Claude Code Skills · page 83

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Turn any source that describes how a kind of task gets done (a SKILL.md, a chat log, a runbook, plain prose) into a runnable Morph trace. Lift it into a DAG, write a contract per step, place inputs, and drive the full run lifecycle to verify it. Use whenever someone wants to make a trace from a source.

后端代码编写、逻辑强、安全性高、可读性好、版本控制、代码审查。当任务需要写实验代码、模型代码、数据处理代码、可视化代码、后端接口或系统逻辑时使用。要求逻辑清晰、安全、可读、可维护、便于复现/扩展/部署。支持 Git 版本管理、代码审查、注释规范、README、依赖管理、环境配置、运行说明与项目结构整理。

论文引用规划、审查与多格式生成。当用户需要处理参考文献、引用、bibtex 时使用。审查引用的关联度、真实性、权威性、时效性、数量、中外占比，是否引用了经典/最新/代表性/对比工作。避免虚假引用、过度引用、无关引用、堆砌、遗漏关键文献、低质量来源、引用与正文不匹配。生成 BibTeX/EndNote/GB-T 7714/APA/IEEE 等格式并按目标 venue 调整。

竞赛与项目申报材料辅助。当用户做统计建模、数学建模、互联网+、挑战杯、大创、创新创业、科研训练等项目时使用。辅助写申报书、项目计划书、商业计划书、路演 PPT、答辩稿、项目摘要、技术路线、创新点、可行性分析、市场分析、研究基础、预期成果、经费预算、团队分工。用于非论文投稿场景，可与论文/软著/专利/PPT 联动。

统一风格与一致性维护。在论文、PPT、图表、代码、项目文档之间保持术语一致、视觉风格一致、逻辑线索一致、创新点表述一致（常驻，所有任务后台生效）。避免同一项目在不同材料中出现说法不一致、指标名称不统一、图表风格混乱、创新点前后矛盾、方法名称变化、数据集名称不统一、论文与 PPT 逻辑不一致、软著与系统功能不一致。

数据处理、数据质量分析与数据集构建。当用户需要清洗数据、处理缺失/异常值、特征工程、数据增强、划分数据集、评估数据质量，或需自建数据集（采集、标注规范、格式、说明文档、隐私合规、发布）时使用。在提 idea 前优先判断现有数据是否足以支撑研究。

从顶会大牛角度进行专业绘图与组图。当用户需要把规划好的图实际画出来时使用。按情况用 Python(matplotlib/seaborn/plotly/altair)、R(ggplot2)、MATLAB、Visio、Origin、LaTeX/TikZ、Illustrator、PowerPoint 等。审美统一、专业清晰、配色合理、字体规范、线条清楚、高分辨率，适合直接投稿。不仅画图，还从论文表达角度判断怎么排、怎么组、怎么标注、怎么突出重点。

根据论文内容规划应该做哪些图、哪些表、插在哪里、各起什么作用。当用户需要论文图表规划时使用。图表不限于统计图，也包括数据集真实效果图、模型输出示例、案例展示、可解释性可视化等。规划框架图、技术路线图、数据集示意图、模型结构图、算法流程图、结果对比/消融/敏感性图、真实效果图、统计表/对比表等，以审稿人标准判断哪些必做、哪些冗余。

强大地读文件并学习——Word、PDF、PPTX、Excel、CSV、图片、视频、代码、压缩包等。当用户提供任何文件、问"这个文件讲了什么"、或任务需要理解已有材料时使用（常驻，自动触发）。不只提取文字，而是理解结构、逻辑、图表、数据、实验结果、格式要求、章节关系、视觉风格、隐含要求与可复用内容，并转化为可执行任务。

独特吸睛、审美好、有特色、美观全面的前端设计。当任务涉及前端界面、项目展示页、系统演示、大屏可视化、可视化平台、微信小程序 UI、移动端界面、设计系统、Tailwind v4、shadcn/ui、Next.js、React、Vite、可访问性、动效、重设计审计时使用。不只是能用，而是好看、统一、清晰、有亮点、有视觉记忆点，适合展示/答辩/演示/落地。按主题选风格：科技感、学术感、农业智慧化、数据可视化、极简、玻璃拟态、卡片式、大屏、管理系统、移动端、小程序等。

