Skills de Claude Code · página 135

通用项目部署到远程服务器。自动识别项目类型（Node.js/Python/Rust/Go/静态站），SSH 配置、环境安装、项目上传、进程管理、Nginx 反向代理、Cloudflare SSL、安全加固。当用户需要部署项目、上线服务、配置域名时使用

服务器安全审计与加固。扫描 SSH、防火墙、端口暴露、文件权限、暴力破解等安全问题，生成报告并提供一键修复。当用户说服务器安全、安全审计、安全检查、安全加固时使用

Audit, design, categorize, distribute, and measure agent skills using lessons from Anthropic's Lessons from building Claude Code: How we use skills. Use when reviewing an existing skill, deciding whether a workflow deserves a skill, planning a skill library, turning team knowledge into skills, choosing skill categories, writing trigger descriptions, designing progressive disclosure, or planning skill marketplace and usage measurement.

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

生成口播视频背景 PPT 幻灯片（16:9 横版 PNG 序列）。当用户需要做 PPT、生成幻灯片、做演示背景图时使用

当用户需要在长会话的逻辑边界手动压缩上下文、保留关键决策和约束、丢弃中间探索过程时使用。

Logging system design guide covering centralized architecture, field standards, and distributed tracing. Use when designing log systems, establishing standards, or debugging production issues.

Comprehensive logging system design guide. Use when designing log architecture, establishing logging standards, adding observability, or debugging production issues. Covers centralized configuration, field standards, and distributed tracing.

系统性能诊断。当用户说电脑卡、系统慢、查看进程、CPU占用高、内存不够等性能问题时使用

Four-phase debugging framework for any technical issue. Use when encountering bugs, errors, or unexpected behavior. Prevents random fix attempts.

Technical specification and design document expert. Use when writing design docs, RFCs, ADRs, or evaluating technology choices. Covers C4 model, system design, and architecture documentation.

Enforces TDD discipline with RED-GREEN-REFACTOR cycle. Use when writing new features, fixing bugs, or refactoring code. Ensures tests genuinely verify behavior.

Use when the user explicitly asks for $threads, Codex-native subagents, 开几个子 agent, or a GitHub issue/PR queue needing parallel lanes, worktrees, review/merge gates, and closure audit. Do not use for OS/language threads, chat/email/forum threads, or Assistants product threads unless Codex workflow orchestration is explicit.

Use when the user wants detailed travel itineraries with activities, logistics, day-by-day schedules, vacation planning, trip organization, or travel documentation.

Modern TypeScript project architecture guide for 2025. Use when creating new TS projects, setting up configurations, or designing project structure. Covers tech stack selection, layered architecture, and best practices.

Use when coordinating complex tasks across multiple AI agents with a centralized handoff document for planning, execution tracking, and feedback fusion.

Guard long, ambiguous, or stateful AI-agent work from drift. Use when the user asks to run or continue a multi-step task, autonomous loop, bug fix, repo change, PR readiness check, compaction handoff, resume from previous context, cost-control checkpoint, or any task likely to span many tool calls, files, sessions, agents, or verification gates.

Audit and recommend improvements for a repository's agent-readable context, including AGENTS.md, CLAUDE.md, WARP.md, CONTRIBUTING.md, .agents/skills, and specs/ PRODUCT.md and TECH.md contracts. Use when asked to review, score, assess, or standardize repo instructions, agent onboarding, spec workflows, or cross-repo agent-context conventions. Use agentsmd-scaffold instead when the user wants to generate or apply root/scoped AGENTS.md files.

Diagnose excessive Codex local SQLite diagnostic log writes with read-only evidence by default. Use when a user mentions logs_2.sqlite, logs_2.sqlite-wal, block_log_inserts, SSD/TBW wear, or explicitly asks to protect, clean up, verify, or restore Codex diagnostic logging.

Generate or update repository-specific AGENTS.md instruction files from real repo evidence. Use when asked to create, design, scaffold, split, or improve root or scoped AGENTS.md files for Codex/Claude/agent workflows, especially when a repo needs directory-specific rules, validation commands, generated-file boundaries, or a short agent onboarding router.

