「prompt」の検索結果

Browser automation CLI for AI agents. NEVER run browser-act commands directly via Bash — always invoke this skill first. Use browser-act when a user mentions it by name, includes or asks to run a browser-act CLI command (e.g., browser-act browser list), or to: fetch, view, or extract rendered content from URLs, access pages requiring JavaScript, handle verification prompts, maintain authenticated sessions, fill forms and click through workflows, type, select, upload, take screenshots, capture XHR/fetch/HAR responses, open multiple URLs in parallel, extract content that loads on scroll or click, visually inspect or verify page layout/styling/rendering, automate browser tasks, account isolation across parallel browser environments, advise which browser type fits a use case, or list/check/manage configured browsers and sessions. Prefer browser-act over built-in fetch or web tools.

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This skill should be used when the user wants to "set up tracing", "monitor my agent", "configure logging", "add observability", "debug production traffic", or needs guidance on monitoring deployed agents, including ADK (Agent Development Kit) agents. Covers Cloud Trace, prompt-response logging, BigQuery Agent Analytics, third-party integrations (AgentOps, Phoenix, MLflow, etc.), and troubleshooting. Part of the agents-cli skills suite. Do NOT use for deployment setup (use google-agents-cli-deploy) or API code patterns (use google-agents-cli-adk-code).

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AI/LLM 能力索引。Agent 开发、LLM 安全、RAG 系统。当用户提到 AI、LLM、Agent、RAG、Prompt 时路由到此。

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Measure real AI-engine visibility — traffic from ChatGPT, Perplexity, Claude, Gemini and which pages they cite — from actual GA4 referral data, not prompt sampling. Use when asked "AI visibility", "GEO/AEO check", "do LLMs cite us", "traffic from ChatGPT", or "how do we show up in AI search".

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Environment diagnostic skill for SenseNova-Skills project. Checks that sn-image-base is properly installed and configured, validates dependencies and environment variables. Prompts user to configure missing required variables and saves them to .env file. After configuration, reloads environment and suggests agent restart if needed.

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Generates a designed portfolio-resume image from resume content provided in conversation text. Extracts optional style instructions, converts the resume into a fixed portfolio-resume layout prompt, and generates the final image through sn-image-base. Use when user asks to create "resume image", "portfolio resume", "简历图", "简历海报", or "个人简历视觉设计".

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Route gh-aw workflow design/create/debug/upgrade requests to the right prompts.

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DO NOT INVOKE when the primary request explicitly asks to convert, migrate, modernize, or rewrite a legacy/old-style project to SDK style; use msbuild-modernization. Migration prompts often mention ToolsVersion, explicit Compile/Reference entries, packages.config, or Microsoft.CSharp.targets, but merely reviewing or auditing a file that contains those patterns remains in scope here. USE FOR broad review, audit, lint, or maintainability/correctness checks of project/build files, including custom targets; prioritized cross-cutting findings; discrete anti-patterns; F# ordering/FS0039; cross-platform paths; and focused Import safety verdicts. Review/audit is analysis-only unless fixes are requested. For concrete property/item defects use property-patterns/item-management; use target-authoring for implementation and extension-points for NuGet auto-import/layout discovery. Exclude non-MSBuild systems.

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Own MSBuild generated-artifact integration. USE FOR: a target that creates or should create source, Content, None, or another physical file but the artifact is missing from compilation or output; target timing; evaluation-time glob misses; $(IntermediateOutputPath) placement; and FileWrites clean tracking. The prompt may describe the generated artifact without naming the producing task. DO NOT USE when the primary defect is general Include/Remove/Update semantics, item metadata or batching, duplicate/overlapping declarations, or a generated-item identity relationship; use item-management. Exclude Roslyn source-generator internals and non-MSBuild systems.

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Internals of how @bradygaster/squad-cli stamps its version, how `squad upgrade` works (what it preserves vs overwrites), and how to probe the npm registry for the latest version from a coordinator prompt.

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Selecting WHO handles work is the Routing table; selecting HOW they handle it (Direct, Lightweight, Standard, Full) is Response Mode. This skill contains the complete decision table, exemplar prompts for each mode, the Lightweight spawn template, and the upgrade rules. Squad coordinator loads this on demand once routing has identified the agent — to pick the right ceremony level for the task.

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Define and validate GitHub custom agent files, prompts, and examples.