以顶刊/顶会审稿人标准严格判断 idea 是否真有突破，还是常规组合、套壳、概念堆叠、缺乏理论深度或实验支撑。当用户问"这个 idea 行不行/够不够创新/帮我挑刺"，或 m03 产出 idea 后必须使用。先盲后明立标准、八维度加权打分、五视角对抗、反谄媚硬协议，给判决 + Revision Roadmap，引导回 m03。

根据项目实际情况提出有潜力、有差异化、有亮点的研究 idea。当用户需要创新点、研究思路、提方案，或问"这个方向/数据能做什么"时使用。结合已有基础、数据条件、技术能力、时间周期、发表/竞赛/课题目标，说明为什么值得做、创新点、相对现有方法的优势、能解决的具体问题、适合的成果形式与投稿层次。

辅助软件著作权与专利申请。当用户需要做软著或专利材料时使用。软著：软件名称、功能说明、操作说明书、源代码整理、文档撰写、材料清单、流程、界面截图、功能模块说明、版本与完成日期。专利：判断是否可申发明/实用新型/外观，梳理技术问题、技术方案、有益效果、创新点、实施例、权利要求书与说明书草案、附图说明。最终文本须由专业代理人审核。

强大地搜索并规范化整理科研资料。当用户需要调研某研究方向、找文献综述、了解某领域有哪些工作、搜集中英文论文/专利/标准/政策/数据集/开源项目/竞赛方案/行业报告/技术博客/官方文档时使用。不只罗列结果，而是筛选去重分类、判断可信度与重要性，整理为文献表、研究脉络、代表方法、优缺点对比与可复用资源。

上下文管理、记忆持久化与科研项目管理。当任务涉及长期项目、需要记住项目背景/进展/版本/偏好，或需要把项目拆成阶段任务时使用（常驻）。持续记住研究方向、已定 idea、数据、实验进度、论文/PPT/图表/代码版本、投稿记录、用户偏好、目标期刊。把项目拆成阶段任务并建立任务清单、时间线、里程碑、风险清单与版本记录。

编排器。当用户给出跨多个阶段的大任务（如"从这个数据集做到一篇论文""帮我把这个项目从调研做到投稿"）或要求"继续/刚断了/接手/恢复上下文"时，规划或恢复阶段化 pipeline，按 CONVENTIONS 阶段主线逐阶段调用相应技能，并在阶段间设置强制检查点（决策点 + 确认点），维护产物台账与断点交接。小任务（改摘要、画一张图、查引用）不走 pipeline，直接路由到单技能。

撰写论文初稿与单章节起草、重写、自检。当用户要写论文、写某个章节（标题/摘要/引言/相关工作/方法/实验/结果/讨论/结论/局限/未来工作）、重写某章节、或对草稿做失败模式自检时使用。五种模式 full/outline-only/abstract-only/section-redraft/self-review；以顶刊/顶会审稿人标准打磨，逻辑严谨、创新点突出、结论不夸大、claim 有引用支撑。

对论文分模块润色——语言润色、逻辑重构、结构优化、创新点强化、论证补强、摘要精炼、引言增强、实验分析深化、结论提升。当用户需要改论文、觉得某段不通顺/逻辑弱/创新点不突出时使用。从审稿人角度找逻辑薄弱、论证不足、表达不专业、易被质疑、缺引用、需重组之处，给出具体修改方案与修改后文本。

规范整洁的项目文件夹整理。当任务涉及新建项目、整理已有项目结构、规整文件命名与版本时使用。规划 data、src、models、results、figures、docs、paper、ppt、patent、software-copyright、experiments、logs、configs、references、assets、notebooks 等目录，保证结构清晰、命名规范、版本可追踪，便于写论文、答辩、申请软著专利与复现实验。

科研伦理、学术规范与风险审查。在所有科研任务中默认检查学术不端、数据造假、图片重复使用、引用不规范、隐私泄露、版权、结论夸大、过度包装、论文代写、专利权属、软著材料不真实等风险（常驻，所有任务后台生效）。对论文、项目、软著、专利与比赛材料做合规审查，确保内容真实、规范、可解释、可追溯。

