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概要と使いどころ
Evaluate HackerOne / Bugcrowd / Intigriti programs before committing time. Decide signal-to-noise, payout history, response time, scope size, and competition level. Use when the user is considering a new bug bounty target or asking "should I hunt this".
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。
This skill should be used when the user asks to "build a scroll animation", "add ScrollTrigger", "pin a section while scrolling", "scrub an animation to scroll", "create a hero timeline", "do a horizontal scroll section", "split text and animate words", "animate a layout change with GSAP Flip", "sync ScrollTrigger with Lenis", "Lenis smooth scroll jittery", "ScrollTrigger markers drift", "ScrollTrigger pins break with Lenis/Locomotive", or "set up smooth scroll with GSAP pinning". Covers GSAP timelines, ScrollTrigger, SplitText, Flip, and Lenis/Locomotive smooth-scroll sync for code-driven web motion.
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。
AgentCore CDK・デプロイ・ランタイム統合のナレッジ。Runtime作成、JWT認証、SSE、Dockerfile、コンテナ管理、Browser Tool等
Use when verification depends on agent runtime phases, queued input, tool-call lifecycle, save points, retry, abort, compaction, or branch behavior.
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 checking Agent Brain inside a real agent runtime or adapter before trusting harness usability.
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 a command, adapter, or runtime smoke depends on proving an agent runtime's capabilities before trusting command routing, writes, shell access, or validation claims.
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。
Evaluates whether work-in-design is ready to hand off from a chat design conversation to autonomous execution (Claude Code, n8n, agent runtime). Runs a 7-condition gate: spec stability, verification surface, invariants, pump-primer, work decomposition, rollback cost, token budget headroom. Three invocation modes — deliberate (caller provides work_context, returns JSON verdict), proactive sensing (Claude detects handoff signals in design conversation and offers the check), conversational walkthrough (Claude walks the 7 conditions as discussion topics). Returns structured JSON in all modes. Use when designing a Claude Code deployment, scoping an autonomous run, or deciding next move. Trigger on: "ready to hand off," "ready for execution," "is this CC-ready," "handoff check," "walk me through readiness," "are we ready to ship this," "should I just hand this off," or proactive detection. Do NOT use for evaluating completed work, runtime selection, or prompt quality. Profile-agnostic.
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 a local coding agent (Codex, Claude Code, Cursor, or similar) needs to operate Multica through the authenticated `multica` CLI: reading or updating issues, comments, metadata, labels, custom properties, subscribers, projects, agents, squads, runtimes, repos, skills, autopilots, attachments, or workspace state; searching issues; inspecting or cancelling agent runs; replying to a Multica issue from an external agent; creating or triaging issues; checking linked pull requests; or safely handling Multica mention/status side effects without relying on the Multica hosted agent runtime.
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 and analyze discrete or continuous Markov chains including transition matrix construction, state classification, stationary distribution computation, and mean first passage times. Use when modeling a memoryless system with observed transition counts or rates, computing long-run steady-state probabilities, determining expected hitting times or absorption probabilities, classifying states as transient or recurrent, or building a foundation for hidden Markov models or reinforcement learning MDPs.
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 a complete EU/DACH relocation timeline with dependency mapping between bureaucratic steps, deadline tracking, and country-specific procedure identification. Use when planning a move between EU/DACH countries, relocating from a non-EU country to an EU/DACH destination, coordinating employment-based relocation with employer HR, managing a relocation with tight deadlines, or when needing a single document that maps the entire relocation process end-to-end.
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 structured insect behavior observations using sampling protocols, ethogram categories, event recording, interaction logging, environmental context, and summary analysis. Covers focal animal sampling, scan sampling, all-occurrences sampling, and instantaneous sampling methods. Defines a standard insect ethogram with locomotion, feeding, grooming, mating, defense, communication, and rest categories. Includes timestamped event recording, intraspecific and interspecific interaction logging, environmental covariate documentation, and time budget analysis. Use when studying insect behavior for ecological research, documenting behavioral repertoires for a species, observing pollinator activity or predator-prey dynamics, or supporting conservation assessments with behavioral data.
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 a hiking tour with trail selection by difficulty (SAC/UIAA), time estimation using Munter's formula, elevation analysis, and safety assessment. Covers multi-day hut-to-hut tours, day hikes, and alpine routes with terrain classification and group fitness considerations. Use when planning a day hike or multi-day trekking tour, selecting trails appropriate for a group's fitness and experience, estimating realistic hiking times, or planning hut-to-hut tours with overnight logistics.
