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概要と使いどころ
Inspect one bounded Proto UI pull-request slice, produce canonical review input, and route each eligible exact head toward review and integration. Use for review queues, linked work, evidence gaps, merge conflicts, stale revisions, or portfolio health. This evidence leaf is read-only; authorized review and integration continue in their registered leaves.
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。
Audit an already completed protected Proto UI publication and reconcile immutable external facts with Version entities, release records, and public projections. Use after publication for registry, tag, GitHub Release, asset, checksum, workflow-head, and deployment evidence. Never republish or replace immutable evidence.
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。
Record one authorized non-remediation terminal outcome in Proto UI's autonomous-maintenance state. Use after a supported no-finding result, an independently rejected finding, or a blocked mission whose terminal evidence is complete.
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。
Establish Proto UI repository state, live contributor authority, Agent comprehension scope, task risk, and authorization before work begins. Use at the start of a development session, before autonomous task selection, after a material repository update, or when permission or assessment state may have changed.
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。
Run Proto UI's dynamic comprehension assessment and derive an unsigned local task-fit result. Use when an Agent needs review calibration or a fresh autonomous task and review ceiling. Do not use a self-result as identity, permission, or acceptance evidence.
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。
Select, run, and report proportional Proto UI validation for a completed change. Use before review, handoff, commit, or pull request updates, and after resolving validation failures. Cover focused evidence first, then affected graph, types, docs, packaging, consumers, release, or repository checks according to risk.
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 whenever the user wants to use AI agents to work with Penpot design files via the Penpot MCP Server. Triggers include: using Penpot through an AI agent, design files, design systems, design tokens, Penpot MCP, design-to-code, generating UI from design, auditing a design system, creating components/variants, renaming layers, exporting assets from Penpot, adding flows, interactions, animations, overlays, or prototyping in Penpot, or prompting an AI agent to read/modify a Penpot file. Also triggers when the user wants to set up Penpot MCP, connect any MCP-compatible AI agent or IDE to Penpot, or produce production-ready HTML/CSS/React from a Penpot design. Use this skill for Penpot-agent workflows — design, code, audit, prototyping, or setup.
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。
Open protocol for AI agent interoperability enabling standardized communication between agents, applications, and humans across different frameworks
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。
Advanced prototype pollution playbook — server-side RCE, client-side gadgets, filter bypasses, and detection techniques. Companion to ../prototype-pollution/ for basics. Use when you've confirmed pollution and need to escalate to code execution or find framework-specific gadgets.
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。
Prototype pollution testing for JavaScript stacks. Use when user input is merged into objects (query parsers, JSON bodies, deep assign), when configuring libraries via untrusted keys, or when hunting RCE gadgets via polluted Object.prototype in Node or the browser.
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。
Network protocol attack playbook. Use when exploiting layer 2/3 protocols including ARP spoofing, LLMNR/NBT-NS/mDNS poisoning, WPAD abuse, DHCPv6 attacks, VLAN hopping, STP manipulation, DNS spoofing, IPv6 attacks, and IDS/IPS evasion.
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。
Rapid prototyping workflow. Skips normal standards to quickly validate a game concept or mechanic. Produces throwaway code and a structured prototype report.
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。
Manage persistent coding sessions across Claude Code, Codex, Antigravity (agy), Grok Build, and OpenCode engines. Use when orchestrating multi-engine coding agents, starting/sending/stopping sessions, running multi-agent council collaborations, cross-session messaging, ultraplan deep planning, ultrareview parallel code review, autoloop autonomous workspace iteration, ultraapp building deployable web apps from a structured Q&A interview, switching models/tools at runtime, exposing the orchestrator's 78 tools as an MCP server to Hermes Agent / Claude Desktop / Cursor / Cline / Continue / Zed / Windsurf / Goose, or running as an Agent Client Protocol (ACP) agent that Zed / JetBrains / Neovim / Emacs / VS Code / dsh can drive directly. Triggers on "start a session", "send to session", "run council", "ultraplan", "ultrareview", "autoloop", "ultraapp", "Forge tab", "build a web app", "one-click app", "AppSpec", "autonomous iteration", "iterate until goal", "auto research", "switch model", "hand off a session", "session handoff", "switch engine", "continue on codex", "continue on claude", "move this session to another engine", "multi-agent coding", "coding session", "session inbox", "grok session", "grok build", "opencode", "mcp server", "clawo-mcp", "hermes mcp", "model context protocol", "ultracode", "dynamic workflow", "fanout", "fan-out", "best-of-N", "steer turn", "interrupt turn", "fork thread", "rollback turns", "acp", "agent client protocol", "clawo acp", "zed agent", "jetbrains agent", "external agent", "dsh subagent", "deepseek harness", "clawo runs", "run ledger", "session cost", "coding-agent token usage", "spend cap", "budget limit", "maxBudgetUsd", "durable workflow", "resume a run", "verify a coding run", "acceptance contract", "evidence bundle", "did the agent's tests actually pass", "protected tests", "agent edited the tests", "human gate", "repair loop", "clawo workflow", "clawo verify", "clawo solve", "clawo fanout", "fix and verify", "solve from the terminal".
