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概要と使いどころ
Erstellt aus Geschäftsmodell, Altbedingungen, Preislisten und Beschwerden vollständige passende AGB samt Einführungstexten. Steuert zehn Fachskills gezielt, fragt entscheidende Lücken nach und prüft aktuelle Normen und Rechtsprechung.
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。
Prüft, ob AGB-Änderungen durch ausdrückliche Zustimmung, Schweigen oder Weiternutzung wirksam vereinbart werden können. Für Änderungsmitteilungen einschließlich Bankbedingungen; liefert Zustimmungsweg, Beweisplan und konkrete Fassung.
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Bestimmt nach Nichteinbeziehung oder Unwirksamkeit einer AGB-Klausel den verbleibenden Vertrag. Prüft Teilbarkeit, salvatorische Regelung und Lückenfüllung; ersetzt weder Anspruchsberechnung noch Rückabwicklung oder Prozessführung.
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。
Prüft und formuliert die zusammenhängende Haftungsregel in AGB: geschützte Schäden, Verschulden, wesentliche Pflichten, Folgeschäden und Haftungshöchstbetrag. Liefert eine abgestimmte Fassung statt isolierter Ausnahme- oder Deckelbausteine.
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。
Scaffold and run a reproducible Monte Carlo simulation study in R — a declared assumption regime, a parameterized DGP, an estimator grid, a seeded replication loop, and a summary of bias, RMSE, empirical SE, coverage, size/power with Monte Carlo standard errors. Use when the user says "run a Monte Carlo simulation", "simulation study", "check the bias/coverage of an estimator", "compare estimators in simulation", "size and power simulation", "Monte Carlo experiment", or wants to demonstrate an estimator's finite-sample properties. Produces a numbered R script in `scripts/R/` and saves per-replication raw results + a summary table to `output/`.
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。
Stress-test a finding against the choices you did not make. Enumerates the discrete forks a competent analyst could have taken (measure definition, sample filter, control set, clustering level, weighting, functional form), runs the specification grid, and reports the distribution rather than a point estimate — then attacks the identifying assumption with named, computable sensitivity statistics. Use when the user says "is this robust", "challenge this result", "specification curve", "multiverse", "how sensitive is this", "what if I'd used a different measure", "stress-test my estimate", or before a result becomes a headline claim. NOT a reviewer of prose or code — it challenges the CLAIM.
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。
Draft a structured preregistration document (OSF, AsPredicted, or AEA RCT Registry style) from a research spec or free-form study description. Output is a Markdown file with hypotheses, design, sampling plan, analysis plan, exclusions, and inference criteria — annotated with MUST / SHOULD / MAY clarity flags. Use when user says "preregister", "draft a preregistration", "OSF preregistration", "AsPredicted", "AEA RCT registry", "PAP", "preanalysis plan", or before launching an experiment / data collection / analysis on data the analyst has not yet seen. NOT a registry submission tool — produces a document the user uploads to OSF / AsPredicted / AEA themselves.
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。
Compare two implementations of the same thing — a port (R↔Python↔Stata), a reimplementation, a replication package, a refactor, or a new version against the old — so that agreement means something. Freeze inputs first, inventory every expected output, test the comparator itself, compare every channel (not just the headline number), and give each divergence a stable ID and a smallest witness. Use for cross-language parity, replication, upgrade/regression gates, or whenever "the numbers match" is about to license a claim.
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。
Analyze, recreate, align, and verify reference-video motion. Use when the user asks to recreate, copy, match, align, pixel-align, or compare a video; when a HyperFrames or Remotion remake is judged not aligned; when fixed reference playback and reusable motion components must be separated; or when delivery needs full-frame runtime, render, decode, boundary, timing, and archive evidence instead of sampled screenshots alone.
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。
Capture a user's real writing voice from 5-20 prior samples, store a local voice.yaml fingerprint, and enforce it on newsjack drafts so AI tells disappear. Measures voice with named stylometry lenses (Burrows's Delta function-word vector, MATTR lexical diversity, sentence-length burstiness, Biber Dimension-1 register, opener-POS profile, punctuation rates) and gates drafts against the fingerprint as bands, not vibes.
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。
Recover the first public timestamp and canonical major coverage for a newsjacking signal, then decide whether newer coverage is the same story, a different story, or a materially new development.
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。
Consolidate freshness-gated newsjack signals and route them by client standing before angle generation. Collapses any remaining same-story duplicates, decides strong/partial/none standing with a journalist-shape sanity check, and sorts each story into pitch_ready, big_story (always-surfaced suggestion), or watch. Never writes angles or pitches, and never drops a fresh big story.
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。
Design evaluation datasets and supporting documentation for AI systems and agents, covering the scoping interview, difficulty distribution, dataset contract, sample review, and metric and tooling selection. Use when building or reviewing an evaluation set for a conversational agent, assistant, or retrieval-grounded AI system.
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 diverse synthetic test inputs for LLM pipeline evaluation using dimension-based tuple generation. Use when bootstrapping an eval dataset, when real user data is sparse, or when stress-testing specific failure hypotheses. Do NOT use when you already have 100+ representative real traces (use stratified sampling instead), or when the task is collecting production logs.