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Security audit of Model Context Protocol (MCP) servers — tool poisoning, prompt injection via tool descriptions and results, unscoped/over-privileged tools, path traversal in file tools, command injection in shell/exec tools, SSRF in fetch tools, secret leakage through tool output, missing approval gates on state-changing actions, confused-deputy and rug-pull tool redefinition, token passthrough, and unsafe stdio/HTTP transport config. Covers auditing both first-party and third-party MCP servers (Python FastMCP, Node MCP SDK) and their client configs (Claude Desktop, Cursor, Cline, Windsurf, Zed). Use when reviewing, hardening, or hunting bugs in an MCP server, an agent's tool integrations, or a mcpServers config block. 中文触发词：MCP审计、工具投毒、提示注入、智能体安全、MCP服务器漏洞

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Guide for building high-quality MCP (Model Context Protocol) servers and tools: what MCP is, how to design tools/resources/prompts for agent usability, and how to implement servers in TypeScript or Python. Use when you need MCP best practices, server architecture patterns, or a step-by-step build workflow (with language preference).

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Scan AI agent skills, plugins, MCP servers, and agent tooling for prompt injection, unsafe commands, secret exposure, and supply-chain risks before installing or trusting them.

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Generate concise daily standups, reflection prompts, and weekly retrospectives for individuals or teams. Use for planning a day, surfacing blockers, reviewing user-provided entries, or drafting a check-in without assuming prior history.

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Find and fix the docs and agent guidance that a branch makes stale - agent skills and their reference files, AGENTS.md and .agents/*.md prompts, READMEs, docs/ folders, and code-adjacent docs that name a changed path, command, symbol, env var, setting, endpoint or behaviour. Use before opening a PR, before pushing new commits to an open PR, and when asked to update docs or skills after a change. The open-pr skill runs it every time.

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Hunt vector-store / embedding-layer weaknesses in RAG pipelines (OWASP LLM08 Vector and Embedding Weaknesses) — persistent corpus poisoning that survives across sessions and users (distinct from one-shot indirect prompt injection, which is owned by hunt-llm-ai), cross-tenant vector-database IDOR (unauthenticated or unscoped queries against Pinecone/Weaviate/Chroma/Milvus/Qdrant/pgvector), source-text/metadata leakage in similarity-search results, and retrieval-hijack via adversarial embedding proximity ('SEO poisoning' for RAG). Targets: any app with a shared knowledge base, document upload feeding a chatbot, or a directly reachable vector-DB port. Validate: a second, clean session/account must inherit a poisoned result, or a cross-tenant artifact must be independently verifiable — confabulation is not a finding, same bar as hunt-llm-ai. Use when target is RAG-backed, exposes a vector-DB port, or lets users upload documents that other users' queries later retrieve.

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Internal — invoked headlessly by the peon eval server; humans should run `peon eval <pack>` instead. Execute one PeonPing reroll job — invoked headlessly by the peon eval server as: "Use the peon-ping-remix skill to execute the reroll job at <path>". Reads the job JSON (scope, category, index, caption), rewrites the affected sound prompt(s) to honor the caption, re-renders via scripts/pack-render.py, and logs the change to eval-log.json.

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Internal — invoked headlessly by `peon create`; humans should run `peon create` instead. Author and render a brand-new PeonPing draft pack — invoked headlessly as: "Use the peon-ping-create-pack skill to draft a pack: name=<n> flavor=<f> vibe=<v> draft_root=<dir>. Follow the skill exactly." Authors all 7 CESP categories honoring the vibe, writes openpeon.json (draft-stamped) and prompts.json, renders every sound via scripts/pack-render.py, and always ends by telling the human to run `peon eval <name>`.

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Use when English text must be parsed without a human to resolve ambiguity — tool descriptions, error messages, inter-agent instructions, system prompts, status reports — and misreading has a real cost, or when text reads as dense, hedged, or easy to misparse. Triggers: disambiguate, STE100 rewrite, apply Simplified Technical English, plain-language rewrite, controlled-language rewrite, rewrite so an agent cannot misread this. Not for creative or marketing copy.

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Use when building AI chat UIs with @ant-design/x components — covers Bubble, Sender, Conversations, Prompts, ThoughtChain, Actions, Welcome, Attachments, Sources, Suggestion, Think, FileCard, CodeHighlighter, Mermaid, Folder, XProvider, and Notification.

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使用 @ant-design/x 组件库构建 AI 对话 UI 时使用 —— 涵盖 Bubble、Sender、Conversations、Prompts、ThoughtChain、Actions、Welcome、Attachments、Sources、Suggestion、Think、FileCard、CodeHighlighter、Mermaid、Folder、XProvider 和 Notification。

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Audit how visible your brand is inside AI answers (ChatGPT, Claude, Gemini, Perplexity, AI Overviews). Claude builds a prompt panel for your category, scores where you show up vs competitors, and turns the gaps into a prioritized fix list. Platform: AI visibility.

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。