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Rewrite text so it reads like a real person wrote it, not an AI. Auto-detects Russian vs English and applies the matching rule set. Use when the user asks to humanize text, remove the "AI smell", de-AI, or make AI output sound natural - especially for Russian. Russian requests count too - очеловечь, перепиши как человек, оживи текст, убери запах ИИ, сделай живее, звучит как робот, убери канцелярит. Also use when asked whether a text was written by AI - нейросеть писала?, похоже на ChatGPT?, видно ли, что это ИИ, человек или бот писал, did AI write this? - then check and report findings without rewriting. While installed, also keep your own Russian and English writing free of the quick-list stock phrases, silently; never edit the user's text unless asked.

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Find the most valuable commercial insight and the monetization point closest to lawful revenue for a project, product, service, website, community, content account, open-source tool, or business idea. Use when a user asks how to monetize, who pays, why people do not buy, what to charge, whether to keep building, what to stop, what to do next, or asks to continue or deepen an earlier business analysis. Answer the user's actual question before asking for more information and carry the analysis forward without repeating it. When the buyer, purchased result, or delivery ability is unknown, do not select them from industry stereotypes; use explicit conditional candidates and an evidence-gathering next step. Not for investment advice, income forecasts, get-rich-quick schemes, or guaranteed results.

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AWS の構成図を draw.io の XML（.drawio）で描く。公式の AWS4 アイコンを使い、タイトルや凡例や番号バッジのような装飾を足さず、どのサービスがどこにあって何とつながっているかだけで読める図にする。AWS 構成図、アーキテクチャ図、インフラ構成図、ネットワーク構成図、VPC の図を描いてほしいとき、draw.io で AWS の図を作りたいとき、既存の構成図を直したいときは必ずこの Skill を使う。Use this whenever the user asks for an AWS architecture diagram, cloud infrastructure diagram, VPC diagram, or a draw.io file of AWS resources, even if they don't say "draw.io". Mermaid、PlantUML、Python の diagrams ライブラリなど、ほかの形式を指定されたときは使わない。Do not use it when the user explicitly asks for Mermaid, PlantUML, or the Python diagrams library.

Derive a theoretical result step-by-step from first principles or established theorems, with every step explicitly justified and special cases checked. Use when deriving a formula or theorem from first principles, proving a mathematical statement by logical deduction, re-deriving a textbook result for verification or adaptation, extending a known result to a more general setting, or producing a self-contained derivation for a paper or thesis.

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Deep, iterative, multi-source research that explores a topic from multiple perspectives, detects contradictions, and produces a comprehensive vault note with fact-level citations and a confidence map. Use this skill whenever the user says /deep-research, "deep research X", "research X thoroughly", "investigate X deeply", "I need to really understand X", or wants research that goes beyond a quick web search. Also use when the user wants to understand a complex, contested, or multi-faceted topic, compare competing approaches with evidence, or build a comprehensive mental model they can trust for decisions. This is heavier than /research — it searches web, academic papers, Reddit, and the vault, then loops through exploration and critique rounds until the evidence base is solid. When in doubt between /research and /deep-research, prefer this one if the topic is complex, contested, or the user wants depth over speed.

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Draft, expand, revise, structure, and maintain a full Islamic University of Technology BSc thesis from source documents using the local iutbscthesis LaTeX template. Use when writing or editing thesis metadata, front matter, abstract, chapters, related works, methodology, results, discussion, conclusion, citations, appendices, figures, tables, code listings, or compile guidance for main.tex. Also use when the user expects an AI agent to turn papers, notes, PDFs, LaTeX sources, bib files, data tables, and assets into a substantial thesis manuscript chapter by chapter without requiring repeated babysitting.

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The TaskBarHero game updated (new version) and the tbh-meter reader needs re-calibration — symptoms are gold showing 0, stage mode "?", or the live party/XP/stats dropping out (party empty / no live levels / runs degraded) after a patch, or you're prepping a meter release for a new game build. Runs the diagnose→dump→diff→reseed→bump→verify playbook (the fix is usually a reseed, but a build can also change an Obscured encoding with zero offset movement — caught only by the live gate, fixed by a read-strategy change). Use when the game version changed, after a TBH patch, when a player reports gold/stage/party/XP broken following an update, or when asked to re-seed / re-calibrate the reader for a new build.