想法群聊室 — 类比者角色。被 idea-team 主编排器调用，或用户单独说"类比一下"、"别的行业有没有"、"yes-and 扩展"、"X 让你想到什么"、"跨界启示"时触发。**专门做跨界类比 + yes-and 扩展——不评判、不挑刺、不要求事实证据**。Do NOT use when 用户要数据（用 `idea-research`）、要反方意见（用 `idea-devils-advocate`）、或要严谨论证（类比是启发，不是论证）。

想法群聊室 — 反方角色。被 idea-team 主编排器调用，或用户单独说"反方意见"、"挑这个想法的刺"、"为什么会失败"、"找漏洞 / 反例"、"devil's advocate"时触发。**专门挑漏洞、找隐藏假设、给反例——不安慰、不"也许可以这样"、不全盘否定**。Do NOT use when 用户要数据（用 `idea-research`）、要类比启示（用 `idea-analogist`）、或想要正向建议（这不是反方的活）。

想法群聊室 — 调研员角色。被 idea-team 主编排器调用，或用户单独说"调研一下 X"、"X 的现状/竞品/数据"、"找 2026 数据"、"事实底"时触发。**用 WebSearch 拉真实 2026 数据、列竞品、引来源——只给事实，不评判，不建议**。Do NOT use when 用户要评价想法好坏（那是反方）、或要跨界启示（那是类比者）、或已有 PRD 在跑（不需要这种调研）。插件内 skill 会由 `idea-coach` namespace 路由；catalog 用户直接按 skill 名调用。

想法群聊室主持人 — 把一句话想法丢给多角色 AI 团队（调研员/反方/类比者）做查漏补缺。每个角色有自己的 voice，他们互相 @ 接话；你随时插话。**这是创意扩展工具，不打分、不否决、不堵路**。Use when 用户说"组个团队聊一下"、"开会讨论这个想法"、"找几个角度看看"、"群聊一下 X"、"team review X"，或调用插件命令 `/idea-coach:idea-team`。Do NOT use when 用户已决定做这个想法只要 PRD（用 `idea-to-product`）、或用户要单独某个角色 skill、或用户只想自由 brainstorm（不需要 team 结构）。

端到端产品教练 — 把一句话想法走到 PRD + 可点击 HTML 原型。会顶嘴、强制砍功能、用 Nielsen + Norman 做友好性硬检。Use when user 说"我有一个想法"、"想做一个产品"、"做 MVP"、"写 PRD"、"做用户友好的产品"，或调用插件命令 `/idea-coach:idea`。Do NOT use when 用户已有完整 PRD 在跑、明确说"只 brainstorm 不决策"、只想要 UI 设计（用 design-shotgun）、或只想起项目骨架（用 /init）。

Use when creating or maintaining UI design systems, design tokens, component documentation, responsive calculations, quality checks, or design-dev handoff materials.

Audit AND optimize a CLAUDE.md / AGENTS.md instruction file — score it against the five high-leverage patterns, flag anti-patterns, then apply approved fixes in place. Use when the user says 优化 CLAUDE.md / 优化 AGENTS.md / optimize my agent doc / 帮我改 claudemd, or after an audit when they want the fixes applied (not just reported).

End-to-end app feature inventory and user-story testing workflow with a canonical tracker. Use when the user asks to audit every feature, derive expected behavior from code, test user journeys, or explicitly fix and retest documented UX or logistical defects.

Evidence-driven architecture research for understanding real systems and making technical decisions. Use when doing architecture landscape studies, source-backed system archaeology, build-vs-buy or adopt/adapt/build decisions, open-source and commercial comparisons, revisiting an earlier architecture choice, or handling requests such as 架构调研, 架构选型, 竞品架构, 技术尽调, 同类方案, 开源替代, how is X built, and what should we learn from X. Do not use for small mechanical changes, market-only discovery, or detailed design after the technology direction is already fixed.

Plan, produce, or diagnose evidence-backed product demo videos and screen-recorded promotional walkthroughs. Use when the user asks to make a product demo, launch video, feature showcase, app walkthrough, demo reel, or polished recording; wants every important capability shown without a slow feature tour; asks to remove dead time or fix narration-to-action pacing; or needs a repeatable script, capture plan, and verified final media. Do not use for fictional commercials with no real product proof, general-purpose video editing, or documentation-only tutorials that do not need a promotional narrative.

