1.5K skills · sorted by relevance
Build and debug Python agents with Swarms, a framework for coordinating multiple AI agents. Covers tool connections, saved memory, autonomous execution, and workflow choices such as sequential steps, parallel work, specialist delegation, and combined answers.
Monitor a swarm, a group of agents, and receive notifications when its status or agent count changes. A watcher script checks status periodically and streams changes through Monitor; separate tools provide one-time status and health checks.
Define and deploy Azure infrastructure with ARM templates and Bicep, languages for describing cloud resources in files. Get guidance on reusable modules, resource dependencies, environment parameters, and previewing deployment changes before applying them.
Manage Reddit account activity through a staged 30-day plan, progressing from browsing and voting to comments and brand promotion. Track daily work, publishing approvals, account progress, and follow-up replies using local records.
Use Google Model Armor to filter user prompts and model responses for safety. The skill covers creating templates that define checks and discovering command parameters before making API calls through the gws command-line tool.
Builds and applies 3D pharmacophore models using RDKit Pharm3D, the apo2ph4 receptor-based workflow (Heider et al. 2023), Pharmer / Pharmit for search, and PharmacoForge for protein-pocket-conditioned pharmacophore generation (Flynn et al. 2025), covering ligand-based pharmacophores from active-set alignment and receptor-based pharmacophores from binding-pocket geometry. Explicitly handles feature types, geometric tolerances, partial matching, and pharmacophore-based virtual screening. Use when identifying scaffold-hopping candidates, building shape-and-feature search queries, or transferring SAR across chemotypes.
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Implement AlarmKit alarms and countdown timers for iOS and iPadOS with Lock Screen, Dynamic Island, StandBy, and paired Apple Watch system UI. Covers AlarmManager scheduling, AlarmAttributes and AlarmPresentation, system Stop and AlarmButton secondary actions, authorization, state observation, countdown widget-extension handoff, and Live Activity integration. Use when building wake-up alarms, countdown timers, or alarm-style alerts that need Apple's system alarm experience.
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Implement AlarmKit alarms and countdown timers for iOS and iPadOS with Lock Screen, Dynamic Island, and Apple Watch system UI. Covers AlarmManager scheduling, AlarmAttributes and AlarmPresentation, AlarmButton stop and snooze actions, authorization, state observation, and Live Activity integration. Use when building wake-up alarms, countdown timers, or alarm-style alerts that need Apple's system alarm experience.
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Maintain and validate OpenShell builds for Windows on x64 and ARM64 using MSVC, Microsoft's C++ toolchain. Check compilation, build executables, run tests on native hosts, and report artifacts and unsupported driver behavior.
Retrieve genes, genetic variants, clinical annotations, and dosing guidelines from the PharmGKB API, then turn the results into concise summaries. Save the full response when you need the underlying data.
ClearML is an open-source MLOps platform that records machine-learning experiments, versions datasets, chains tasks into pipelines and runs them on remote machines through agents and queues. Use when a user asks to "track experiments with ClearML", "log metrics, artifacts and models", "version a dataset", "run training on a remote GPU with clearml-agent", "build a ClearML pipeline", "run hyperparameter optimization", or "self-host ClearML Server". Covers the clearml 2.x Python SDK, clearml-agent 3.x and ClearML Server 2.x.
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Builds and applies 3D pharmacophore models using RDKit Pharm3D, the apo2ph4 receptor-based workflow (Heider et al 2022/2023 J Chem Inf Model 63:147-158), Pharmer / Pharmit (search), and PharmacoForge (diffusion-based generation, Flynn et al 2025 Front Bioinform), covering ligand-based pharmacophore (from active set alignment) and receptor-based pharmacophore (from binding pocket geometry). Explicit handling of feature types, geometric tolerances, partial matching, and pharmacophore-based virtual screening. Use when identifying scaffold-hopping candidates, building shape-and-feature search queries, or transferring SAR across chemotypes.