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Hands on the local Mac for any agent - system state (battery, volume, brightness, IP), app launch/quit, open websites and YouTube, media and browser control via keystrokes, screenshots, clipboard, notifications, TTS, and honest mic/speaker hardware tests. Use whenever the user asks to control the computer, change volume or brightness, open an app or site, play or pause media, check battery/IP/running apps, take a screenshot, or test the mic or speakers. Rebuilt from the PC-Automation repo: the agent is the intent brain; this skill is only the hands.

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This skill should be used when the user asks to "minimize the repro path", "/minimize", "shrink the crash steps", "精简复现路径", "找最小复现", "把 12 步压成 3 步", or wants to compress a recorded crash repro_path into the shortest sequence that still reproduces the same crash signature. Drives live Android replay via mobile-mcp + ui-mcp + log-mcp, validates each candidate subset with analyzer-mcp's signature, and updates the session's crash record with the verified minimal path; iOS sessions use the static fallback because ui-mcp replay is Android-only.

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This skill should be used when the user says "找 bug" / "看一下有没有 bug" / "smart-qa" / "/smart-qa" / "帮我测一下这个项目" / "智能 QA" / "推断业务流程测一下"，or asks for an autonomous bug-hunt where they DON'T already know what to test. Distinct from `qa` (blind exploration) and `devtest` (verifies a specific change). smart-qa READS the project source — prefers PRD/requirements docs, falls back to code (activities, routes, click handlers, API calls) — then proposes a focused test plan for confirmation before driving the app. v1 stops after the user confirms the plan and hands off to the existing `qa` skill for execution. v2 (planned) adds per-step assertions.

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Operate Railway infrastructure: sign up for or sign in to a Railway account, create projects, provision services and databases, manage object storage buckets, deploy code, configure environments and variables, manage domains, troubleshoot failures, check status and metrics, set up Railway agent tooling, and query Railway docs. Use this skill whenever the user mentions Railway, signing up, creating an account, registering, logging in, deployments, services, environments, buckets, object storage, build failures, agent setup, MCP, or infrastructure operations, even if they don't say "Railway" explicitly. Also invoke this skill when the user asks to be signed up, registered, or onboarded to Railway: do not refuse — drive them through the unauthed `railway up` flow (deploys + signs up on the fly) or `railway login` (which creates new accounts on the fly).

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Cut a versioned LoreWeave release — draft the CHANGELOG.md entry from what shipped since the last tag, confirm the SemVer bump and pre-release/release decision with the user, then tag and push to trigger the automated build + GitHub Release. Use when the user says "cut a release", "publish v...", "ship v0.1.0", "make a release", or asks what's changed since the last version.

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Before any multi-step work with a stated goal — a feature, a bugfix spanning multiple files, a QA sweep, a doc draft + iterations, a multi-day investigation — create an ATO Mission instead of doing it via bare `ato dispatch` calls. A Mission persists the goal + the verifiable success criteria, lets the coordinator tick drive the work across days, captures every event in a structured audit trail (SQLite + markdown narrative), and integrates parallel agents' work via merge strategies. Complement to `ato-warroom` (the cross-family decision before you start) and `ato-review` (the post-code-diff review). Missions is where multi-step work LIVES; war-rooms are where decisions ABOUT it get made; reviews are where the resulting commits get vetted. Fires when: the work has more than one decision point, a verifiable end state, or runs across more than one session. Use it for any ATO development that doesn't fit in a single dispatch.

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Design the technical architecture for a NowStack Mobile app from .agents/plan/PRD.md, mapping the product onto the FIXED boilerplate stack (Convex schema + functions, Expo Router screens, Better Auth, Apple IAP/Stripe, R2), then save .agents/plan/ARCHITECTURE.md. Use for "design the architecture", "ns architecture", "plan the schema and screens", or as the second planning step after ns-prd and before ns-tasks. Not a blank-slate stack design — the stack is set; this maps features onto it.

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Search, scrape, and interact with the web via the Firecrawl CLI. Use this skill whenever the user wants to search the web, find articles, research a topic, look something up online, scrape a webpage, grab content from a URL, get data from a website, crawl documentation, or interact with pages that need clicks or logins. Also use when they say "fetch this page", "pull the content from", "get the page at https://", or reference external websites. This provides real-time web search with full page content and interact capabilities — beyond typical built-in browsing without the Firecrawl CLI. Do NOT trigger for local file operations, git commands, deployments, or code editing tasks.

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List the skills on this machine (your Library) and the skills your team shares (the Marketplace) as Markdown boards from the terum-skills CLI. Use when the user asks what skills they have, what the team shares, what is installed, edited, untracked or missing an eval, or wants an overview of their terum-skills state.