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 sustainable on-call rotations with balanced schedules, clear escalation policies, fatigue management, and handoff procedures. Minimize burnout while maintaining incident response coverage. Use when setting up on-call for the first time, scaling a team from 2-3 to 5+ engineers, addressing on-call burnout or alert fatigue, improving incident response times, or after a post-mortem identifies handoff issues.
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。
Deploy machine learning models to production serving infrastructure using MLflow, BentoML, or Seldon Core with REST/gRPC endpoints, implement autoscaling, monitoring, and A/B testing capabilities for high-performance model inference at scale. Use when deploying trained models for real-time inference, setting up REST or gRPC prediction APIs, implementing autoscaling for variable load, running A/B tests between model versions, or migrating from batch to real-time inference.
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。
Implement AI-powered anomaly detection for operational metrics using time series analysis (Isolation Forest, Prophet, LSTM), alert correlation, and root cause analysis. Reduce alert fatigue by intelligently identifying true anomalies in system metrics, logs, and traces. Use when operations teams are overwhelmed by alert volume, when detecting complex multi-metric anomalies beyond static thresholds, when seasonal patterns make thresholds ineffective, or when needing to predict issues proactively before they impact users.
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 a blameless post-mortem analysis after an incident. Build timeline reconstruction, identify contributing factors, and generate actionable improvements. Focus on systemic issues rather than individual blame. Use after any production incident or service degradation, following a near-miss, when investigating recurring issues, or to share systemic learnings across teams.
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。
Capture outbound HTTP and telemetry from a CLI harness at runtime. Covers capture-channel selection (transcript file vs verbose-fetch stderr vs outbound proxy vs on-disk state), hook-driven per-event capture vs long-running session capture, JSONL output format for diff-friendly artifacts, and the observability table that maps each target to the cheapest channel that captures it. Use when a static finding needs runtime confirmation, when a payload shape is needed for a client re-implementation, or when dark-vs-live disambiguation requires watching what the binary actually sends.
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 general-purpose Dockerfiles for Node.js, Python, Go, Rust, and Java projects. Covers base image selection, dependency installation, user permissions, COPY patterns, ENTRYPOINT vs CMD, and .dockerignore. Use when containerizing an application for the first time, creating a consistent build/runtime environment, preparing an app for cloud deployment or Docker Compose, or when no existing Dockerfile is present in 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。
Create multi-stage Dockerfiles that separate build and runtime environments for minimal production images. Covers builder/runtime stage separation, artifact copying, scratch/distroless/alpine targets, and size comparison. Use when production images are too large, when build tools are included in the final image, when you need separate dev and prod images from one Dockerfile, or when deploying to constrained environments like edge or serverless.
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-agnostic SQLAlchemy 2.0 core shared by fastapi-plugin and flask-plugin: declarative mapped classes with Mapped/mapped_column, column type selection, relationships with explicit lazy loading and cascades, 2.0-style select() querying, transaction/flush discipline, and Alembic-agnostic migration metadata rules. Framework plugins layer their delta skills (async sessions for FastAPI, Flask-SQLAlchemy integration for Flask) on top of this skill. Use this skill to: - Write SQLAlchemy 2.0 declarative models with Mapped[T] annotations and mapped_column(). - Pick correct column types (String(N), Numeric, DateTime(timezone=True), Uuid, Enum). - Define relationships with explicit lazy loading strategy and cascade settings. - Query with 2.0-style select() statements and manage flush vs commit boundaries. - Keep model metadata visible to Alembic autogenerate. Do NOT use this skill for: - FastAPI async engine/session lifecycle — see fastapi-plugin:sqlalchemy-patterns. - Flask-SQLAlchemy extension setup and Flask-Migrate — see flask-plugin:sqlalchemy-patterns. - Python idioms — see python-foundation:python-conventions.
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 market environment analysis and reporting tool. Analyzes global markets including US, European, Asian markets, forex, commodities, and economic indicators. Provides risk-on/risk-off assessment, sector analysis, and technical indicator interpretation. Triggers on keywords like market analysis, market environment, global markets, trading environment, market conditions, investment climate, market sentiment, forex analysis, stock market analysis, 相場環境, 市場分析, マーケット状況, 投資環境.
Deep emotional sentiment analysis beyond positive/negative classification
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 reducing C/C++ compilation times with ccache, sccache, distcc, unity builds, precompiled headers, split DWARF, IWYU, or link time reduction.
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。
Implement or extend one governed Proto UI Module semantic slice. Use when Module ownership, facade and port boundary, dependencies, resource lifetime, Host Capability plan, Runtime registration, Adapter evidence, and validation scope are fixed by current authority. Do not use to admit a new Module or decide ownership.
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。