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Refine academic writing for computer science research papers targeting top-tier venues (NeurIPS, ICLR, ICML, AAAI, IJCAI, ACL, EMNLP, NAACL, CVPR, WWW, KDD, SIGIR, CIKM, and similar). Use this skill whenever a user asks to improve, polish, refine, edit, or proofread academic or research writing — including paper drafts, abstracts, introductions, related work sections, methodology descriptions, experiment write-ups, or conclusion sections. Also trigger when users paste LaTeX content and ask for writing help, mention "camera-ready", "rebuttal", "paper revision", or reference any academic venue or conference. This skill handles both full paper refinement and section-by-section editing.

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Transparent, rigorous research with full methodology — not a black-box API wrapper. Conducts exhaustive investigation through mandated 2-cycle research per theme, APA 7th citations, evidence hierarchy, and 3 user checkpoints. Self-contained using native OpenClaw tools (web_search, web_fetch, sessions_spawn). Use for literature reviews, competitive intelligence, or any research requiring academic rigor and reproducibility.

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This skill should be used when interacting with Apple Calendar on macOS. Use it for listing calendars, viewing events, creating/updating/deleting calendar events, and checking availability/free-busy times. Triggers on requests like "check my calendar", "schedule a meeting", "what's on my schedule", "am I free tomorrow", or any calendar-related operations.

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Calculate ROI for AI-as-a-Service (managed AI agents). Estimates cost savings, efficiency gains, and payback period for deploying AI agents across business operations. Use when evaluating whether managed AI agents make financial sense for a company.

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Analyze any GitHub repository in AI-friendly format. Convert entire repos to single markdown documents, generate architecture diagrams with Mermaid, inspect structure trees, language breakdowns, and recent activity. Includes GitHub URL tricks, API shortcuts, and advanced search techniques. Read-only analysis — never executes code from repositories. Built for AI agents — Python stdlib only, no dependencies. Use for repository analysis, code architecture review, open source research, GitHub intelligence, repo documentation, and codebase understanding.

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Personal knowledge base for capturing and retrieving information about people, places, restaurants, games, tech, events, media, ideas, and organizations. Use when: user mentions a person, place, restaurant, landmark, game, device, event, book/show, idea, or company. Trigger phrases: "remember", "note that", "met this person", "visited", "played", "what do I know about", etc. Brain entries take precedence over daily logs for named entities.

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Create short URLs and submit feature requests using 1p.io. Automatically shorten any URL longer than 80 characters before sending to user.

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AI-powered presentation generation using 2slides API. Create slides from text content, match reference image styles, or summarize documents into presentations. Use when users request to "create a presentation", "make slides", "generate a deck", "create slides from this content/document/image", or any presentation creation task. Supports theme selection, multiple languages, and both synchronous and asynchronous generation modes.

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Securely inject secrets from 1Password into agent workflows. Uses service accounts with op run/.env.tpl as the primary pattern, op read as fallback. Includes hardened security rules, input validation, and troubleshooting for auth/permission failures. Use when accessing API keys, credentials, or any 1Password secret.

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Diagnose GitHub bug reports in wordbricks/onequery. Use when given a GitHub issue URL from wordbricks/onequery and asked to decide next steps such as verifying against the repo, requesting more info, or explaining why it is not a bug; follow any additional user-provided instructions.

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Check a change for performance regression against this machine's recorded baseline - decide whether the diff needs checking, run the deck benchmark under the hygiene wrapper, and attribute any regression to an element. Use before opening a PR that touches the simulation hot path.

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QA a change by driving the running dev app like a real QA engineer - start the server and a real browser session, write a checklist from the agreed requirements and the diff, execute every item with evidence, then go looking for anything that looks off even where the diff did not touch. Reports findings by severity and separates regressions from pre-existing problems. Use after a change is implemented and the gates pass, when the user asks to "QA this", "test it in the app", or "check it actually works".