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Builds and applies 3D pharmacophore models using RDKit Pharm3D, the apo2ph4 receptor-based workflow (Heider et al 2022/2023 J Chem Inf Model 63:147-158), Pharmer / Pharmit (search), and PharmacoForge (diffusion-based generation, Flynn et al 2025 Front Bioinform), covering ligand-based pharmacophore (from active set alignment) and receptor-based pharmacophore (from binding pocket geometry). Explicit handling of feature types, geometric tolerances, partial matching, and pharmacophore-based virtual screening. Use when identifying scaffold-hopping candidates, building shape-and-feature search queries, or transferring SAR across chemotypes.
The English summary is being prepared. Showing the original description.
Builds and applies 3D pharmacophore models using RDKit Pharm3D, the apo2ph4 receptor-based workflow (Heider et al 2022/2023 J Chem Inf Model 63:147-158), Pharmer / Pharmit (search), and PharmacoForge (diffusion-based generation, Flynn et al 2025 Front Bioinform), covering ligand-based pharmacophore (from active set alignment) and receptor-based pharmacophore (from binding pocket geometry). Explicit handling of feature types, geometric tolerances, partial matching, and pharmacophore-based virtual screening. Use when identifying scaffold-hopping candidates, building shape-and-feature search queries, or transferring SAR across chemotypes.
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Process AI model responses through a specified Google Model Armor template before sending them to users. Supply response text directly, pipe output from another command, or provide a JSON request for outbound safety processing.
Check a user prompt through an existing Google Model Armor template. Supply the text directly, submit a JSON request, or pass text through standard input, which lets the command read piped content.
Assess how Wi-Fi clients respond to impersonated access points using Evil Twin, KARMA, and Mana methods. Covers portal-based credential exposure, traffic interception conditions, client behavior, and limits imposed by modern protections.
🇺🇸 Reddit Marketing & Growth Playbook 2026 — The complete Reddit operations SOP for AI/SaaS founders. Covers account warming (Karma 0→500 in 20 days), shadow ban prevention, multi-account matrix via fingerprint browsers, subreddit selection across 10K+ communities, content templates (90/10 rule), Product Hunt × Reddit flywheel, AMA strategy. Why this matters NOW: Reddit content is 40.11% of ChatGPT/Claude training data — the highest-weighted English UGC source. Battle-tested by AFFiNE Reddit campaigns generating 3-4万有效曝光 with 5-8% GitHub Star conversion, plus 7 case studies (Adobe 3x conversion, Base44 $80M AMA, SenseNova-U1 multi-sub launch, Starterstory 0→3K users). 🇨🇳 Reddit 运营增长实战手册 — AI/SaaS 出海 founder 的 Reddit 完整 SOP。覆盖账号体系(20天养号 Karma 0→500)、影子封禁防御、指纹浏览器矩阵、10万+ subreddits 筛选、内容模板(90/10 法则)、Product Hunt × Reddit 飞轮、AMA 打法。**为什么现在做:Reddit 内容占 ChatGPT/Claude 训练数据 40.11%,是最高权重的英文 UGC 来源**。基于 AFFiNE Reddit 实战 3-4 万曝光 + 5-8% Star 转化,含 7 个完整案例(Adobe 3x 转化、Base44 $80M AMA、SenseNova-U1 多社区联动、Starterstory 0→3K 用户冷启动)。 🇯🇵 Reddit マーケティング＆グロースプレイブック — AI/SaaS創業者向けの完全なReddit運用SOP。アカウント育成（20日でKarma 0→500）、シャドウBAN防止、フィンガープリントブラウザマトリックス、サブレディット選定、コンテンツテンプレート（90/10ルール）、Product Hunt × Redditフライホイール、AMA戦略をカバー。**重要な理由：Reddit コンテンツは ChatGPT/Claude のトレーニングデータの40.11%を占める英語 UGC 最大の情報源**。AFFiNE Redditキャンペーン3-4万インプレッション＋5-8% GitHubスター変換で実証。7つの実例（Adobe 3倍変換、Base44 $80M AMA、SenseNova-U1、Starterstory 0→3Kユーザー）。 🇰🇷 Reddit 마케팅 & 그로스 플레이북 — AI/SaaS 창업자를 위한 완전한 Reddit 운영 SOP. 