能力蒸馏工作流——从用户批准的强模型访谈或真实任务轨迹中提取非显然的判断规则，形成可审计的 judgment packet，再交给 skill-audit 和 skill-creator 决定是否落成可加载 skill。当用户说“蒸馏这个模型的判断力”“把这次任务的关键决策固化下来”“模型窗口要关了，保留它在某类场景的判断”时使用。普通流程文档、直接写 SKILL.md、泛化最佳实践或未授权的会话日志扫描不使用本 skill。

对用户拥有或明确获授权的本机可执行文件、应用二进制和版本产物做只读静态逆向，包括 Claude Code 及其他 CLI、Mach-O、ELF、PE、Wasm 或打包应用。仅在用户明确要求“逆向、扒实现、查二进制字符串/符号/依赖、验证内部行为、比较两个版本”时使用；不要因普通提及软件而触发，也不要用于执行未知样本、绕过授权/计费、破解或再分发。

Design, audit, and verify configuration, environment separation, secrets, BYOK flows, key rotation, config schema validation, and drift checks across local, dev, staging, and production. Use when adding env vars, changing runtime config, handling API keys or user-provided keys, diagnosing config drift, or preparing deploy/release configuration.

Design and verify data contracts, schema changes, migrations, rollbacks, backfills, compatibility windows, tenant isolation, and API-to-database field mapping. Use when changing database schema, event payloads, persisted documents, analytics tables, multi-tenant data boundaries, or any user-visible data contract where silent fallback or undeclared fields would be dangerous.

Route full software-development architecture work from product intent through design, implementation, testing, release, and operations. Use when the user asks for a complete development architecture, wants to know which Spellbook skills to combine, needs an execution path across PRD/spec/API/data/security/performance/release/SRE, or asks to turn an idea or repo into a production-ready engineering plan.

Create or audit SLOs, SLIs, alert rules, incident response steps, escalation paths, postmortems, operational runbooks, and customer-impact communication. Use when defining production reliability, preparing launch readiness, responding to an outage, writing a runbook, tuning alerts, or closing the loop after an incident.

检测一个 IP 或住宅/机房代理节点的质量——注册库、地理库一致性、ASN/org、风控信誉、黑名单、住宅真实性、BGP 宣告、目标服务(Grok/Claude/ChatGPT)解锁与延迟三角测量。判定该 IP 能否安全用于 AI 服务(避免被地理库误判到别国导致区域锁)。当用户说"查这个 IP"、"这个节点在哪"、"这个代理能用吗"、"IP 质量检测"、"验收住宅 IP"、"为什么被判定在 X 国"、"check ip"、给出 socks5 代理凭证问归属或解锁时使用。

Detect known malicious npm package versions and install-time supply-chain indicators in repositories, lockfiles, and node_modules. Use when a user mentions an npm compromise, Shai-Hulud, the Keyv/cacheable incident, suspicious preinstall scripts, credential-stealing packages, or asks whether a JavaScript project was exposed to a package supply-chain attack. Do not use as a general CVE or license audit.

Plan, diagnose, and verify performance budgets, latency targets, load tests, capacity estimates, bottleneck analysis, caching strategy, query efficiency, queue throughput, and regression gates. Use when a feature may be slow, a system must scale, a performance regression is suspected, or release readiness depends on throughput, cost, memory, CPU, or response time.

精细化 AI 短剧 IP 创作技能（v0.6.0）。三阶段架构：Phase 1 创作（剧本+ref图，反复迭代）→ Phase 1.5 分镜图（每 grid 1-4 张候选静态图 = 视频首帧，工业级核心层）→ Phase 2 出片（按集解锁，4 模自动选）。v0.6.0 关键升级：① ref 库工艺偏置铁律（现代摩天楼易拟物，古建筑必出 chibi 人体，前期 IP 设计阶段就要避坑）② 即梦 5.0 失败模式 + 敏感词清单（3 类 fail 区分 / prompt 1500 字硬上限 / 反派词替换表 / 暧昧词清单）③ 分镜图 8 段 prompt 模板（CHARACTER/BACKGROUND/ACTION/SCENE/CAMERA/LIGHT/TEXT/STYLE）+ NOT humans 子句必加。沿用 v0.3.0 升级：Phase 1.5 分镜图层、4 模视频、ref 5-8 最优。v0.2.0：bash → Python subprocess、36 grid × 4-10s 变奏、红果必爆 7 招、工业级 ref 库 80-150 张、单 prompt 300-500 字。务必触发：用户提到短剧、微短剧、竖屏剧、AI 短剧、AI 漫剧、剧本创作、分镜、即梦/Seedance 出片、红果/番茄/抖音 IP 改编、爽剧、重生、穿越、赘婿、追妻、神医相师、AI 漫剧奇观、或"帮我做一部短剧"类请求。

