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概要と使いどころ
Expert malware analyst specializing in defensive malware research, threat intelligence, and incident response. Masters sandbox analysis, behavioral analysis, and malware family identification. Handles static/dynamic analysis, unpacking, and IOC extraction. Use PROACTIVELY for malware triage, threat hunting, incident response, or security research.
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Expert firmware analyst specializing in embedded systems, IoT security, and hardware reverse engineering. Masters firmware extraction, analysis, and vulnerability research for routers, IoT devices, automotive systems, and industrial controllers. Use PROACTIVELY for firmware security audits, IoT penetration testing, or embedded systems research.
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Korean and French screenwriting practice (韩国/法国编剧方法) — from the international masterclass; Oh Seung-uk (Christmas in August, The Shameless; field research in disguise, "write the emotion", first act and last act first, spaces as skeleton, reading to hostile listeners, surviving producers), Choi Seok-hwan (King and the Clown; genre as promise to the audience, "story = deepening crisis + intensifying desire", two reversals and bittersweet endings, the producer/director/writer trio), and Frédéric Auburtin (Paris je t'aime; dialogue last, Italian-style table reads, "12 lines not 13", dignity for characters, originals born from commissions, editing by emotion). Use when working in a Korean-style commercial drama or genre film, a French auteur/commission context, when planning field research, when a producer relationship is the problem, or when you need the Korean "three live together" development model or the French "emotion first, structure second" editing approach.
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Interactive interview that formalizes a fuzzy research idea into a structured spec (RQ, hypotheses, identification, data needs, empirical strategy). Use when user says "interview me", "help me think through this idea", "I have a half-baked idea", "formalize this into a project", "walk me through framing a study". Multi-turn Q&A; saves spec to disk. NOT for lit review (`/lit-review`) or ideation from scratch (`/research-ideation`).
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Publish finished creatives as live Meta (Facebook/Instagram) ads via the Meta Marketing API, plus research and ad-copy support. Uploads an image or video, builds a multi-variant TEXT_LIQUIDITY creative, and creates a PAUSED ad in an existing ad set. Also pulls top-performing ads (ranked by ROAS) and competitor ads from the Ad Library to inform copy. Use when the user asks to deploy / publish / launch a creative as a Meta or Facebook ad, build a Meta ad, push a video or image into an ad set, pull their top ads, or research competitor ads. Not for generating creative (use the image/video skills) and not for writing AdTable/Airtable rows (use adtable-light).
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Turn a source-bound company and market dossier into testable ideal-customer-profile hypotheses, buying roles, triggers, constraints, disqualifiers, standing, counterevidence, and research gaps. Use when a URL, company description, monitor profile, or evidence manifest needs to become defensible ICP inputs for buyer research or an AI-visibility prompt panel.
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Build a researched, fit-checked journalist list for a specific PR campaign. Prefer Medialyst's asynchronous media-list research workflow when available, with explicit approval before credit spend; otherwise produce a best-effort local list without guessed contacts.
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Design Thinking handoff knowledge for research-ready rpi-research inputs and DT-aware rpi-plan, rpi-implement, and rpi-review context
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A-share sector research: computes board-wide Top N gainers, cross-checks week/month announcement windows across two sources, and grades market-sentiment evidence (L1–L3), via parallel Agent Team execution with fresh-context adversarial verification. Use for 「XX 行业 Top N/Top 10 标的」, 「这些标的最近一周/一个月发过哪些公告」, 「判断 XX 行业/板块现在的市场情绪」, or 「用 Agent Team 做行业投研」/对抗验证行业研究结论。
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Co-creates a personal investment-research LLM Wiki (Karpathy's pattern) by interviewing the user for THEIR OWN analysis framework, not a template — a living CLAUDE.md of pure markdown + wikilinks, no RAG in place of compilation. Use to build a compounding 投研第二大脑 / 投研知识库 / 个人投研 wiki, ingest research reports / earnings calls into an existing wiki, or run post-earnings prediction→fulfillment reviews.
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Clones and audits competitor repositories into evidence-based intelligence with file:line citations. Use to track competitors, add a competitor repo, review competitor code, compare capabilities, build a competitor landscape, or check for code updates: 竞品分析 / 竞品 / competitor scan / analyze competitor / compare with X / latest competitor code. Not for market research without code (use deep-research).
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Build a candidate's research brief on a company before an application or interview. Use when asked to research a company for a job, prep a company brief before an interview, or understand a prospective employer fast. Produces a one-page brief — what they do & how they make money, recent news & trajectory, product & competitors, likely challenges, culture signals, and smart questions to ask.
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Draft a clinical trial protocol synopsis with the elements regulators and IRBs expect. Use when asked to write a clinical trial protocol, a study protocol synopsis, a trial design, or to structure endpoints/eligibility/statistics for an interventional study. Produces a structured protocol synopsis — objectives, design, population with eligibility, interventions, endpoints, statistics, and safety/ethics — for expert review. (For non-clinical/UX research, use research-protocol.)
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Build or tailor an academic CV for faculty, postdoc, research or fellowship applications: publications, grants, teaching, supervision and service in the order the field expects, with consistent citations. Use when asked to write my academic CV, update my CV for a lectureship, tenure-track or postdoc application, format my publications list, or adapt my academic CV for an industry research role. Produces the full academic CV, a field-appropriate section order, a clean publications list in one citation style, and a two-page summary version when asked.
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Use when a vulnerability or security report arrives for triage, when assessing a CVE/RCE/OGNL/injection claim against the code, or when drafting a reply to a security researcher — to research the claim from source without trusting the reporter and without fabricating your own facts.
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Fast, clean web research: search with an AI answer and cited sources. Trigger phrases: research this, web search, look up, tavily, find sources.
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Scholarly research companion using Knows sidecar spec (.knows.yaml). Generates, validates, reviews, queries, and compares structured research-paper sidecars, and fetches them from knows.academy. Use for academic literature search, survey synthesis, paper authoring assistance, and peer review with token-efficient claim/evidence/relation access.
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。
Generate a Python code skeleton from an experiment blueprint
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Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.
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Write publication-ready ML/AI/Systems papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM, OSDI, NSDI, ASPLOS, SOSP. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation verification workflows.
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Run fast, progressive experiments to map tunable parameters and their effects. Use when optimizing code, prompts, configurations, or other artifacts against a goal or benchmark, or when a long research run needs a planned hypothesis queue, focused trials, and durable findings.
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Sponsor and ad-read research on podcasts and YouTube: who sponsors a show, or which shows a brand sponsors, how often, since when. Uses arcmira.
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Researches a person across podcasts and YouTube for interview or meeting prep: where they appeared, their own words, who discusses them. Uses arcmira.
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Record a decision, document existing code, or file a supplied research material. Modes: document decision (ADR, RFC, or rule), document code (spec, doc, guide, or scenario for existing behavior), document research (only when a finished report or one external material is already in hand). Use for 'we decided', 'record this decision', 'document why we chose X', 'make it our standard', 'draft an RFC', 'should we switch to Y' proposals, 'resolve the RFC', 'we accepted the proposal', 'document the auth module', 'capture how the payment system works', reference material, how-to instructions, or a user flow with examples over an existing spec. Name the type inside the subject to skip the type question. Planning a feature or an intended user journey → /archcore:plan. Checking docs against code or docs health → /archcore:review.
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。