docker
提供生产级 Dockerfile 设计、镜像体积与安全优化与扫描建议
相关性达标的全量 Skills(含未精选长尾)· 共 6067 个
提供生产级 Dockerfile 设计、镜像体积与安全优化与扫描建议
基于 PostgreSQL 与 Prisma/Drizzle 设计高性能数据库模式并优化查询与迁移
提供 PostgreSQL 生产级运维方案:备份、恢复、复制与性能监控。
将代码审查任务委派给指定模型的子代理并汇总其结果。
撰写能转化并优化排名的博客、简报、案例与社媒文案。
使用并行子代理从架构到安全进行全面的代码审查并合并结果。
为 React/Vue/Svelte 生成生产级组件模板,涵盖所有状态与无障碍。
起草专业邮件、反馈、会议记录与跨文化沟通模板。
对 diff 或提交进行正确性、安全性与可维护性的详尽审查。
为 GitHub Actions/GitLab CI/CircleCI 设计高效安全的 CI/CD 流水线。
Manage the ChromaDB vector database that stores the ecosystem's persistent memory. Use when user asks to check memory storage, backup memory, search stored entries, delete entries, or reset the vector database. Do NOT use for general question answering about past sessions (use the memory skill for that).
生成销售外联、提案书、SOW、投融资演示文稿与 RFP 回复模板。
生成品牌识别、设计系统与设计代币,并产出创意方向与设计简报。
按 OWASP/NIST 标准实现生产级认证与授权(OAuth/OIDC、WebAuthn、会话、RBAC)。
根据需求设计 REST/GraphQL 接口,并生成符合 OpenAPI 3.1 的规范与最佳实践。
通过 inference.sh CLI 在云端运行多种 AI 应用与模型(图像、视频、LLM 等)。
为 AI agent 提供浏览器自动化能力:导航、填表、点击、截图与数据提取。
按专业UI/UX规范为网页、界面或组件提供美学设计指导与方案。
通过分阶段任务与核验自动推进复杂项目,仅在关键决策处暂停。
在派工前评估并应用节流与配额规则,避免多代理烧掉 token。
在重启或收工前把会话中未落地的内容写入外部并汇报同步状态。
读取本项目下待接手的交接卡并接管任务,支持 /pickup 指令。
将事项与完整脉络打包成交接卡,移交到目标项目或未来会话。
解析机票/PNR 并将航班事件准确加入用户的 Google 日历,处理跨时区。
Maintain a project's canonical truth across long-running AI collaboration. Discover authoritative project documents, identify the current valid plan, audit conflicts and duplicates, absorb external materials through Preview and confirmation, route decisions and changes to the right files, separate design acceptance from implementation and verification, archive processed sources safely, and resume after context loss. Use when users ask which plan is current, want to take over or organize project docs, update current/decisions/changes/delivery/evidence, reconcile docs with code, archive reviewed materials, or continue without re-deciding settled issues. Do not use for generic Markdown formatting or one-time mass document summarization.
在提交前对未暂存的 git diff 启动多位专责审查代理,汇总问题提示开发者修正。
把中文文本去除 AI 风格并匹配作者个人文风,保留事实与专有名词。
从方向到投稿编排研究全流程,管理跨会话状态与阶段闸门。
对比并确认投稿期刊或会议,抓取官方模板、CFP 与作者指南信息。
进行结构化理论推导,逐步证明或显式标注猜想与反例。
把确认选题转为可证伪的假设、实验矩阵、依赖图与里程碑并指定最小验证。
基于研究者画像与文献缺口发散候选 idea 并收敛到可行选题。
按计划执行实验、记录有凭证的指标并做对抗性验证与早停判断。
对研究工作做多维对抗式评估并给出可执行的 go/revise/stop 判定。
Initialize a project's engineering foundation and knowledge hub, and manage its Architecture Decision Records (ADRs). Use whenever the user types /ctxinit, or asks to bootstrap/scaffold a new project's structure, set up a context/ knowledge folder, engineering rules, or security rules. /ctxinit interviews the user about the project's surfaces (admin panel, portal, landing, mobile app, API), then generates a complete context/ hub — rules, docs, plans, decisions (ADR), technical-debt registry, map — plus thin agent entry-point files (CLAUDE.md, AGENTS.md, .cursor stubs) that REFERENCE context/ instead of copying it. /ctxinit --sync (re)generates context/map.md and the component-reuse registry by scanning the current codebase — also use --sync when the user says "update the map", "sync the context", or "list the components". Once a project has a context/decisions/ log, this skill also RECORDS, QUERIES, and SUPERSEDES its ADRs — trigger on "record this decision", "why did we decide X", "di
Use this skill to rate, audit, red-team, or release-gate a concrete AI agent harness, runtime, orchestrator, runner, repository, deployment, trace set, or configuration. Use for loop correctness, tool dispatch and result correlation, context and memory isolation, permissions and sandboxing, approvals, retry and idempotency, timeouts, cancellation, budgets, termination, observability, cost, recovery, author defense, and same-rubric re-reviews. Trigger for wording such as rate my harness, audit this agent runtime, review my agent loop, red-team this orchestrator, is this agent production ready, harness 上线前挑刺, 给 agent harness 打分, or 这个智能体运行时能上线吗. Require an actual harness artifact or runtime evidence, including one in the current workspace. Do not use for generic agent architecture advice, ordinary code review, reviewing a standalone Skill or prompt, validating only a plugin manifest, debugging one model response, or evaluating a model without its runtime.
Use this skill to rate, audit, red-team, or release-gate a concrete AI agent harness, runtime, orchestrator, runner, repository, deployment, trace set, or configuration. Use for loop correctness, tool dispatch and result correlation, context and memory isolation, permissions and sandboxing, approvals, retry and idempotency, timeouts, cancellation, budgets, termination, observability, cost, recovery, author defense, and same-rubric re-reviews. Trigger for wording such as rate my harness, audit this agent runtime, review my agent loop, red-team this orchestrator, is this agent production ready, harness 上线前挑刺, 给 agent harness 打分, or 这个智能体运行时能上线吗. Require an actual harness artifact or runtime evidence, including one in the current workspace. Do not use for generic agent architecture advice, ordinary code review, reviewing a standalone Skill or prompt, validating only a plugin manifest, debugging one model response, or evaluating a model without its runtime.
Write mermaid diagrams that actually render on the target platform. Covers all 31 diagram types in mermaid 11.16.0 — flowchart, sequence, class, state, ER, gantt, gitGraph, C4, architecture, mindmap, and the long tail — with per-diagram syntax, node shapes, and worked examples. Use when writing or fixing any ```mermaid block, choosing which diagram type fits, debugging a diagram that renders as a broken box on GitHub, or deciding whether a diagram type is supported by the renderer you are targeting.
Extracts user workflows into self-contained agent skills by interviewing for intent and edge cases, designing modular packages with progressive disclosure (lean SKILL.md body, on-demand references/), writing step-by-step instructions with gotchas and validation loops, structuring directory trees (references/, assets/, scripts/), and enforcing the full spec checklist before deployment. Use whenever the user asks to create a new agent skill, extract expertise into a skill package, build a reusable workflow, audit an existing skill for compliance, or convert rough notes into a structured SKILL.md.
Writes and audits A2A-compliant agent-card.json files: the machine-readable discovery metadata that lets other agents find, route to, and invoke an agent's skills. Covers all required and optional JSON fields, skill declaration quality (description, examples, tags), capability flags, security schemes, and alignment between the card and the agent's runtime SKILL.md. Use when creating a new agent, auditing an existing agent-card.json, or adding/modifying skills declared in a card.