对已确认可行的 idea 制定极其详细的研究方案、实验设计与执行规划。当用户的 idea 已通过 m04 审查、需要把 idea 拆成可执行可复现的完整科研流程时使用。覆盖研究目标、技术路线、数据流程、实验/消融/敏感性/鲁棒性/显著性检验、时间安排、风险与备选，并保证全程可复现。

对执行出来的结果、实验数据、模型输出、图表结果进行详细专业深入的分析。当用户实验跑完、需要解读数据、问"这些结果说明什么"时使用。不只描述好坏，而是解释为什么、哪些结果证明方法有效、哪些暴露问题、哪些异常需排查、哪些规律可成为论文亮点、哪些需补充实验。

审稿意见模拟与返修回复。当用户需要在投稿前预审论文、或收到真实审稿意见后做返修时使用。模拟顶刊/顶会审稿人严格审稿，给出贡献评价、优点、缺点、必改问题、建议补充实验、拒稿风险、可能追问。收到真实意见后分析审稿人关注点、制定返修策略、逐条撰写 response letter、修改论文对应部分。

自动反思与自我审查。每次完成任务后自动检查是否存在逻辑漏洞、事实错误、格式问题、表达不清、创新不足、引用不准、结果夸大、审美不统一、重复内容、结构混乱、不可执行等问题（常驻，所有任务收尾时生效）。不一次性给出粗糙结果，而是先自我审查与迭代后再输出。

制作精美 PPT。当用户需要为论文、项目、竞赛、答辩、汇报、路演做幻灯片时使用。设计封面/目录/过渡/内容/图表/流程/时间线/对比/团队/结论/致谢等页。逻辑清晰、排版高级、审美统一、重点突出，按主题选风格（学术/科技/农业/医学/商务/极简/浅色/深色/数据可视化/竞赛路演）。不仅生成内容，还规划整套叙事逻辑、视觉风格、页面层次与演讲节奏。

后端系统设计与数据库能力。当任务涉及系统架构、数据库设计、接口设计、权限/日志/异常/性能/部署时使用。设计 ER 图、数据表结构、接口文档、用户权限、数据流转、模块划分、API 规范、数据库索引、安全策略、部署方案，尤其适合科研系统、管理系统、数据分析平台、可视化平台、竞赛作品与软著项目。

工具选择与多工具协同。根据任务自动判断适合用什么工具——搜索、Python、R、MATLAB、LaTeX、Word、Excel、PowerPoint、Visio、Origin、数据库、Git、前端/后端框架、绘图工具、文献管理工具等（常驻，所有任务后台生效）。不盲目用工具，而是按实际任务选最高效、最稳定、最专业的实现方式。

根据目标期刊/会议/学校/比赛要求做 LaTeX 或 Word 排版并编译导出最终 PDF/Word。当用户需要套模板、排版、解决编译错误、导出论文时使用。处理标题层级、页边距、目录、摘要、关键词、公式、图表、算法伪代码、参考文献、附录、页眉页脚、页码、单/双栏、编号、交叉引用，并检查 LaTeX 编译错误、Word 格式问题、引用错误、图片位置、字体一致性、编号混乱。

投稿定位与期刊/会议匹配。当用户问投哪个期刊/会议、要评估录用可能性/录用概率、做投稿规划时使用。根据论文质量、方向、创新程度、实验完整性、语言水平、作者背景和预算，推荐中文/英文期刊、SCI/SSCI/EI/CCF/核心、国际/国内会议。分析投稿难度、方向匹配度、审稿周期、版面费、以可核查信号做录用可能性定性分级(高/中/低,不编百分比)、是否适合本科生/弱导师资源、预警风险，给出冲刺/稳妥/保底分层选择。

Render rich interactive visuals — SVG diagrams, HTML widgets, Chart.js charts, interactive explainers — directly inline in chat using the render_visualization tool. Use whenever the user asks to visualize, diagram, chart, draw, map out, or illustrate something, or when a topic has spatial, sequential, or systemic relationships a diagram would clarify better than prose. Also use proactively for data comparisons, metrics, architecture, processes, or mechanisms that benefit from a visual.

Audit a UI or design against WCAG 2.2 AA/AAA and ARIA patterns, returning criterion-referenced findings with severity and specific fixes. Use when the user wants an accessibility check, contrast verification, keyboard/screen-reader review, or wants to confirm a component meets POUR.