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。

When the office_edit tool is available and the document is in the workspace, use the office skill instead: the user watches the edit live. Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx) or Word templates (.dotx). Triggers include: any mention of Microsoft Word Documents, such as 'Word doc', 'word document', '.docx', '.dotx', 'microsoft doc'. Also use when extracting or reorganizing content from .docx or .dotx files, inserting or replacing images in documents, find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a deliverable as a Word or .docx file (to download, email or print), use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or coding unrelated to document generation.

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。

Set up the prerequisites for the Quarkus + LangChain4j agentic stack in the coding agent you are running — detect and (with approval) install the toolchain, register the Quarkus Agents MCP and context7 MCP servers, and write the conventions file into your project. User-invoked only, via /setup-agentic-scaffolding; it prepares the environment the /scaffold-project and /audit-project skills depend on.

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Cut a hyalo release end-to-end — verify/bump the workspace version, rotate the changelog with hyalo itself, sync the winget fork, publish the GitHub release with curated + auto-generated notes, and watch the pipeline. Use this skill whenever the user wants to release a new hyalo version, cut/tag/publish vX.Y.Z, says /release, or says things like "ship 0.19.0", "cut the release", or "publish the release".

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。

Detect and remove the tells of AI-generated writing in BOTH English and Spanish, and read back the evidence honestly. Use when the user asks to "de-AI" / "humanize" / "un-slop" a draft, to examine whether text carries the tells (delve, tapestry, "it's not just X, it's Y", "here's the thing", em-dash overuse, an assistant's own closing line left in the document), to compare documents for overlap, or mentions signs-of-ai / SignsOfAI. Backed by the SignsOfAI engine — for a measured 0–100 score, sentence-rhythm burstiness, originality, citations, a writer baseline or perplexity, hand off to that engine (web app, CLI or MCP server) as described below. It cannot determine who wrote a text and must never imply that it can.

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。

Create polished, validated architecture, workflow, sequence, data-flow, and lifecycle/state diagrams as explorable standalone HTML with inline SVG, dark/light themes, optional trace motion, and PNG/JPEG/WebP/SVG/WebM export. Accept plain-language requirements or pasted Mermaid flowchart, sequenceDiagram, and stateDiagram input; inspect repository evidence when the diagram must reflect real code. Use when the user asks to visualize system architecture, infrastructure, cloud/security/network topology, technical workflows, API call sequences, request lifecycles, data pipelines, ETL/ELT, data lineage, state machines, or to convert/beautify Mermaid. Also use for Archify, interactive or animated HTML diagrams, インタラクティブ図, 動く構成図. When an editable .drawio file is required, use the draw.io workflow instead and treat Archify HTML only as a supplement.

"Apply pragmatist philosophy (Peirce, James, Dewey) to frame knowledge as instrumental for action, evaluate ideas by their practical consequences, and conduct inquiry as problem-solving. Use this skill when the user needs to bridge theory and practice, evaluate competing theories by their usefulness, employ abductive reasoning to generate hypotheses, or when they ask 'which theory is more useful here', 'how do I move from abstract ideas to actionable knowledge', or 'what practical difference does this distinction make'.".

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Builds and audits a concept-ontology for a regelrecht corpus to decide and verify reference types — cross-law source-binding vs intra-law source-binding vs open-norm leaf vs external-fact leaf — and to catch concept conflation across laws. Core principle: a value's reference type is fixed by WHO derives it, and a binding is sound only when producer and consumer denote the SAME concept; the lexical surface (the word in the text) is not the concept. Detects false friends (one term, different concepts — e.g. a register-based vs a factual "naar de omstandigheden"-based residence/ingezetene concept), orphan plain-params (a value a regulation actually derives, modeled as a brute input), negation/complement re-entry (¬X modeled as an independent leaf next to X), and duplication (one concept re-entered as several independent leafs, losing the single-source-of-truth invariant). Produces a law-level glossary and a stelsel-level concept map with sound same-concept binding edges and flagged lexical-match-but-concept-mismatch edges. Use when deciding whether a value should be cross-law/intra-law/leaf, when the same term means different things across laws, or when a binding might silently conflate concepts.

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Drive the user's real, logged-in browser from the shell via the reins CLI. Because it's their actual browser, every site is already authenticated — so you can scrape behind logins, read cookies/tokens/localStorage, watch and replay live API traffic, call a site's own API as the signed-in user, and click/type/screenshot. Use whenever asked to interact with, test, scrape, extract data from, or automate a live webpage or authenticated site in the user's browser.

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。