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Synthesises a batch of customer feedback (survey answers, reviews, ticket and chat comments) into a DRAFT themed report: coded themes with record counts and shares, anonymised verbatim quotes cited to record codes, contradictions and unverified claims, and the follow-up questions to answer next. Produces no sentiment score or percentage positive as fact; ratings appear only as distributions with n. Use when the user asks to "theme this feedback", "what are customers saying", "summarise these survey answers", "analyse these reviews", "find the top complaints in these comments" or "code this voice of the customer data". Do not use for exit interviews, use exit-interview-synthesis instead; for sorting a live queue and drafting replies, use ticket-triage-pack; for project retrospectives, use lessons-learned-synthesis. Drafts for human review; never approves, authorises or signs off.

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。

Reads one spreadsheet, CSV export or pasted table together with the user's question and drafts an insight pack: a data profile, observations cited to named columns and values and framed as leads, caveats, and suggested charts and follow-up analyses. Every figure carries the label "as read, verify in source"; correlation is never stated as causation. Use when the user asks to "profile this spreadsheet", "what does this data say", "find anomalies in this extract", "give me a first read on this CSV" or "summarise this table". Do not use for a purchase order log or spend extract, use purchase-order-anomaly-review instead; for actuals versus budget, use budget-variance-explainer instead; for a report that arrives by email on a schedule, use report-attachment-analyzer instead. Drafts for human review; never approves, authorises or signs off.

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。

Turns reported data problems (messages, tickets, meeting notes, complaints) into one DRAFT issue log: one row per issue with the symptom as reported, dataset and fields named, reproduction steps, affected reports, first seen date, reporters, a suggested owner, and blank severity, root cause and status cells for the owner. Never asserts a root cause, severity or fix. Use when the user asks to "log these data issues", "turn these complaints about the numbers into an issue list", "build the data quality backlog", "which reports are hit by this data problem" or "write up the reproduction steps for this data bug". Do not use for exploring or profiling a dataset, use dataset-insight-pack instead; for an IT incident write-up, use incident-postmortem-drafter; for tracking fix actions once assigned, use corrective-action-tracker. Drafts for human review; never approves, authorises or signs off.

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。

Turns a list of KPIs or metrics into a DRAFT definition sheet: one row per KPI with the name as stated, formula quoted from a source, source system, owner, refresh cadence, unit, filters and known caveats, with UNKNOWN in every cell no source fills and a gap list grouped by data owner. Never composes a formula, sets a target or names an owner the sources do not state. Use when the user asks to "define these KPIs", "build a metrics definition sheet", "document how each KPI is calculated", "which of our KPIs have no owner or formula" or "reconcile the two KPI lists". Do not use for the weekly numbers report, use kpi-weekly-report-writer instead; for field-level definitions of a table, use data-dictionary-builder; for logging a wrong figure as a defect, use data-quality-issue-log. Drafts for human review; never approves, authorises or signs off.

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。

Proposes redactions of personal identifiers in pasted text or an attached document (names, contact details, identification and account numbers, dates that identify, quasi-identifiers) and returns a DRAFT redacted copy with consistent placeholder tokens plus a redaction table stating what was replaced, where and why, with a confidence level and a blank reviewer decision per row. Use when the user asks to "redact the names in this document", "de-identify this transcript before we share it", "strip personal data from these notes", "anonymise this report for publication" or "what would need redacting here". Do not use for assessing a planned processing activity, use dpia-draft-pack instead; do not use for working out what data an incident exposed, use data-incident-impact-brief instead. Never declares a document anonymised, safe to release or compliant. Drafts for human review; never approves, authorises or signs off.

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。

Builds a DRAFT data dictionary from a schema export, table definition, sample rows or a partial existing dictionary: one row per field with the name as stated, type as stated or observed, meaning quoted from the documentation supplied, owner, allowed values, and UNKNOWN wherever no source defines the field, plus a gap list for the data owner. Never infers a definition from a column name. Use when the user asks to "build a data dictionary for this table", "document these columns", "what does each field mean", "turn this schema export into a dictionary" or "fill in the field definitions from these documents". Do not use for profiling values or finding patterns in the data, use dataset-insight-pack instead; for metric definitions, use kpi-definition-sheet; for logging data defects, use data-quality-issue-log. Drafts for human review; never approves, authorises or signs off.

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。

Reads a project or processing description and returns a DRAFT data protection impact assessment pack: processing description, necessity and proportionality questions, a risk table with blank rating cells, mitigations to consider and open questions. Never rates risk, determines the lawful basis or judges adequacy; the DPO assesses and signs. Works from descriptions, never personal data. Use when the user asks to "start a DPIA for the new monitoring tool", "draft a data protection impact assessment", "scaffold a DPIA from this processing description" or "what would the DPO need for a DPIA on this". Do not use for redacting or de-identifying a document, use document-deidentification-pass instead; do not use for working out what data an incident exposed, use data-incident-impact-brief instead. Drafts for human review; never approves, authorises or signs off.