Apply a visual direction — an archetype (high-end agency, editorial minimal, brutalist, soft-SaaS, dark-tech) or one of 138 named design systems (apple, linear-app, stripe, vercel, notion, material, shadcn, spotify, tesla…) — by resolving it into the token system. Use when the user wants a specific look/vibe/brand feel, or asks to make a design feel premium/expensive/non-generic.

Generate a complete, accessible brand design system from a brief — primitive → semantic → component DTCG tokens (color, type, spacing, radius, shadow, motion), light + dark, plus a single theme.css — verified for WCAG. Use when the user wants a from-scratch brand/design foundation, a new palette + type system, or a themeable token kit for a product.

Generate production-ready, accessible, token-driven component code for ANY framework — React+Tailwind, Next.js, SwiftUI, Vue, Svelte, Angular, Solid, Web Components/Lit, React Native, Flutter, Jetpack Compose, vanilla CSS, or CSS-in-JS. Use when the user wants working UI code for a component or screen in a specific stack.

Design a UI component spec to the house quality bar — anatomy, variants, sizes, the 8 states, token mapping, and accessibility. Use when the user wants to design or document a component (button, input, tabs, toast, combobox, date picker, modal, etc.) at the spec level before or alongside code. For generating framework code, use design-code.

Set up or run design QA gates — token + hardcoded-value lint, automated a11y (axe), contrast, visual regression across variants/states/themes/RTL, and the manual a11y checklist. Use when the user wants CI quality gates, to prevent design regressions, or to QA a component/screen before shipping.

Review or audit a design/UI across 6 weighted dimensions with Nielsen's 10 heuristics and a prioritized findings table. Use when the user wants a design critique, quality score, heuristic evaluation, or audit of an existing screen, page, or product before/after build.

Generate, extend, or audit design tokens in DTCG format with the 3-tier architecture (primitive → semantic → component). Use when the user wants a color palette, type scale, spacing/shadow/radius/motion tokens, multi-brand theming, or wants to validate token files. Covers colors, typography, spacing, shadows, borders, breakpoints, motion, gradients, opacity, blur, sizing, states, theming.

Keep Figma and code in sync — map the 3-tier DTCG tokens to Figma Variables (collections + modes), sync in either direction, use the Figma MCP when connected, and verify component parity (variants/states). Use when the user wants to push tokens/components to Figma, pull a design into code, set up token↔Variable sync, or check design-code drift.

Govern how the design system evolves — SemVer for tokens/components, the contribution workflow, deprecation policy, and change communication. Use when the user wants to add/promote/deprecate a component or token, decide a version bump, set up a contribution process, or keep the system from fragmenting.

Turn a reference image, screenshot, or mockup into token-driven, accessible code — infer the design system from the reference (palette, type scale, spacing, radius, layout archetype), map it to the 3-tier tokens, rebuild it, then verify with the kit's gates. Use when the user provides a design/screenshot and wants matching UI code.

Map this token system to or from any external design system (Material Design 3, Apple HIG, Fluent, Carbon, Ant, shadcn/ui, Radix, Chakra, Mantine, Bootstrap…) — adopt their look, build on their stack, or migrate between systems. Use when the user mentions interop, migration, or a specific design-system/component-library bridge.

Optimize UI performance against Core Web Vitals — LCP, INP, CLS — with loading/code-split strategy, layout-shift prevention, and animation performance rules. Use when the user wants to improve speed, fix jank or layout shift, hit Web Vitals budgets, or make a UI feel fast on low-end devices.

Move an idea up the fidelity ladder (content-first → wireframe → low-fi → high-fi → code) with a validation plan at each level, plus user-journey mapping and usability-testing scripts. Use when the user wants to prototype, wireframe, map a user flow, or plan/run usability testing.

Upgrade an existing website or app to premium quality without breaking functionality — audit the current design, identify generic/AI tells, then apply taste and system rules surgically. Use when the user wants to improve, modernize, polish, or "make better" an existing UI/codebase.

