3,647 件 ・ 関連度順
概要と使いどころ
Execute implementation tasks from the current plan. Works through tasks sequentially, marks completion, and preserves progress for continuation across sessions. Use when user says "implement", "start coding", "execute plan", or "continue implementation".
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。
Execute implementation tasks from the current plan. Works through tasks sequentially, marks completion, and preserves progress for continuation across sessions. Use when user says "implement", "start coding", "execute plan", or "continue implementation".
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。
不具合報告（GitHub Issue、フォーラム投稿、社内報告）を起点に、調査・再現・修正・検証・記録までを一貫実行するスキル。症状の切り分け、原因仮説の整理、最小再現、修正実装、ExecPlan作成、PR下書き、ナレッジ追記が必要なときに使う。
ロードマップ（`.agent/roadmap.md`）の依存順を基準に次の未完了タスクへ着手するスキル。完了時のチェック反映、ExecPlan未作成タスクの計画作成先行、実装開始までを一貫して行う。
ロードマップ（`.agent/roadmap.md`）を運用するスキル。タスクの追加・変更・削除・次タスク着手・完了同期を一貫して扱い、ExecPlan未作成タスクは着手前に計画を作成する。
Interactively gather parameters for `python -m cli.inference.throughput_optimizer`, generate a deployment simulation command, explain assumptions, ask for execution confirmation, then run the simulation and summarize the best parallel strategy. Use when the user wants to evaluate a model on one or more hardware profiles, compare hardware targets, compare aggregation versus disaggregation versus PD ratio optimization, choose TP/EP/MOE-DP search ranges, or obtain recommended concurrency, throughput, TTFT, and TPOT results from throughput modeling.
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。
Interactively gather parameters for `python -m cli.inference.text_generate`, generate a concrete text generation simulation command, explain assumptions, ask for execution confirmation, then run the command and summarize performance metrics. Use when the user wants to validate a specific model, hardware profile, batch/query size, prefill or decode scenario, TP/DP/EP/MOE parallel strategy, profiling database, chrome trace, empirical metrics export, or a best row produced by throughput_optimizer.
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。
Execute a plan file — parse phases, spawn parallel subagents, handle failures.
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 one-command Obsidian workflow orchestration across the existing six domain skills. This skill owns named workflow command IDs and preview/apply execution policy, while delegating all direct official CLI command execution to the appropriate domain 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。
Use when building functions that must survive process crashes, retry automatically on failure, run on a schedule, react to events, or maintain state across infrastructure failures — e.g., webhook handlers that drop events, flaky cron jobs, background jobs that fail mid-execution, or workflows that need to resume where they left off. Covers Inngest function configuration, triggers (events, cron, invoke), step execution and memoization, idempotency, cancellation, error handling, retries, logging, and observability.
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。
Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, checking memory operations, or examining agent performance. Automatically fetches recent traces and analyzes execution patterns. Requires langsmith-fetch CLI installed.
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 you have a written implementation plan to execute in a separate session with review checkpoints
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 Model Context Protocol (MCP) servers - discover, analyze, and execute tools/prompts/resources from configured MCP servers. Use when working with MCP integrations, need to discover available MCP capabilities, filter MCP tools for specific tasks, execute MCP tools programmatically, access MCP prompts/resources, or implement MCP client functionality. Supports intelligent tool selection, multi-server management, and context-efficient capability discovery.
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。
A two-phase repair skill that analyzes errors and suggests fixes before executing repairs. Phase one: user describes error, agent analyzes and proposes solution. Phase two: upon approval, executes the repair action.
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 structured cyber threat intelligence reports at strategic, operational, and tactical levels tailored to specific audiences including executives, security operations teams, and technical analysts. Use when producing finished intelligence products from raw collection data, creating sector threat briefings, or delivering post-incident intelligence assessments. Activates for requests involving CTI report writing, threat briefings, intelligence products, finished intelligence, or executive security reporting.
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。
Executes authorized phishing simulation campaigns to assess an organization's susceptibility to email-based social engineering attacks. The tester designs realistic phishing scenarios, builds credential harvesting infrastructure, sends targeted phishing emails, and tracks open rates, click-through rates, and credential submission rates to measure human security awareness. Activates for requests involving phishing simulation, social engineering assessment, email security testing, or security awareness measurement.
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。
Consolidated Galyarder Framework Executive intelligence bundle.
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。
Executes authorized attack simulations against Active Directory environments to identify misconfigurations, weak credentials, dangerous privilege paths, and exploitable trust relationships that could lead to domain compromise. The tester uses BloodHound for attack path analysis, Mimikatz for credential extraction, and Impacket for protocol-level attacks including Kerberoasting, AS-REP Roasting, and delegation abuse. Activates for requests involving Active Directory pentest, AD attack simulation, domain compromise testing, or Kerberos attack 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。
Use when you have a written implementation plan to execute in a separate session with review checkpoints
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。
Executes comprehensive red team exercises that simulate real-world adversary operations against an organization's people, processes, and technology. The red team operates with stealth as a primary objective, employing the full attack lifecycle from initial reconnaissance through objective completion while testing the organization's detection and response capabilities. This differs from penetration testing by focusing on adversary emulation rather than vulnerability identification. Activates for requests involving red team exercise, adversary simulation, adversary emulation, or full-scope offensive security 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。
Zero-ceremony atomic task execution. Use for tiny fixes or enhancements (minutes, not hours) that don't warrant SDLC ceremony. Flags: --discuss (disambiguate first), --full (delegate to /pre-commit after execution), --issue N (link commit to GitHub 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。
Orchestrates the full law interpretation pipeline: MvT research, machine_readable generation with validation/BDD testing, and reverse validation. Invokes /law-mvt-research, /law-generate, and /law-reverse-validate sequentially. Use this skill proactively when: user wants to make a law executable, add machine_readable sections, or mentions "interpret" in the context of a Dutch law YAML file. Activate automatically when user opens a corpus YAML file without machine_readable sections and wants to make it executable.
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。
Execute an existing implementation plan or settled multi-step request through connected slices with targeted checks and controlled plan drift.
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。
Facilitate monthly S&OP or integrated business planning cycles by aligning demand, supply, inventory, capacity, and executive decisions with a standard meeting rhythm and decision log. Use when operations teams need a demand review, supply review, pre-S&OP reconciliation, or executive S&OP pack. Do not use for single-SKU replenishment math only.
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。