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Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always

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Use when implementing any feature or bugfix, before writing implementation code

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Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

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风水堪舆 · 阳宅/阴宅选址与布局建议。用户提供**地点**（最好是准确经纬度）、**周边环境描述或图片**、 以及**阳宅或阴宅**（可选朝向/坐向、建造或入住年、居者生辰性别、所求何事）→ 先用脚本**确定性**地 定位经纬度、查周边山水路(砂水)与地势、定坐向二十四山、排三元九运玄空飞星、八宅游年 → 再参《葬书》 《青囊经》《撼龙经》《雪心赋》等古籍，以**形势派(峦头)·理气派(玄空/八宅)·现代建筑学**三层解读 → 最后给出「藏风聚气·背山面水·明堂开阔·阴阳平衡·五行调和」的交叉对照表与**可执行、非破坏性**的改善建议。 Use this skill whenever the user asks about 风水、堪舆、看风水、阳宅、阴宅、选址、坐向、朝向、 住宅/办公室/店铺/墓地风水、玄空飞星、八宅、命卦、龙脉、砂水、四神砂、藏风聚气、明堂、财位、 文昌位、穿堂煞、户型点评、装修布局、买房看风水. Triggers: "看风水", "风水建议", "这房子风水", "阳宅", "阴宅", "选址", "坐向", "朝向", "玄空飞星", "八宅", "财位", "办公室风水", "墓地风水", "feng shui", "geomancy", "house feng shui", "office feng shui", "site selection". 即使用户只是随口问「帮我看看这房子风水好不好」「大门朝这边行不行」，也应主动使用本技能。

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Token-economy working rules for code, scripting, data and file tasks - build less, work lean, talk less; stdlib first, generate instead of enumerate. Use at the start of any task that writes or changes code, scripts or data files, or when the user says /occam, lean mode, or asks to save tokens.

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Use when writing, reviewing, or architecting Rust code — systems programming, async services, CLI tooling, web backends, or any work in `.rs` files. Triggers on editing `.rs` or `Cargo.toml` files, or mentions of "Rust", "Tokio", "Axum", "cargo", "borrow checker", "lifetime", "trait object", "async Rust", "crate", "rustc", or explicit requests to "build this in Rust" / "rewrite X in Rust". For adversarial security review of Rust code see security-reviewer.

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Use when building games or interactive software in Godot 4 with C# — designing scenes and nodes, writing gameplay code, handling input, physics, animation, UI, rendering, save systems, performance work, WebSocket-based multiplayer, or exporting to platforms. Triggers on "Godot", "GDScript", "C# Godot", "scene tree", Godot node types (Node2D, Node3D, Control, CharacterBody), "_Process", "_PhysicsProcess", "signal", "autoload", "RPC", "WebSocketMultiplayerPeer", "shader", "export preset", or any Godot 4 project files. For game design see game-systems-designer; for balance see game-balancer; for game-specific intake see game-design-shaper.

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Scan and redact PII and sensitive data (emails, phone numbers, SSNs, API keys, IP addresses, credentials, amounts, company/person names) from repository files. Includes a pre-commit hook to block commits containing PII. Use when asked to audit code for sensitive data, sanitize files before publishing, or install PII detection hooks. For application security hardening see security-engineering.

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Structures a vague engineering request into a well-scoped task brief before any implementation begins. Use when the user has an engineering goal but the ask is missing which repos are in scope, what "done" means, constraints, or open questions. Triggers on "shape this", "scope this out", "frame this work", "write a brief for", "I want to build" (with unclear scope), or the /shape slash command. Produces a filled task template (multi-repo feature, single-repo change, investigation, or bugfix). Not for already-scoped work — go straight to execution. If the domain is unclear, ask one qualifying question before routing — for marketing intake see marketing-shaper, game-design intake see game-design-shaper.

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Conducts multi-axis code review across correctness, readability, architecture, security, and performance. Use before merging any change. Use when reviewing code written by yourself, another agent, or a human. Triggers on "review my PR", "review this diff", "code review", "review this changeset", "is this ready to merge", "pre-merge review".

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Use to structure a vague marketing request into a well-scoped brief before any real work begins. Triggers on "shape this campaign", "plan this launch", "scope this content", "marketing plan", "growth plan", "content strategy", "outbound plan", or when invoked as the /mshape slash command. Produces a filled brief (campaign, content, optimization, research, or pipeline) that downstream marketing skills can act on. Do not use for work already well-defined — go straight to execution. For engineering task shaping see prompt-shaper; for game-design intake see game-design-shaper.

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Auto-assembles a domain-specific expert panel (7–10 experts), scores any content or strategy artifact against a typed rubric, and iterates until the aggregate hits 90+ (max 3 rounds). Use as a quality gate on copy, email sequences, landing-page drafts, strategy docs, charts, titles, or recruiting evaluations — or when another skill needs a final review gate on its output. Triggers on "expert panel this", "score this", "rate these variants", "quality check this", "panel review", "expert score", "evaluate this copy/strategy/page". For variant generation and multi-round conversion optimization see autoresearch; for live-URL CRO auditing see conversion-ops; for the scripted content-production pipeline see content-pipeline.

日本語の概要は準備中です。原文の説明を表示しています。