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Write and rewrite Turkish so it reads as native Turkish rather than translated English. Use for Turkish marketing copy, product and UI text, localization, documentation, support, legal or security prose, and reviews of AI-written Turkish. Detect translationese, copied clause structure, nominalization, wrong case and collocations, generic helper verbs, SaaS and sales calques, jargon leakage, forced slogans, corporate abstraction, cross-sector template residue, unsupported operational claims, and page-level repetition.

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amazon-listing-doctor是由buluslan（公众号：新西楼.AI）研发的亚马逊Listing质检Skill，他会基于 CDQ算法 / A9 / COSMO / Alexa 四大底座知识，来给Listing进行全身体检和打分，输出一份诊断报告，帮你揪出"流量加倍、弯道超车"的机会点。 更多跨境电商 AI 实战内容，请关注公众号「新西楼.AI」。 Audits Amazon listing health across CDQ / A9 indexability / COSMO intent-coverage / Alexa discoverability, plus 2026 compliance rules (title 75-char, highlights 125-char, image defects). Pure diagnosis, zero dependencies, works offline. Trigger whenever the user mentions checking/auditing/scoring/diagnosing an Amazon listing's quality — in any language; 中文卖家说 listing 体检 / 质检 / 打分 / 诊断 / 健康度 / 质量分 时同样适用。

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Universal context persistence across sessions. Auto-saves decisions, conventions, and progress to .rune/ files. Loads state at session start. Use when any skill makes architectural decisions or establishes patterns that must survive session boundaries.

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Gathers N independent, decorrelated perspectives across model families (external CLIs when present, subagents when not) and arbitrates inline. Use when a decision needs genuine debate — not same-model echo — for critique, review, or judge calls. Honest about degradation: never claims 'consensus' from a single model family.

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Deploy application to target platform. Use when user explicitly says 'deploy', 'push to production', 'ship it'. Handles Vercel, Netlify, AWS, GCP, DigitalOcean, and VPS with pre-deploy verification and health checks. Enforces cost allocation tags + Managed-vs-Self-Host crossover decisions so deploy choices map to actual unit economics, not hand-waved 'we'll optimize later'.

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Context window management. Auto-triggered when context is filling up. Triggers smart compaction and preserves critical information across compaction boundaries. Called by L1 orchestrators at context thresholds.

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Persistent state tracking and Architecture Decision Records across sessions. Use when recording a decision, ADR, or progress that must survive session boundaries. Manages progress state, module health, dependency graphs, and ADRs for any workflow.

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Use when looking up journal impact factors (JCR IF), checking a journal's impact factor by name, comparing IF across journals, or answering questions about "影响因子" / "impact factor" / "IF". Triggers on "impact factor", "journal IF", "影响因子", "JCR", "IF score", "journal rank", "which journal has higher IF", "what is the IF of". PROACTIVELY USE when user mentions journal prestige, publication venue quality, or manuscript submission target evaluation.

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Use when generating video clips with Chinese video models — text-to-video (文生视频), image-to-video (图生视频), first/last-frame and reference-to-video across 4 platforms: Bailian (Wan/PixVerse/Kling/Vidu/HappyHorse), Jimeng (doubao-seedance), MiniMax (Hailuo), Hunyuan (hy-video)

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Use when evaluating biology grant ideas in the Chinese funding context (NSFC, MOST, etc.) — diagnosing project legitimacy, mechanism-centered scientific questions, reviewer-aware logic, innovation discipline, feasibility, and scope control across funding levels (youth, general, key).

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Use tmux from the command line to run, drive, and monitor terminal programs as a coding agent (detached sessions, send-keys input injection, capture-pane output polling, formats, completion detection, and control mode). Use when a task needs long-running or interactive processes (builds, tests, servers, REPLs, watch modes, TUI apps) kept alive across turns or inspected live; skip when plain `cmd > log 2>&1 &` is enough.

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Prioritize drug targets from a ranked gene list (e.g., scRNA-seq DE output) by orchestrating parallel API queries against UniProt, OpenTargets (with integrated DepMap CRISPR essentiality + gnomAD constraint), PubMed, the Human Protein Atlas (HPA), and ChEMBL tool compounds, then re-ranking by a composite score combining protein localization, druggability, disease genetics, tissue specificity (safety), focus-cell-type expression, CRISPR essentiality, LoF safety constraint, and research maturity. Use whenever the user wants to filter, triage, prioritize, or "do due diligence" on a list of candidate genes for drug discovery, especially after a DE / DEG analysis when they say things like "which of these should I follow up on", "filter for druggable targets", "make a target dossier", "rank these for tractability", "annotate these genes for druggability", or "build a target report". Trigger even when the user says just "filter these candidate genes" or hands over a CSV from a DE pipeline.

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Search free commercial-use creative assets across multiple online sources — photos, illustrations, icons, video footage, music, sound effects, and fonts. Use when the user needs to find assets for videos, PPTs, articles, or any content creation. Trigger on phrases like "find me a photo of", "search for icons", "I need background music", "find video footage", "search fonts", "找一张图片", "搜素材", "帮我找图标/视频/音乐/字体". Covers Pexels, Unsplash, Pixabay, Iconify, Freesound, Google Fonts and more — with API-backed search where available.

