「agent response」の検索結果

重みが公開されたLLMの回答拒否に関わる仕組みを分析し、再学習なしで重みを変更する実験を支援。計算環境に合うモデルや手法の選択、変更前後の品質評価まで扱います。

• LLMの回答拒否の仕組みを調べたいとき
• 再学習せず重み変更を実験したいとき
• 計算環境に合うモデルと手法を選びたいとき

言語モデル内部の反応を少数の特徴に分解し、学習した概念を調べます。学習済みSAEの分析、自作SAEの訓練、特徴を使った出力調整と品質評価を案内します。

• 学習済みSAEで特徴を調べたいとき
• 特定のモデル層にSAEを訓練する
• 入力文ごとの特徴の反応を比較する

取得できないWebページを、アーカイブやAPIなど別の経路から探します。本文が本物かを確認し、保存日時と出典を残しながら、調査に使えるコピーを見つけます。

• 403・429で取得できない記事の調査
• 削除されたページの記録を探す
• APIやRSSで代替情報を探したいとき

The core OpenAI-compatible inference endpoints: chat completions, embeddings, images, audio (TTS/STT), moderations, rerank, and the Responses API. The primary integration surface for AI 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。

Runs a hypothesis-driven debugging loop across any language or binary, escalating to orthogonal oracle angles and locking the fix with a failing test. Use for crashes, silent failures, hangs, wrong responses, memory leaks, async misbehavior, or reverse engineering.

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。

How this team handles a production incident issue — what to establish before proposing a fix, and what an agent may and may not do on its own.

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。

Prepares evidence-bounded, constructive peer-review drafts and structured manuscript assessments. Supports authorized review of scientific manuscripts, protocols, preprints, or research proposals; reporting-guideline selection; claim–evidence checks; methods, statistics, reproducibility, ethics, figure/table, and citation critique; or revision-response planning.

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。

Designs experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so results are interpretable. Use whenever someone is planning a study, asks how to assign subjects/samples to groups, mentions randomization, blocking, stratification, controls, factorial or fractional-factorial designs, design of experiments (DOE), screening many factors, response-surface optimization, crossover or repeated-measures or split-plot designs, cluster/group randomization, Latin squares, plate layouts, batch/run-order effects, replication vs. pseudoreplication, or sequential/adaptive/group-sequential designs. Trigger even for informal phrasings like "how should I set up this experiment", "how do I avoid confounding", "what's the best way to test these 6 factors", or "assign these mice to conditions". For computing the sample size or power once the design is chosen, use statistical-power; for analyzing data already collected, use statistical-analysis.

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。

API design principles and decision-making. REST vs GraphQL vs tRPC selection, response formats, versioning, pagination.

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 client SDK code, API wrapper libraries, request/response models, and language-specific usage patterns for any REST 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。

Implement bounded, idempotency-aware API throttling, backoff, and retry handling for 429 and transient 5xx responses.

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。

Set up alerting rules, configure on-call rotations, and manage incident response workflows.

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。

Eliminate sycophantic agreement patterns in AI responses. Load via /skill anti-sycophancy.

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。

Security audit, hardening, threat modeling (STRIDE/PASTA), Red/Blue Team, OWASP checks, code review, incident response, and infrastructure security for any 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。

Python background job patterns including task queues, workers, and event-driven architecture. Use when implementing async task processing, job queues, long-running operations, or decoupling work from request/response cycles.

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。

Python resource management with context managers, cleanup patterns, and streaming. Use when managing connections, file handles, implementing cleanup logic, or building streaming responses with accumulated state.

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 structured incident response runbooks with step-by-step procedures, escalation paths, and recovery actions. Use this skill when building a service outage runbook for a payment processing system; creating database incident procedures covering connection pool exhaustion, replication lag, and disk space alerts; onboarding new on-call engineers who need step-by-step recovery guides written for a 3 AM brain; or standardizing escalation matrices across multiple engineering 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。

Write effective blameless postmortems with root cause analysis, timelines, and action items. Use when conducting incident reviews, writing postmortem documents, or improving incident response processes.

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。

Foundation skill for Power Automate via FlowStudio MCP — auth setup, the reusable MCP helper (Python + Node.js), tool discovery via `list_skills` / `tool_search`, and oversized-response handling. Load this skill first when connecting an agent to Power Automate. For specialized workflows, load `flowstudio-power-automate-build`, `flowstudio-power-automate-debug`, `flowstudio-power-automate-monitoring` (Pro+), or `flowstudio-power-automate-governance` (Pro+) — each contains the workflow narrative, this skill provides the plumbing they all rely on. Requires a FlowStudio MCP subscription or compatible server — see https://mcp.flowstudio.app

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 on one hard mathematics problem in this session yourself, with no sub-agents: reason in stages, record each settled step in a notes file so that nothing written is lost if a response is cut off, and end with a self-contained proof.md. Usage: /math-proof:solo <the problem, stated in full, or the path of a file holding it>.

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 and configure Zeek (formerly Bro) to passively analyze network traffic, generate structured connection/DNS/HTTP/SSL/file logs, detect anomalous behavior, and write custom scripts for organization-specific threats. Use for passive monitoring at network choke points, feeding SIEM/threat hunting with protocol metadata, or retrospective log analysis during incident response; not a substitute for inline IDS/IPS or host 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。

Deploys Llama Guard 3 safety classification, NeMo Guardrails programmable dialogue rails, and LLM Guard input/output scanner pipelines as complementary runtime defenses that inspect and constrain LLM prompts and responses. Use when adding a production runtime safety layer to an LLM, RAG, or agent application to block jailbreaks, prompt injection (OWASP LLM01), toxic content, or sensitive-data leakage before it reaches or leaves the model.

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 the user asks how to build with OpenAI products or APIs and needs up-to-date official documentation with citations (for example: Codex, Responses API, Chat Completions, Apps SDK, Agents SDK, Realtime, model capabilities or limits); prioritize OpenAI docs MCP tools and restrict any fallback browsing to official OpenAI domains.

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。

Opt-in ambient signal capture. After explicit enablement, applies on substantive inbound messages to detect original thinking and entity mentions. Use an authorized sub-agent where supported; otherwise detect inline. Aim to never block the main response.

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。