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Interview the user once and write a docs/gtm-cofounder/founder-brief.md that every other skill reads first, so the advice is about their real business, not a textbook. Use this before anything else, or whenever the agent lacks context on the user's product, ICP, market, or stage, or is giving generic GTM advice.

After the founder brief, turn it into an honest diagnosis and a prioritized, stage-aware GTM roadmap saved as docs/gtm-cofounder/gtm-roadmap.md. This is the hub the user returns to every session to see where they are and the single next move. Use right after start-here, whenever the user doesn't know what to work on next, wants a plan instead of a one-off task, or is drowning in disconnected tactics.

Define a real ICP and the developer personas in the sale. Use when the user says the product is \"for developers,\" can't name who would say no, or is marketing to whoever holds the budget instead of who actually adopts.

Run developer customer discovery via a Technical Advisory Board (TAB). Use when the user has never interviewed a user who isn't a friend, is inventing messaging from a conference room, or is guessing at the roadmap instead of hearing the pain firsthand.

Build a positioning and narrative where the developer is the hero and a real trend is the villain. Use when the messaging describes the product instead of the problem, sounds like every competitor, or has no urgency because nothing is at stake.

Position an AI product when everyone claims AI and skeptics call it \"just a wrapper.\" Find the real wedge (data, workflow, trust, domain), make reliability the differentiator, answer \"won't the big labs just build this,\" and stop leading with \"AI-powered.\" Use when your AI or dev tool blends into a sea of similar demos, buyers doubt the accuracy, or you can't say why you win when the model is a commodity.

Write a developer value proposition that is specific, provable, and free of puffery. Use when the messaging leans on \"powerful,\" \"better,\" \"seamless,\" or \"best-in-class,\" when claims have no proof, or when the same line is supposed to reach both the developer and the buyer.

Structure a dev-tool homepage that converts developers into champions. Use when the landing page is written for the buyer instead of the developer, reads as salesy, buries what the product does, or makes it hard to start. Pairs with the ShipReady homepage audit.

Turn a curious developer into an activated one: the docs, the quickstart, and the first-run experience that gets them to their first real win fast, and back again. Use when people sign up or star the repo but never get it working, come once and never return, or you're about to pour traffic into a first-run that leaks.

Get the first 50 real users through channels the user's ICP already uses. Use when the product shipped and nobody came, the user is \"posting more\" with no result, or is reaching for paid ads before product-market fit.

Plan a developer launch (Show HN, Reddit, Product Hunt) that earns goodwill instead of a flaming. Use when the user is sitting on a launch out of fear, wants to \"go viral,\" or is about to post a press-release-style announcement to a developer community.

Turn developer love into revenue by enabling champions and choosing a GTM model. Use when developers adopt the free tier but nobody pays, when the user is cold-selling the VP instead of arming the developer, or when picking between open-source, PLG, inbound, and sales-led.

Help the user decide what to charge and how to package it: the value metric, the tiers, the free-to-paid line, and finding the actual number. Use when the user is guessing at a price, priced too cheap and can't change it, is stuck on free vs paid, or believes \"developers won't pay\" so never charges.

Coach the user through actually selling: finding the champion and the buyer, running the first sales conversations, demoing their use case, handling \"let me think about it,\" and closing the first paying customers yourself. Use when developers love it but nobody pays, you've never sold anything and freeze on the conversation, or deals keep stalling on \"we'll think about it.\"

Build authority by teaching the problem space, not announcing features. Use when the user finds \"marketing\" distasteful and does none, publishes only product updates, or wants a sustainable content and GitHub-README strategy that developers actually respect.