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。
Run error analysis on a dataset. Build a review UI, select diverse samples, monitor annotations, and organize failure modes.
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。
Consolidates redundant documentation while preserving all valuable content; keeps a doc set truthful after things change. Modes: (1) POST-CHANGE GOVERNANCE — when code, scripts, config, env vars, ports, paths, deployment, auth, tests, or a documented procedure changed; also "which docs are now wrong", "check docs for inconsistencies", "过时的命令/路径", "文档同步". Use it even when the user mentions only the change and never the docs — updating them is part of the change, not optional cleanup. (2) CONSOLIDATION — "clean up docs", "documentation bloat", "reduce documentation sprawl", "consolidate documentation", "too many doc files", "merge redundant docs", "merge these docs", "文档太多/太乱", multiple files covering the same topic, or documentation exceeding 500 lines across multiple files on similar topics. Finds stale copies of a fact changed in only one place, values that should never have been persisted (counts, totals, "N items", restated summaries), rival definitions that drift apart, and orphaned cross-references.
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。
Two pipelines ending at a scanner-look PDF. (1) Phone photos of paper documents (contracts, stamped certificates, receipts, forms, handwritten notes) → clean scanner-quality PDF: perspective rectification + noteshrink whitening + A4 assembly + mandatory whole-document check. Trigger: "把照片 做成扫描件", "photos to scanned PDF", "make this look scanned", "手机拍的 文档转 PDF", "盖章文件扫描", replacing pages in an existing scanned PDF, any CamScanner-like request. (2) A digital document with no signature yet (rendered docx/PDF, confirmation form, contract draft) → make it look hand-signed and scanned: synthesize a handwriting-style signature, composite it onto the signature line, apply the same scan-look post-processing. Trigger: "帮我做个手写签名", "生成签名盖到这份文件上", "做成签过字的扫描件", "synthesize a signature", any request for a document that needs to look signed without a real photographed signature. Do NOT hand-roll levels/contrast enhancement for scan-look — tried and rejected twice; this skill's pipeline is the proven one.
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。
Migrates Claude Code personal memory (the per-project memory/ directory) into tool-agnostic reference docs so other AI CLIs that auto-load AGENTS.md (Codex primarily) can read the same user profile and preferences. Use when the user says "migrate my memory", "my memory is locked to Claude Code", "make Codex/Cursor read my profile", or reports memory has grown bloated with content that should be shared across tools. Also use it to retire a project's auto memory entirely (turn it off, move everything into documents, archive the directory).
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。
Bridges Claude Code and Codex sessions when native tools can't reach the target. Use for 跨产品通信 / 脚本回帖 / 送达排查: cross-product messages, hooks or scripts posting to a session, verifying delivery (Held messages included), shared-work ownership, or an inbound peer assertion. Not for same-product or parent/subagent messaging (use native tools), spawning agents, moving full history, or cross-machine pairing (deprecated).
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。
Audit a landing page or funnel step and produce a prioritised CRO test plan. Use when asked to improve conversion rate, audit a landing/signup/checkout page, reduce funnel drop-off, or plan A/B tests for a page. Produces a CRO plan — a heuristic conversion audit, the diagnosed friction, prioritised test hypotheses (ICE), test designs with sample-size math, and the measurement guardrails.
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。
Compress tool outputs, logs, and JSON before they enter the context window — structural compression via a deterministic stdlib script (schema + samples + stats instead of 300 raw rows), no API, no summarization loss. Use when asked shrink this tool output, my context is full of JSON, compress these logs before analysis, or stop wasting tokens on raw data. Produces the crushed artifact with its token math shown, the crush-or-keep decision rules, and the fetch-the-original escape hatch.
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。
Treat context switches as the budget line they are — the switch census (how fragmented the week really is), the batching moves that consolidate scattered same-kind work, the calendar defrag that turns Swiss cheese into slabs, and the switch-cost line for saying no. Use when asked my day is fragmented to death, count my context switches, batch my meetings and reviews, or defend against calendar Swiss cheese. Produces the fragmentation census, the batching plan, the defrag moves, and the protective phrases.
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。
Organize the people and information involved in caring for someone — family, doctors, helpers — so care doesn't fall through the cracks or all land on one person. Use when asked help me coordinate care for, organize caregiving for my parent, set up a care system, or I'm drowning coordinating everyone. Produces a map of who does what (medical, daily, financial, emotional), a shared-information system so everyone's on the same page, a way to divide tasks fairly among family, a communication rhythm, and how to keep the key information (meds, contacts, wishes) in one accessible place — turning caregiving chaos into a coordinated effort.
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。
Hire an AI agent the way you'd hire an employee — a role spec with success criteria, a structured work-sample interview run on your real tasks, reference checks (what do actual users report), probation KPIs, and termination criteria written before day one. Use when choosing between AI agents/tools/copilots for a job, formalizing an AI pilot, or 'which agent should we use for X'. Produces the role spec, interview pack with scoring rubric, a decision record, and a probation plan.
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。