계정 워밍업(20일 Karma 0→500), Shadow Ban 방지, 핑거프린트 브라우저 매트릭스, 서브레딧 선정, 콘텐츠 템플릿(90/10 룰), Product Hunt × Reddit 플라이휠, AMA 전략을 다룹니다. **지금 해야 하는 이유: Reddit 콘텐츠는 ChatGPT/Claude 학습 데이터의 40.11%를 차지하는 최대 영어 UGC 소스**. AFFiNE Reddit 캠페인 3-4만 노출 + 5-8% GitHub Star 전환으로 검증. 7가지 사례 연구(Adobe 3배 전환, Base44 $80M AMA, SenseNova-U1, Starterstory 0→3K 사용자). Triggers: "reddit marketing" | "reddit growth" | "reddit ads" | "reddit advertising" | "reddit strategy" | "reddit promotion" | "subreddit marketing" | "reddit playbook" | "reddit SOP" | "reddit shadow ban" | "shadow ban prevention" | "reddit karma" | "reddit AMA" | "reddit product launch" | "reddit AI training data" | "reddit GEO" | "AFFiNE reddit case" | "Base44 AMA" | "SenseNova Reddit" | "subreddit selection" | "r/SaaS posting" | "r/MachineLearning self-promotion" | "fingerprint browser matrix" | "AdsPower reddit" | "reddit account farming" | "reddit 90/10 rule" | "reddit content strategy" | "reddit indie founder" | "reddit cold start" | "Reddit 运营" | "Reddit 营销" | "Reddit 防封" | "Reddit 矩阵号" | "Reddit 出海" | "Reddit 引流" | "Reddit 冷启动" | "影子封禁" | "Reddit Karma" | "Reddit 养号" | "Reddit AMA" | "Reddit マーケティング" | "Reddit グロース" | "Reddit 마케팅" | "Reddit 그로스"
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Reverse engineers ARM/AArch64 malware by identifying the architecture and instruction set state (ARM/Thumb), parsing ELF/Mach-O ARM headers, and orienting analysis around the ARM calling convention. Activates for requests to reverse ARM binaries, analyze AArch64 malware, or handle ARM/Thumb instruction-set decoding.
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Captures screenshots on registered computers and manages saved captures. Supports screen recording on macOS and timed screenshot sequences on HarmonyOS, checking available capabilities first and returning the finalized file path when recording stops.
Create a Google Model Armor template for checking AI prompts and responses. Specify a Google Cloud project, location, and template ID, then use a preset or provide custom settings in JSON.
Design and deploy a Google Cloud global external Application Load Balancer with caching and web attack protection. Matches settings to workload types, discovers existing backends, and generates Terraform or gcloud deployment scripts.
Find the right workflow for MNN CPU work, from investigating slow execution to implementing calculation routines and planning tests. Compare ARM, x86_64, and RISC-V backend structures before choosing the relevant guidance.
Runs a long swarm of hundreds to thousands of cheap headless coding workers that drive a large failing-test backlog to green. Failures are clustered by signature, each worker fixes one cluster in its own git worktree, a serial no-regression merge gate lands the fixes, the loop resumes across restarts for hours, and it federates across sibling sessions over git branches when one container's CPU is not enough. Use for "fix these thousands of failing tests with a Haiku swarm", "drive the test suite to green with hundreds of agents", "build this compiler or interpreter against a conformance suite", "launch, relaunch or check in on the swarm loop", "the swarm is stuck, idle or rejecting everything", "parallel agents fixing tests keep hitting merge conflicts and regressions", "add sibling containers to the swarm", or "compare models on the tail the swarm could not fix". Ships the harness, a per-project TOML config, a fake-worker selftest, check-in signals and the 2026-10-12 haiku-cc failure catalogue.
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