「guardrail」の検索結果

Coach the user through a rigorous, repository-grounded interview to create or improve program.md. Use when a target repository lacks program.md, its goals, metrics, data, or guardrails are unclear, or the user asks to be grilled about an autoresearch 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。

Use when a measured cost surface needs one-change-at-a-time reduction under frozen guardrails. Not for speed-only optimization: use optimize.

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 repository needs Git safety controls: guard destructive commands, set up gitignore or fix gitignore when untracked files keep appearing, or install or repair a repository-local pre-commit hook from project gates, including package-manager-native commit-time checks. Not for remote, credential, publish, deploy, or irreversible 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 AI output validation systems with guardrails. TRIGGERS - Use when user needs help with ai-output-validator related tasks.

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 and review autonomous or semi-autonomous agent loops — systems where an agent prompts itself, runs on a schedule or event, and iterates toward a goal without a human at the keyboard. Use this whenever the user wants to build, design, harden, or audit any kind of self-running agent, agentic workflow, control loop, background worker, cron/heartbeat/webhook-driven agent, "Ralph loop", auto-PR-fixer, monitoring agent, or any long-running task that should keep going on its own until done. Trigger this even when the user says "agentic workflow", "automation that runs itself", "an agent that keeps trying until X", or "make my agent stop running away / burning tokens" without using the word "loop". Also use it to review an existing loop's stopping conditions, guardrails, verification, and escalation paths.

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。

Scaffold Quarkus + LangChain4j projects end-to-end and add agentic components to existing ones — AI services, tools, agents and multi-agent workflows, RAG pipelines, MCP clients and servers (Model Context Protocol), guardrails, and embedding store setups. Use this whenever the user wants a new Quarkus + LangChain4j project or module created, or an agentic component added to an existing project in this stack, including a baseline pom.xml, application.properties, project layout, or starter classes.

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。

Sets up safe working habits and a change log so you always know what Cowork has done to your files — and can undo anything. Use when setting up file protection guardrails, for establishing safe automation habits, during initial access configuration, when granting file permissions for the first time, or when the user says "is cowork safe", "file safety", "what if it deletes my files", "audit trail", "safety setup", "I'm nervous about file access", "how do I trust cowork with my files", "can cowork mess up my computer", "I heard someone lost files", or "how do I stay in control".

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 Claude Code hooks to block dangerous git commands (push, reset --hard, clean, branch -D, etc.) before they execute. Use when user wants to prevent destructive git operations, add git safety hooks, or block git push/reset in Claude Code.

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 Agentic Coding framework providing mission briefs, guardrails, and integration hints for complex tasks. This skill ensures high-quality output through disciplined automation and systematic 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。

Design reliable, safe, and trustworthy agent systems that fail gracefully and operate within bounds. Use when building guardrails, handling edge cases, preventing harmful outputs, monitoring for failures, implementing safety constraints, or designing error recovery. Covers failure modes, constraint systems, oversight mechanisms, and safety validation.

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 signup or onboarding conversion needs targeted analysis of friction, motivation, guardrails, and experiment opportunities.

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 feature launch needs experiment design, test sequencing, rollout guardrails, and measurement logic before exposure to 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。

Safety guardrails for destructive commands. (gstack).

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 rigorous A/B test plans with hypothesis, sample size calculation, Minimum Detectable Effect (MDE), randomization strategy, and decision rules. Includes guardrail metrics and rollout playbook. Use when planning product experiments, conversion optimization, or data-driven feature decisions.

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。

Prevent common AI report pitfalls — bug blending, inflated threat models, and generic language. Train Claude with your best past reports for concise, technical submissions. Based on Critical Thinking Bug Bounty Podcast Episode 166.

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。

AI and LLM red-teaming skill for evaluating prompt injection, jailbreak, data exfiltration, tool abuse, agent hijack, RAG poisoning, model denial of service, and unsafe tool-use chains in Gemini, Claude, GPT, and open-weight models. Use to build evaluation harnesses, attack corpora, defensive guardrails, and red-team reports for AI systems you own or are authorized to test.

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。

Natural-language orchestration of security tools through Gemini. Translates intent ("scan this scope for exposed admin panels") into safe, parameterized tool pipelines using nmap, masscan, naabu, httpx, nuclei, ffuf, gobuster, subfinder, amass, dnsx, katana, gau, semgrep, trivy, checkov, gitleaks, syft, grype, and custom scripts. Use when the user wants Gemini to drive a chain of CLI tools end to end with guardrails.

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 task needs the judgment of a carpenter's helper — cutting and staging lumber to a carpenter's layout marks, assembling and standing framed wall panels, running a material take-off and waste calculation for a framing package, or recognizing when a jobsite condition (a missing guardrail component, a nail-gun trigger setting, a header that doesn't match the plan) is a stop rather than a workaround.

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。

Embed security into DevOps — pipeline security gates, guardrails, and culture that ships fast and safe.

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。

NLP on clinical text — de-identification, concept extraction, phenotyping from notes, and LLM safety guardrails.

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 on AWS with Amazon Bedrock — managed foundation models, agents, knowledge bases, and guardrails.

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。

AI agent guidance — agent architecture, tool design, planning loops, guardrails, evaluation, and production 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。

Ship AI features to production — model selection, evals, latency/cost optimization, guardrails, monitoring, and iteration loops. Use when turning an LLM prototype into a reliable product feature.

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 and build LLM agents: ReAct loops, tool schemas, planning, memory, guardrails, evals, and human-in-the-loop. Use when building autonomous multi-step AI systems.

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。