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Generate an /insights-style HTML report across MANY of your agent coding sessions (Claude Code and OpenAI Codex): what you work on, how you use the agent, your wins, your friction points, and concrete fixes to become more effective — precise goal-setting, not trusting output blindly, scoping, context hygiene, right-sizing models. Reads local session history and writes one self-contained report.html. Use when the user asks to "разбери мои сессии", "оцени как я работаю с агентом", "мой insights", "session insights report", "review how I work", "на что обратить внимание", or runs "/mentor", "/mentor claude", "/mentor codex", "/mentor both". Do NOT use for reviewing the CODE produced (that's code-review) or building the agent.

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开源项目发布与场景主题营销，含 Web/CLI 数据采集验证。覆盖 GitHub star 增长、KOL 合作、Reddit / HN / Discord / Telegram 社群分发、 多语言落地（中英日韩）。从 gingiris-opensource 提炼。适用场景：要发布一个开源项目， 想在 6 个月内冲 10k star，或已发布但流量卡住要破局。

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🇺🇸 GitHub README Writing System — Craft a README that converts visitors to stars in <3 seconds. Proven structure from AFFiNE's 0→60K star journey: tagline engineering, first-screen law, section-by-section copywriting guide, Claude Code integration section, anti-patterns, and a pre-publish checklist. Use when you need to write or rewrite a specific README file. 🇨🇳 GitHub README 写作系统 — 打造 3 秒内把访客转化为 star 的 README。来自 AFFiNE 0→60K star 实战：tagline 工程、首屏法则、逐节文案指南、Claude Code 集成板块、反模式、发布前自检清单。需要写或改一个具体 README 文件时使用。 🇯🇵 GitHub README 作成システム — 3秒以内にビジターをスターに変えるREADMEを作る。AFFiNE 0→60Kスター実績から: タグライン設計、ファーストスクリーン法則、セクション別ライティングガイド、Claude Codeインテグレーション、アンチパターン、公開前チェックリスト。 🇰🇷 GitHub README 작성 시스템 — 3초 안에 방문자를 스타로 전환하는 README 작성법. AFFiNE 0→60K 스타 실전: 태그라인 설계, 첫 화면 법칙, 섹션별 카피라이팅 가이드, Claude Code 통합 섹션, 안티패턴, 게시 전 체크리스트. Triggers: "write README" | "README template" | "GitHub README" | "project description" | "tagline" | "open source README" | "README structure" | "README review" | "fix README" | "rewrite README" | "README写作" | "写README" | "README模板" | "项目介绍" | "开源项目文案" | "改README" | "README检查" | "README 구조" | "README 작성" | "README 검토" | "READMEの書き方" | "READMEレビュー"

Twitter/X Agent Operations SOP v2 — Rebuilt from real dual-account operations. Real data: • CN account: grew from 1,150 → 3,679 followers (~14 months), 1 tweet/day, OAuth1 direct post • EN account: Buffer API • Real incidents: cron silent-failed for 2 weeks unnoticed, analytics data off by 17×, thread performance worse than single posts What's new in v2: • Step-by-step onboarding (Q1→Q6), multiple choice wherever possible — no big forms • Mandatory daily log after every tweet + weekly report every Monday • Pre-publish safety checklist (agent must pass before posting) • Content pool tracker (used/unused) + alert when ≤3 items remain • Dual-account credential separation (CN: OAuth1 / EN: Buffer API) • MASTER-STATUS.md as the single source of truth — read + write every session 🇨🇳 Twitter/X Agent 运营 SOP v2 — 基于真实双账号运营经验重写。 开局对话式收集 context、强制日报+周报、发推前红线自检、素材库追踪、双账号凭证分离、MASTER-STATUS.md 单一真相来源。 真实数据：中文账号从 1,150 粉增长至 3,679 粉（约14个月），1条/天。 Triggers: "twitter运营" | "推特agent" | "发推SOP" | "AI代运营" | "推文日报" | "素材库" | "Buffer API" | "OAuth1发推" | "twitter automation" | "tweet sop" | "推特涨粉"

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Ready-to-use KOL cold outreach templates — DM scripts for Twitter, email pitches, and follow-up sequences designed to get replies from busy influencers. Tested across 500+ creator partnerships. By @WeiYipei.

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Design, prioritize and run measurable growth experiments across acquisition, activation, retention, referral and revenue. Use when a startup needs an experiment backlog, ICE/RICE prioritization, referral or UGC loops, rapid weekly testing, causal measurement, or stop conditions without spam, fake accounts or dark patterns.

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Build and measure Developer Relations programs across documentation, community, technical content, integrations, events and developer advocacy. Use when designing DevRel strategy, improving developer activation, launching an API/SDK, evaluating sponsorships, or connecting community activity to product adoption and revenue.

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Comprehensive Rust code review that fans out across detected technology areas, running them in parallel when the agent supports subagents and sequentially otherwise. Use for pre-push or pre-PR review of .rs files.

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Reviews BubbleTea TUI code for proper Elm architecture, model/update/view patterns, and Lipgloss styling. Use when reviewing terminal UI code using charmbracelet/bubbletea.

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Comprehensive BubbleTea TUI code review for terminal applications. Use when reviewing charmbracelet/bubbletea, lipgloss, bubbles, or Wish SSH code; optionally reviews each area concurrently.

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Idiomatic Go HTTP middleware patterns with context propagation, structured logging via slog, centralized error handling, and panic recovery. Use when writing middleware, adding request tracing, or implementing cross-cutting concerns.

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