Measure GTM with the metrics that matter (net developer retention, DREAM funnel) instead of vanity numbers. Use when the user has dashboards full of stars and pageviews but can't tell if go-to-market is working, or is optimizing acquisition over a leaky bucket.

Refresh the user's competitive and market context. Re-scan named rivals and the category for what changed since the brief was written (rebrands, repositioning, new entrants, pricing shifts, renamed categories) and update docs/gtm-cofounder/founder-brief.md and the roadmap with anything that moves positioning. Use on a cadence, or whenever a competitor rebrands, a new tool appears, or the user senses the market has shifted under them.

Before showing the user any substantive GTM deliverable (positioning, value prop, homepage, launch post, pricing, sales script, the brief or roadmap), stress-test it against the standard as an independent critic, because the agent that wrote it is the worst judge of whether it is good. Use as a gate right before presenting work, or when the user asks whether something is actually strong.

Turn hand-made first-50 traction into self-reinforcing acquisition loops, so the next 5,000 users come from usage, not founder hours. Use when growth stalls the moment the user stops pushing, every signup traces back to a DM or one launch spike, or they're reaching for \"more channels\" when the real gap is that using the product creates no new users.

Browser automation for the user's Chrome browser. Use for browser tasks that require the user's cookies, logged-in sessions, existing tabs, extensions, or remote authenticated sites.

Platform-neutral guidance for using Open Browser Use, the open-source Chrome automation stack for AI agents. Use when an agent needs to install, verify, troubleshoot, or operate Open Browser Use through its browser extension, native CLI, JavaScript SDK, Python SDK, Go SDK, or Browser Use style JSON-RPC methods; use for tasks involving real Chrome tabs, user tab claiming, CDP commands, downloads, file choosers, clipboard helpers, or session cleanup.

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Threads growth operating system for topic selection, drafting, analysis, prediction, review, and tracker refresh based on the user's own post history.

Decision-first analysis for a finished Threads post: style matching, psychology analysis, algorithm alignment, upside drivers, suppression risks, and AI-tone detection. Use after the user writes a post, or when they ask to analyze, check, inspect, or AK-review a draft.

Select a topic and generate a draft based on the user's Brand Voice. Draft quality depends on Brand Voice completeness. Trigger words: 'draft', 'write', '起草', '寫文'.

Self-contained compound loop: read threads_skill_learnings.log, cluster the misses, propose concrete sub-skill rule edits, and apply them with the user's approval. The fourth step after Plan / Work / Review. Trigger words: 'optimize', 'compound', '優化skill', '自我優化', '閉環'.

Launch or prepare the optional local visual panel for AK-Threads-Booster. Use when the user asks for a dashboard, visual panel, local UI, data cockpit, or quick way to view tracker/compiled data.

Estimate likely 24-hour post performance from the user's historical data. Use after the user writes a post and wants a range estimate, upside view, or expectation check.

Refresh threads_daily_tracker.json. Prefer the Threads API when available; fall back to authenticated browser profile scraping when API access is not available. Trigger words: 'refresh', 'update tracker', 'scrape profile', '更新貼文', '抓最新數據'.

Post-publish feedback loop: collect actual metrics, compare against predictions, update the tracker, refresh style conclusions carefully, and learn from deviations.

Initialize AK-Threads-Booster: import historical posts, normalize them into the tracker schema, auto-generate a personalized style guide, and build a concept library. Run on first use or whenever the user wants to backfill account history.

Mine insights from comments and historical data to recommend the next worthwhile topics. Trigger words: 'topics', 'topic', '選題', '寫什麼'.

Check AK-Threads-Booster for upstream GitHub updates, safely fast-forward the local skill repo, or install an opt-in weekly Codex automation that keeps the skill on the latest version. Trigger words: update skill, check updates, auto update, weekly update, 更新 skill, 自動更新, 每週檢查更新.

Deep analysis of user's historical posts and comment replies to build a comprehensive Brand Voice profile. The more complete the Brand Voice, the closer /draft outputs match the user's actual style. Trigger words: 'brand voice', 'voice', '品牌聲音', '語感分析'

Your personal DSA & LeetCode mentor. Use for problem explanations, progressive hints, code reviews, mock interviews, pattern recognition, complexity analysis, and custom problem generation. Automatically adapts to your learning style and request type.

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