12 skills published by wquguru across 1 repository. Together they weigh 71 942 tokens — that is what loading all of them at once would cost you in context.
12 skills 71 942 tokens total
Keep repository-level Claude and Codex agent instructions compatible by creating safe symlinks between CLAUDE.md and AGENTS.md, and between .claude/skills/* and .agents/skills/*. Use when a repository should work with both Claude and Codex, when one agent instruction file exists but the other is missing, when migrating agent skills between .claude and .agents directories, or when asked to improve Claude/Codex compatibility for a repo.
Route Claude 5 tiers (Haiku/Sonnet 5/Opus 5/Fable 5), effort, and subagents for cost-effective accepted results, and handle Claude 5 prompting, long-running execution, API behavior, refusals, and fallback.
Audit and clean the persistent context that feeds a recurring autonomous agent loop — memory files and indexes, scheduled-task / automation prompts, and CLAUDE.md / AGENTS.md — so the loop stops degrading into a self-reinforcing echo chamber. Works for Claude Code /loop crons and Codex automations.
> Daily English expression coach for intermediate software engineers. Use this skill whenever the user wants to improve their spoken or written English in a software engineering work context — especially for standups, small talk with teammates. Trigger this skill when the user says things like "teach me English", "practice English", "how do I say X at work", "daily English", "help me sound more natural", "English for standups", "how should I phrase this in Slack", or any similar request about sounding more natural or professional in English at a tech job. Also trigger when the user gives you a draft message or spoken phrase and asks you to improve it or make it sound more natural.
Safely audit and reduce CPU, load average, I/O wait, and Docker container resource pressure on macOS and Ubuntu/Linux hosts, especially remote production-like servers over SSH. Use when a host feels slow, load average is high, monitoring shows CPU spikes, Docker containers are suspected of consuming CPU, a service dashboard times out, or the user asks for a repeatable CPU optimization workflow.
Safely audit and reclaim disk space on macOS and Ubuntu/Linux hosts, including remote servers over SSH. Use when the user says their Mac, Linux box, VPS, or server disk is full, asks what can be safely deleted, wants to clean caches, developer artifacts, Docker storage, apt/Homebrew/npm/pnpm/yarn/pip/uv caches, journal/log files, Xcode data, editor extensions, node_modules, Trash, crontab/systemd timer disk checks, or asks for a repeatable free-space cleanup workflow.
Route GPT-5.6 tiers, reasoning effort, and subagents to minimize accepted-result cost subject to an explicit quality floor.
Review, improve, organize, deploy, and verify Grafana dashboards, provisioned alert rules, and the Telegraf→InfluxDB→Grafana monitoring stack. Use when working on Grafana dashboards, dashboard folders, tags, legends, panel readability, Flux/InfluxDB query correctness or performance, Prometheus/Telegraf dimensions, Telegraf JSON/HTTP scraping, anonymous access, dashboard/alert provisioning, Grafana Docker deployments, high CPU or memory caused by dashboard queries, InfluxDB cardinality and tag-vs-field modelling, provisioned alert rules and Flux alert conditions, contact points and Telegram/notification delivery, or requests like "review this dashboard", "optimize Grafana panels", "why is this dashboard slow / using CPU", "fix legends", "move dashboards into a folder", "add tags", "set up Grafana alerting", "alert won't fire / won't deliver", "fix Telegram alerts", "route alerts to a Telegram topic/thread (message_thread_id)", "bump/upgrade the Grafana version", "deploy and verify dashboards", "InfluxDB is OOM-killed / restart-looping / won't start", "series cardinality explosion", "should this be a tag or a field", or "Grafana best practices".
This skill should be used when the user wants to install or configure Pi Agent (@earendil-works/pi-coding-agent) with DeepSeek (built-in), Ant-Ling Ring-2.6-1T (single-model custom provider), and ZenMux (multi-model OpenAI-compatible aggregator including Gemini 3.5 Flash, inclusionAI Ling-3.0-flash, Claude, GPT), including auth, models.json, settings.json, a curated extension set, and known-pitfall fixes. Triggers on "配置 pi"、"setup pi agent"、"pi 装一下"、"配 ring/deepseek/gemini/zenmux/ling 到 pi".
将具体执行委派给 pi coding agent + ZenMux 便宜执行模型（默认强档 deepseek/deepseek-v4-pro：1M 上下文、推理型；廉价快档 inclusionai/ling-3.0-flash：256K、非思考、更省），Claude 负责任务-模型匹配判断、任务书编写、驱动（一次性 pi -p 或 tmux 交互长程）、硬超时重试、独立验收与失败裁决。含双模型选型、3D 素材来源、Spec-Driven、强类型可验证节点、推理档位开关等最佳实践。触发词：pi 委派、把执行外包给便宜模型、deepseek、deepseek-v4-pro、v4 pro 执行、ling、ling-3.0-flash、ling flash 执行、pi sub agent
Find Agent Skills on skills.sh and adopt only the ones that pass a security audit. Use when the user wants to discover, search for, evaluate, or install a third-party skill ("find a skill for X", "is this skill safe to install", "search skills.sh", "what skill should I use for Y"). Always runs the skills.sh security audit before recommending or installing anything, and refuses to adopt skills that fail the audit.
Download YouTube videos with yt-dlp and post-process them with ffmpeg — fetch best-quality video/audio, convert formats, extract audio, and burn in translated (e.g. Chinese) subtitles with a translator watermark. Covers cross-environment tool installation (macOS-focused) including the ffmpeg-full / libass gotcha. Triggers on "youtube 视频下载处理".