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概要と使いどころ
Build Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code interpreter, file search, web search), integrating MCP servers, managing conversation threads, or implementing streaming responses. Covers function tools, structured outputs, and multi-tool agents.
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。
Master coordinator for the founder. Filters noise, owns cross-functional processes, enforces consistency, routes decisions, and positions outputs for impact so the founder can think clearly. Doesn't own any function owns the space between all of them.
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。
Instrument and post-process PFC 5.0 asphalt damage studies using bond-break hits, clustered AE events, macro energy density, source fields, stage summaries, and auditable CSV outputs.
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。
Research your competitors and build an interactive battlecard. Outputs an HTML artifact with clickable competitor cards and a comparison matrix. Trigger with "competitive intel", "research competitors", "how do we compare to [competitor]", "battlecard for [competitor]", or "what's new with [competitor]".
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。
Guide users through creating a new plugin from scratch in a cowork session. Use when users want to create a plugin, build a plugin, make a new plugin, develop a plugin, scaffold a plugin, start a plugin from scratch, or design a plugin. This skill requires Cowork mode with access to the outputs directory for delivering the final .plugin file.
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。
"Combine multiple forecasting models into ensemble predictions for improved accuracy. Use this skill when the user needs to improve forecast reliability, combine ARIMA/Prophet/ETS outputs, or build a robust forecasting pipeline — even if they say 'combine forecasts', 'model averaging', or 'which forecast should I trust'.".
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。
Auto-activates when generating Product Requirements Prompt (PRP) documents from synthesized design outputs, providing structured templates and comprehensive implementation guidance.
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。
Auto-activates when synthesizing outputs from multiple parallel sub-agents to create cohesive, integrated design with consistency checking and conflict resolution.
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 an existing codebase in parallel via 7 specialized mapper subagents. Outputs structured findings to docs/codebase/ for persistent cross-session context. Use when onboarding to a brownfield project or when reactive on-demand file reading is too slow.
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。
Break complex work into bounded Codex delegation tasks with clear ownership, expected outputs, and integration 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。
Design safe recurring Codex automations with clear prompts, outputs, schedules, and gating rules.
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。
Full ASVS 5.0 compliance assessment against a codebase and/or architecture diagrams. Reads all 346 controls from the companion CSV, performs targeted code analysis per control, and produces a complete matrix marked COMPLIANT / NON_COMPLIANT / NOT_RELEVANT — with per-control reasoning and evidence (code snippets, file:line references, diagram observations). Outputs a reviewed CSV matrix and a self-contained HTML evidence 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。
Inspects a skill's SKILL.md and its observations/runs.md log, identifies failure patterns, and proposes a targeted amendment to improve the skill. Trigger on: "improve this skill", "fix this skill", "update this skill", "why does X keep failing", "this skill is wrong", "add this to the skill", or automatically when observations/<skill-name>/runs.md contains 3 or more failure entries. Outputs the amendment as a diff the user can review before applying. Records the amendment rationale in observations/<skill-name>/runs.md after user confirmation.
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。
Triages an Aikido security findings CSV against a local codebase. For each finding, reads the flagged file, traces the code path, and verdicts it as KEEP OPEN or CLOSE with a specific reason. Outputs a reviewed CSV and a self-contained HTML evidence report. Run this at the end of a pentest when an Aikido CSV is available.
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 and analyze AI Data Poisoning attacks. By subtly injecting malicious or targeted misinformation into an LLM's training or fine-tuning dataset, an attacker can covertly manipulate the model's future outputs, implant backdoors, or enforce biases without altering the model architecture.
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。
Work with BioReason-Pro predictions and reasoning traces for protein function. Use when: comparing BioReason outputs against curated reviews, converting web exports to YAML, reviewing reasoning trace quality, or running the SFT/RL comparison. Triggers: "bioreason", "GO-GPT predictions", "reasoning trace review", "bioreason comparison", "SFT vs RL".
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 a Flutter feature Product Requirements Document through a structured clarify-plan-draft workflow. Outputs a PRD using the repository template under `.claude/tasks/<feature-name>/prd.md`. Use when the user says "create a PRD", "write requirements", "define the feature", "start a feature spec", or wants to specify a feature before techspec/tasks/start-job.
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。
Official OpenAI SDK patterns for TypeScript/Node.js — client setup, Chat Completions, Responses API, streaming, structured outputs, function calling, embeddings, vision, audio, and production best practices
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。
Official Mistral AI TypeScript SDK patterns — client setup, chat completions, streaming, function calling, structured outputs, embeddings, vision, Codestral FIM, and production best practices
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。
Official Anthropic SDK patterns for TypeScript/Node.js — client setup, Messages API, streaming, tool use, vision, extended thinking, structured outputs, prompt caching, batch API, and production best practices
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。
General workflow for auditing ML CI failures, experiment regressions, training run failures, golden metric failures, and telemetry-backed ML work-product claims from local repositories, logs, metrics, configs, and artifacts. Use when Codex needs to decide whether an ML failure is a model/convergence issue, correctness bug, data/config issue, infrastructure/runtime issue, evaluation/gating policy issue, or unsupported claim, and produce structured evidence-backed outputs.
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。
Classify a proposed code change as Leaf Node or Core Code to decide how much AI usage and review rigor is appropriate. Use this skill whenever the user asks "is this leaf or core", "what kind of change is this", "should I vibe code this", "can AI write this", "how rigorous should the review be", or before starting any non-trivial change. Also useful as a sub-step inside other workflows (planning, PR prep, code review) when classification matters. Asks 6 diagnostic questions about propagation range, change frequency, tech-debt tolerance, examples, failure cost, and review intensity, then outputs a clear classification with the recommended approach. Trigger even when the user describes a change without explicitly asking for classification — the answer changes how they should proceed.
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。
Multi-agent management workflow — task delegation, progress monitoring, quality verification with regression testing, feedback delivery, and cross-review orchestration. Use this skill when coordinating multiple agents on a shared task, monitoring delegated work, ensuring quality across agent outputs, or implementing a multi-phase plan (3+ phases or 10+ file changes).
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 agents on OpenAI's platform patterns — assistants-style threads, function calling, structured outputs, and multi-step tool workflows. Use when working with OpenAI models as the agent engine.
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。