Set up or run the token build pipeline — transform the DTCG tokens/*.json (source of truth) into platform artifacts (CSS variables, Tailwind @theme, JS/TS, iOS Asset Catalog, Android, Compose) with Style Dictionary / Tokens Studio / W3C DTCG export. Use when the user wants to generate platform theme files from tokens, wire token CI, or multi-platform token output.

Write or review UI copy — buttons, errors, empty states, microcopy, notifications, labels — using the voice & tone system (clear, concise, useful, human, honest) with the what→why→how error formula and inclusive-language rules. Use when the user needs interface copy, error messages, empty-state text, or a copy review.

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Check Python logging levels and patterns for correctness. Focus on identifying wrong severity levels and missing exception handling. Use when reviewing code quality.

Detect explicit user_id parameters in functions to identify potential opportunities for using ambient context. This is an investigation tool that flags instances for human review, not a prescriptive analyzer.

Eliminate component hedging anti-patterns that mask infrastructure failures. Build systems that fail loudly when broken instead of limping along in degraded states. Critical for production reliability and operational visibility.

DO NOT COMMIT unless user explicitly tells you to. Use this skill EVERY SINGLE TIME before creating a git commit. Provides mandatory commit message format, staging rules, and post-commit summary requirements for the MIRA project

Conduct thorough dependency audits to identify redundant code, unused features, and improper usage patterns. Examines each import to ensure we're leveraging existing capabilities rather than reinventing functionality.

Advanced LLM prompt engineering expertise for crafting highly effective prompts, system messages, and tool descriptions with Claude-specific techniques

Write pytests that test real public interfaces with actual components, no mocking, and precise assertions. MIRA-specific patterns. Use when creating or reviewing tests.

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Distill a colleague into a reusable AI skill (work + persona) using tool connections — Slack, Slack AI, Jira, GHE, Bitbucket, Confluence, SharePoint, Teams, Outlook, Notion, Linear, Google Docs, and more — without manual paste. Use when the user wants a colleague skill, digital twin of a coworker, or capture of someone's technical voice from workplace systems. Requires tool_connections + 10xProductivity verified_connections (or equivalent .env).

Design and build automation workflows using building blocks — clarify outcomes, decompose by dependencies, reuse prior art, verify each block before chaining, and research when stuck. Covers generic divide-and-conquer (problem framing, observable surfaces, investigation vs shipping, CLI contracts) plus human-like pacing for social and communication platforms. Use when automating multi-step processes across tools or platforms.

Walk through a UI flow once manually and capture a durable interaction map — which DOM elements to click, which network requests they trigger, and what field shapes they expose. Produces a reusable selector+endpoint reference for automation scripts. Use before writing any browser automation block for a JS-heavy SPA, design app, productivity app, or internal portal where selectors and API paths are not obvious from source inspection alone. Modes include Agent Browser for low-token interactive reconnaissance, Chrome DevTools Recorder, Playwright observer, and Playwright codegen+trace.

Automated LinkedIn engagement workflow. The agent finds a relevant post on your chosen topic, drafts a comment with a genuine insight, gets your approval in chat, and posts it — all in one loop. You approve once before anything is posted. Use when asked to "engage on LinkedIn", "find a post to comment on", "post a LinkedIn comment", or "engage on [topic]". Requires LinkedIn session credentials in .env. Everything runs locally — credentials never leave your machine.

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Connect any tool you use at work to your agent — including internal company tools, custom-built systems, deployment portals, incident trackers, internal knowledge bases, HR systems, and commercial tools like Slack, Confluence, Jira, Linear, GitHub, Outlook, Datadog, and PagerDuty. Use when the user wants to set up a tool connection, connect an internal or custom-built tool, or add a new tool integration from scratch.

Save a custom approach as a reusable skill

Provides adversarial code comprehension for security research, mapping architecture, tracing data flows, and hunting vulnerability variants to build ground-truth understanding before or alongside static analysis.

Instrument C/C++ with -finstrument-functions for execution tracing and Perfetto visualisation

Add gcov code coverage instrumentation to C/C++ projects

Check if specific lines were executed using gcov data

Deterministic debugging with rr record-replay. Use when debugging crashes, ASAN faults, or when reverse execution is needed. Provides reverse-next, reverse-step, reverse-continue commands and crash trace extraction.

Multi-stage pipeline for validating that vulnerability findings are real, reachable, and exploitable, preventing wasted effort on hallucinated findings, dead code paths, or findings with unrealistic preconditions.

