cmux 计算机操作
SkillSkillsMP
io.github.manaflow-ai/cmux/cmux-cua
仅在用户明确提出要使用 Computer Use 时使用：通过内置引擎，从 cmux 智能体会话中驱动真实的 macOS 应用（无障碍树加截图，支持点击、输入、滚动、拖拽，并带可辨识的光标），或向用户说明其由用户授权的权限设置。阅读或发现本技能不构成同意。
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“Ai Company” 共 1,185 个结果
io.github.manaflow-ai/cmux/cmux-cua
仅在用户明确提出要使用 Computer Use 时使用：通过内置引擎，从 cmux 智能体会话中驱动真实的 macOS 应用（无障碍树加截图，支持点击、输入、滚动、拖拽，并带可辨识的光标），或向用户说明其由用户授权的权限设置。阅读或发现本技能不构成同意。
io.github.deepseek-ai/deepseek-harness/dsh-error-handling
Apply error-handling principles when designing or reviewing failures, recovery, retries, background tasks, and user-visible error reports.
io.github.deepseek-ai/deepseek-harness/dsh-client-ui-ux
Design and review DeepSeek Harness client UI changes — visual token discipline, reuse-before-adding, feedback surfaces (toast vs in-place notice vs empty state), overlay and menu safety, platform window adaptation, loading states, and error copy placement. Use when adding or changing product-user-visible GUI behavior in packages/client, or when reviewing such a PR.
io.github.berriai/litellm/rust-string-enums
Define or refactor Rust string-valued enums and their Serde adapters in litellm-rust, using Strum and serde_with while preserving parsing, wire values, and schemas
io.github.coreyhaines31/marketingskills/revops
当用户需要收入运营、线索生命周期管理或市场到销售的交接流程支持时使用。用户提到 RevOps、revenue operations、线索评分、线索路由、MQL、SQL、pipeline stages、deal desk、CRM 自动化、marketing-to-sales handoff、数据清洗、线索没有流转到销售、pipeline 管理、线索资格判定，或“市场何时该交接给销售”等场景时也适用。凡涉及连接市场与收入的系统和流程均可使用。冷邮件触达见 cold-email，邮件序列营销见 emails，定价决策见 pricing。
io.github.deepseek-ai/deepseek-harness/cordis-composition-reference
Use when writing or reading a Cordis bundle patch or agent preset for this harness and you need the Loader YAML dialect (insert, override by id, group, disabled, isolate, !!js) or the list of installable Harness plugin packages and what each provides.
io.github.coreyhaines31/marketingskills/marketing-loops
当用户想设置周期性、自动运行的营销工作流——由 AI 代理按固定节奏（每周、每天或按触发器）重复执行的循环，而不是一次性任务——时使用。用户提到 marketing loop、recurring marketing workflow、automate my marketing、marketing on autopilot、weekly marketing review、ad fatigue check、content refresh loop、churn watch、ranking drop alert、always-on marketing、marketing automation workflow 或“每周都跑一次这个”等场景时也适用。用于选择、适配并排期一个持续运行的营销循环，由它编排其他营销技能。一次性营销点子见 marketing-ideas，实验循环见 ab-testing。
io.github.mem0ai/mem0/mem0-integrate
以目标驱动、TDD 的流程将 Mem0 集成到现有仓库。自动检测仓库语言,并让用户在 Mem0 Platform(托管)与 Mem0 开源(自托管)之间选择。先写失败测试再做实现。产出本地功能分支和供配套验证技能使用的 .mem0-integration/ 产物。触发条件:用户说 "integrate mem0"、"add mem0 to this repo"、"wire mem0 into <repo>",或询问如何在现有项目中加入记忆。不触发:一般 SDK 使用(用 skill:mem0)、CLI 使用(用 mem0-cli)、Vercel AI SDK(用 skill:mem0-vercel-ai-sdk)。成功后调用 skill:mem0-test-integration 在同一工作区验证(松耦合)。