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。
Generates Google Cloud Monitoring Server-Driven UI (SDUI) Widget and XyChart Protocol Buffer textprotos from resolved PromQL or ListTimeSeries queries. Use when: - Generating valid google.monitoring.dashboard.v1.Widget textprotos, containing PrometheusQuery or TimeSeriesFilter datasets, for use with the Cloud Monitoring Dashboards API, gcloud CLI, or declarative dashboard definitions. - Synthesizing Server-Driven UI (SDUI) widget titles, axis labels, and plot types for Prometheus or ListTimeSeries queries. Don't use for: - Metric discovery or PromQL query generation. For those tasks, use the cloud-monitoring-metric-selection or cloud-monitoring-promql-query skills.
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。
Genera las vistas estáticas navegables del producto (landing, auth, app, legales, errores y las propias del mapa de pantallas) usando el design system de design/, sin lógica de backend. Úsalo en el paso "prototipar" del sprint de definición o cuando haya que maquetar vistas nuevas (p. ej. "haz el prototipo", "maqueta las vistas", "crea la landing estática").
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。
Orchestrator for the Product Design skill set — turn early ideas into reviewable prototypes. Routes to the right sub-skill (get-context, research, audit, ideate, image-to-code, url-to-code, prototype, qa, share) based on where you are in the design process. Use when the user gives a high-level design goal like "design a UI for X", "prototype this idea", "rebuild this screen", "review our flow", or isn't sure which design step they need. Delegates to design-* skills.
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 Kafka consumer groups that survive rebalances, hit the right delivery guarantee (at-most/at-least/exactly-once), and handle poison messages without stalling the topic. Use when picking a partition assignment strategy (Range vs Sticky vs Cooperative-Sticky vs the new KIP-848 protocol), tuning heartbeat / session / max.poll.interval timeouts, choosing manual vs auto offset commits, designing dead-letter or retry topics, or migrating from Classic to the next-gen Consumer protocol. NOT for Kafka cluster ops (broker tuning, partition reassignment), schema design (Avro/Protobuf/Schema Registry), or stream processing topology (use a kafka-streams skill).
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。
FIPA standard library of agent interaction protocols for structured multi-agent communication
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。
Analyze bonded agent systems, escrow workflows, slashing rules, oracle trust, Sybil cost, and front-running risk in protocols where agents post bonds or stake reputation. Use for collateral sizing, griefing analysis, dispute design, and bonded-agent security reviews. NOT for smart contract auditing, DeFi tokenomics, blockchain consensus design, or trading strategy.
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。
Handle mental health crisis situations in AI coaching safely. Use when implementing crisis detection, safety protocols, emergency escalation, or suicide prevention features. Activates for crisis keywords, safety planning, hotline integration, and risk assessment.
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-level computer networks covering OSI model, TCP/IP stack, routing protocols, transport layer, application protocols, network security, and software-defined networking.
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 command injection attacks against Modbus TCP/RTU protocol in ICS environments by monitoring for unauthorized write operations, anomalous function codes, malformed frames, and deviations from established communication baselines using ICS-aware IDS and protocol deep packet inspection.
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 covers detecting anomalies in Modbus/TCP and Modbus RTU communications in industrial control systems. It addresses function code monitoring, register range validation, timing analysis, unauthorized client detection, and deep packet inspection for malformed Modbus frames. The skill leverages Zeek with Modbus protocol analyzers, Suricata IDS with OT rules, and custom Python-based detection using Markov chain models for normal Modbus transaction sequences.
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。
プロトタイプの媒体選択ガイド（判断軸）。この検証は Figma でやるべきか、コードで作るべきか、併用かを決める。proto-loop のステップ2、およびデザイン検討の入口で「Figmaとコードどっちでやる？」に迷ったら適用する。基準の核は「止まっている絵で判断できるなら Figma、動かさないと分からないならコード」。