation, sparse activation, massively multi-task models, or scaling systems by 5x to 10x. Use this skill to evaluate system bottlenecks, transition from specialized to unified models, and optimize experimental velocity. Apply his mental models to avoid premature 100x scaling and to treat AI models as reasoning engines rather than memorization databases.
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。
Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI systems.
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。
End-to-end content creation and publishing. Takes a topic (or generates one), drafts in the user's voice, gets approval via Telegram, and publishes to Medium via browser automation. Invoke with "publish a post about X", "write and publish to Medium", "content-publish", or when an ego-dispatched session needs to create and distribute content.
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。