io.github.mem0ai/mem0/mem0-cli
Mem0 CLI——mem0 记忆操作的命令行界面。触发条件:用户提到 "mem0 cli"、"mem0 command line"、"@mem0/cli"、"mem0-cli"、"pip install mem0-cli"、"npm install -g @mem0/cli",或在终端/shell 中运行 mem0 命令(mem0 add、mem0 search、mem0 list、mem0 get、mem0 init、mem0 config、mem0 import)。查询包含 --user-id、--output、--json、--agent 等 CLI 参数,或描述 bash/zsh/终端使用时也触发。不触发:Python/TS 代码中的 SDK 集成(用 mem0 技能),或 Vercel AI SDK provider(用 mem0-vercel-ai-sdk)。
io.github.mem0ai/mem0/mem0
Mem0 Platform SDK,为 AI 应用添加持久记忆。触发条件:用户提到 "mem0"、"MemoryClient"、"记忆层"、"记住用户偏好"、"持久上下文"、"个性化",或需要为聊天机器人、agent 或 AI 应用添加长期记忆。涵盖 Python SDK(mem0ai)、TypeScript SDK(mem0ai)及框架集成(LangChain、CrewAI、OpenAI Agents SDK、Pipecat、LlamaIndex、AutoGen、LangGraph),也涵盖开源自托管的 Memory 类。模糊查询时这是默认的 mem0 技能。不触发:CLI 命令、终端使用或 shell 脚本(用 mem0-cli),或 Vercel AI SDK/@mem0/vercel-ai-provider/createMem0(用 mem0-vercel-ai-sdk)。
io.github.mem0ai/mem0/mem0-oss-to-platform
制定并执行将项目从 mem0 开源/自托管 SDK(本地 Memory 类)迁移到 mem0 Platform 托管 SDK(MemoryClient 类)的方案。当开发者想把 mem0 从 OSS/自托管迁移到托管 API 时使用,例如"把 mem0 设置迁移到平台""从自托管 mem0 切换到 MemoryClient""改用 mem0 API key 而非本地 Qdrant""把 mem0 迁到云端/托管服务""用平台配置替换本地向量库+嵌入器配置"。适用于 Python(from mem0 import Memory → from mem0 import MemoryClient)和 TypeScript/JavaScript(import { Memory } from "mem0ai/oss" → import MemoryClient from "mem0ai")。即使用户没说"迁移"二字,但明显希望现有 mem0 集成改为访问托管平台时也应触发。先产出可审阅的迁移计划,经开发者确认后执行。
io.github.garrytan/gbrain/postgres-adopt
Detect which gbrain engine is in use (PGLite vs Postgres), prefer Postgres for agent-harness installs, install/provision Postgres (Supabase discovery via SUPABASE_ACCESS_TOKEN, local Postgres, opt-in Docker), and move an existing PGLite brain to Postgres with its history (plan, ask, run). Detection is one engine-free command; the install ladder is one flag.
io.github.garrytan/gbrain/postgres-adopt
检测当前 gbrain 使用的是哪种引擎（PGLite 还是 Postgres），在智能体安装场景中优先选择 Postgres，并安装或开通 Postgres（通过 SUPABASE_ACCESS_TOKEN 发现 Supabase、本地 Postgres、需显式选择的 Docker），再用受保护的引擎迁移把已有的 PGLite brain 搬移过去。检测只需一条与引擎无关的命令，安装阶梯也只需一个参数。
io.github.deepseek-ai/deepseek-harness/record-browser-gif
使用可用的浏览器控制工作流，可选 Playwright Videos 提升采集帧率，并以确定性编码把浏览器或 Web UI 交互演示录制为优化后的 GIF，再用 gh --attach 将 GIF 附到 PR 上；无法附加时回退到专用 assets 分支。在被要求制作、录制或生成演示浏览器工作流的 GIF 时使用；凡改动面向产品用户的 GUI 行为的 PR，都必须附带从该 PR 真实服务端与模型流程录制的 GIF。
io.github.deepseek-ai/deepseek-harness/cordis-plugin-development
Use when designing, reviewing, adding, enabling, disabling, installing, configuring, or debugging a plugin, bundle, feature, page, panel, tool, or MCP connection in the current Harness profile, including a shipped plugin that is disabled by default, and for any visual object, decoration, or widget request that names no other destination, which means an installed UI plugin rendered in the Harness Web UI.