Investigate GitHub security incidents using tamper-proof GitHub Archive data via BigQuery. Use when verifying repository activity claims, recovering deleted PRs/branches/tags/repos, attributing actions to actors, or reconstructing attack timelines. Provides immutable forensic evidence of all public GitHub events since 2011.

Recover deleted commits from GitHub using REST API, web interface, and git fetch. Use when you have commit SHAs and need to retrieve actual commit content, diffs, or patches. Includes techniques for accessing "deleted" commits that remain on GitHub servers.

Generate, export, load, and verify forensic evidence from GitHub sources. Use when creating verifiable evidence objects from GitHub API, GH Archive, Wayback Machine, local git repositories, or security vendor reports. Handles evidence storage, querying, and re-verification against original sources.

Recover deleted GitHub content using the Wayback Machine and Archive.org APIs. Use when repositories, files, issues, PRs, or wiki pages have been deleted from GitHub but may persist in web archives. Covers CDX API queries, URL patterns, and systematic recovery workflows.

Orchestrates multi-agent forensic investigations on public GitHub repositories, coordinating parallel evidence collection, hypothesis formation, verification, and report generation.

Relentless continuous-loop vulnerability hunting. Drives the red-team agent swarm to keep attacking — coverage ledger, technique rotation, dead-end memory, and a strict convergence criterion — until consecutive rounds surface no new findings, so nothing is left on the table.

The live-fire tamper loop engine — enumerate every reachable input unit (port, page, form, param, header, cookie, JSON key, GraphQL var), mutate each through the tamper matrix with real runnable probes, and promote a tamper to a finding ONLY when a named behavioral oracle fires against a recorded baseline. Loops until convergence (K consecutive dry rounds AND zero untested (endpoint,input) pairs) so no input is left un-poked and no truncated run reads as "all clear".

Use after installing the sponsio-openclaw plugin to wire the runtime end-to-end. The plugin install only registers hooks + skills; the contract library and per-environment overrides are configured here. Bootstraps the per-plugin contract library tree at ~/.sponsio/plugins/, generates fresh starter libraries for OpenClaw plugins / MCP servers via `sponsio plugin scan` (with `--introspect` to auto-discover tool inventory; the agent then applies the prompt from `sponsio plugin prompt openclaw` to extract semantic contracts using its own LLM context — no separate API call), tunes the shipped rules to the user's actual environment, and verifies with a smoke test. Use when the user says any of "configure sponsio-openclaw", "set up sponsio-openclaw", "first-time setup of sponsio for OpenClaw", "wire up sponsio in this OpenClaw session", "add Sponsio guardrails for my OpenClaw plugins", "tune the plugin for my environment", "the plugin is too strict / too loose", "calibrate sponsio rules", "scan this OpenClaw plugin", "generate sponsio rules for my OpenClaw plugin", "I just installed Y plugin / MCP server in OpenClaw, what should sponsio block", or asks how to make sponsio-openclaw actually block things.

Install, observe, tune, and enforce Sponsio: a runtime contract layer for LLM agents that blocks unsafe tool calls and scores output quality against declared rules. Use when the user wants to set up / add / install Sponsio, add guardrails or runtime safety to an LLM agent, generate or refine a sponsio.yaml, audit tool configurations for risks (data leaks, unguarded writes, missing confirmations), explain or review existing contracts, check what Sponsio would have blocked (`sponsio report`), move from observe to enforce mode, or debug why a contract is (or isn't) firing. Triggers on phrases like "set up sponsio", "add sponsio", "install sponsio", "add guardrails", "monitor my agent", "harden my agent", "audit my agent", "generate contracts", "explain my sponsio.yaml", "sponsio report", "flip to enforce", "false positive", "why is this rule firing".

This skill should be used when working with Vercel AI SDK, AI Gateway, streamText, generateText, generateObject, streamObject, tool calling, or AI SDK providers. Also relevant for "ai-sdk", "@ai-sdk/*" packages, or questions about AI SDK patterns, configuration, and best practices.

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Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files). Triggers include: any mention of 'Word doc', 'word document', '.docx', or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a 'report', 'memo', 'letter', 'template', or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.

Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.