io.github.coreyhaines31/marketingskills/sms
当用户想规划、搭建或优化短信/MMS 营销时使用，包括欢迎流程、购物车挽回、购后触达、流失挽回、促销群发或验证码类事务短信。用户提到 SMS marketing、text message campaigns、SMS sequence、SMS automation、abandoned cart text、post-purchase SMS、Klaviyo SMS、Postscript、Attentive、Twilio、A2P 10DLC、TCPA、SMS compliance、short code、toll-free SMS、MMS campaign、“要不要做短信”或“SMS vs email”等场景时也适用。邮件序列见 emails，短信文案框架见 copywriting，采集手机号的弹窗见 popups。
io.github.moeru-ai/airi/stage-tamagotchi-godot-csharp
仅在 engines/stage-tamagotchi-godot 目录内工作时套用该引擎本地的 C# 开发方法与代码风格,包括其 .cs 文件、.csproj、引擎本地 .editorconfig 与 Godot 特有的 C# 结构决策。不用于 TypeScript、Electron、渲染层代码、共享工作区配置、仓库级 C# 约定,或 engines/stage-tamagotchi-godot 之外的任何文件。
io.github.K-Dense-AI/scientific-agent-skills/polars-bio
Performs genomic interval overlap, nearest, merge, coverage, complement and subtraction on Polars DataFrames, and reads or writes BED, VCF, BCF, BAM, CRAM, GFF, GTF, FASTA and FASTQ data. Use for coordinate-aware genomic joins, read-depth analysis, lazy bioinformatics I/O, SQL queries or migration from bioframe.
io.github.K-Dense-AI/scientific-agent-skills/imaging-data-commons
Queries and downloads public cancer imaging data from NCI Imaging Data Commons. Supports IDC collection discovery, DICOM access, radiology (CT, MR, PET) and pathology AI datasets, metadata SQL, visualization, licensing, and citations. Uses public metadata and download routes without authentication; optional BigQuery and Google Healthcare routes require Google credentials.
io.github.K-Dense-AI/scientific-agent-skills/cantera
Runs Cantera homogeneous chemical reactors and evaluates ignition delay with mechanism provenance, conservation checks, and numerical refinement. Use for combustion kinetics, closed adiabatic ideal-gas constant-volume or constant-pressure ignition, temperature histories, or mechanism-specific ignition-delay comparisons.
io.github.K-Dense-AI/scientific-agent-skills/pycalphad
Computes finite-temperature CALPHAD equilibria, phase fractions, and phase compositions from thermodynamic TDB databases using pycalphad. Use for alloy phase stability, equilibrium temperature sweeps, tie lines, lever-rule checks, or reproducible phase-fraction calculations with explicit components and mole-fraction conditions.
io.github.K-Dense-AI/scientific-agent-skills/pydeseq2
Performs bulk RNA-seq differential expression analysis with PyDESeq2, including count validation, formula designs, explicit contrasts, Wald tests, FDR correction, coefficient-matched LFC shrinkage, and result visualization. Use for PyDESeq2 or Python DESeq2 workflows with biological replicates.
io.github.K-Dense-AI/scientific-agent-skills/dask
Scales pandas, NumPy, and custom Python research workflows beyond memory or across clusters with Dask. Covers DataFrames, Arrays, Bags, Futures, chunking, schedulers, and distributed diagnostics. Use for partitioned file processing, scientific array computation, or parallel tasks whose memory and dependency structure require Dask.
io.github.K-Dense-AI/scientific-agent-skills/matlab
Builds, reviews, migrates, and plans MATLAB or GNU Octave numerical workflows. Use for arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.