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。

Extract structured data from receipts and invoices for bookkeeping, expense tracking, and tax preparation. Supports email, photos, scanned images, PDFs, and accounting exports. Outputs vendor, date, amount, tax, line items as table, CSV, or JSON. Trigger on "process receipts", "extract receipts from email", "scan invoices", "capture expenses".

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。

Convert a BMad skill into a downloadable web bundle (Gemini Gem + ChatGPT Custom GPT) with a persona, install instructions, and knowledge files. Use when a user wants to publish a skill to consumer LLM platforms (Gemini, ChatGPT).

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。

概念データモデルの新規作成・更新・レビューを支援する。業務上の概念(エンティティ)とその関連を Mermaid ER 図で表す D2 成果物で、論理 ER 図・テーブル定義(D3)の上流にあたる。PRD・ドメイン要求仕様書群の整備後、データ観点を整理する段階で呼び出す。

Diagnose or enable Session Cartographer semantic search in Codex, including least-privilege localhost access to local Qdrant and the embedding server. Use when setup, Qdrant health, localhost reachability, sandbox network denial, or semantic indexing is in question.

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。

Query PostHog product analytics via REST API — event analytics, HogQL SQL queries, feature flags, experiments, session recordings, insights, persons, cohorts. Use when the user wants to answer an analytics question, toggle/inspect a flag, or pull event data programmatically without opening the PostHog UI or installing the PostHog MCP.

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。

Query Sentry issues, events, releases, replays, and org/project metadata via REST API. Use when the user wants to diagnose a production error, pull stack traces, list recent issues, inspect a release, or fetch event data programmatically — prefer this over asking for UI screenshots or guessing from memory.

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。

Query and mutate Linear issues, projects, cycles, teams, workflow states, and comments via its GraphQL API. Use when the user wants to read or change Linear data programmatically — without the MCP server, and without hand-navigating the Linear UI.

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。

Operate Stripe's REST API — customers, payments (PaymentIntents / Charges), refunds, subscriptions, invoices, Checkout Sessions, products/prices, payouts, disputes. Use when the user wants to query or mutate Stripe data programmatically without the MCP server or the Dashboard.

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。

Prepare and launch LoRA, LoKr, or T-LoRA training through lora-scripts-next (SD-Trainer), Anima-first. Use when the user asks to train a character, style, or concept LoRA, choose parameters or repeats and epochs, start a run, or monitor training on Anima, SD1.5, SDXL, or Flux. Organize and caption the dataset, run dataset-doctor, choose a documented starting preset from image count and VRAM, show one confirmation card, then call the mikazuki API and monitor its log.

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。

Build an Anima LoRA from a single-character or style name, from Danbooru collection through curation, section-ordered WD14 captions, dataset checks, confirmed training, fixed-seed checkpoint validation, and a staged Civitai draft. Use for requests such as make a LoRA of X, collect images and train a character LoRA, train and upload to Civitai, or build a dataset from scratch and publish. Delegate training to lora-trainer and structural checks to dataset-doctor; never train without confirmation and never auto-publish.

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 and repair a kohya-style LoRA dataset before training. Use when the user asks to check, validate, inspect, fix, clean, 体检, or 修 a dataset, captions, or tags. Inspect image count, repeats and step budget, resolution and aspect buckets, duplicates, corrupt or non-RGB images, missing or empty captions, trigger consistency, and tag-frequency hygiene. Return a structural PASS, WARN, or FAIL report and apply only confirmed fix_dataset.py actions, dry-run first with displaced originals quarantined.

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。

モデルに送るプロンプト面（system prompt / tool 記述・スキーマ / 注入ブロック / 副プロンプト）を 整理・変更するときに、前後で同じ物差しで計測してから完了とする手順。「プロンプト整理して」 「プロンプト削って」「system prompt の重さを見て」といった依頼で使う。ユーザーが /prompt-budget と入力したら使う。

ScalarDB Analytics設計エージェント - ScalarDB Analyticsを使用した分析基盤の設計。Apache Sparkベースの分析クエリ、マルチDB横断分析を策定。/design-scalardb-analytics [対象パス] で呼び出し。

Use when explaining or writing TBQ3 language syntax, including Params, Vars, Events, data types, variables, sequence variables, arrays, Map variables, control flow, operators, assignment, functions, and formula execution rules.

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。

Refresh WhatsApp text into an Obsidian vault (data layer, dashboard, group labels, contact notes). Text only, never downloads media, and pulls the recent window the live companion bridge exposes rather than a full multi-year archive. Semi-automatic by design: it drives the live bridge and needs the phone nearby. Triggers: "/whatsapp-sync", "pull whatsapp".

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。

ПОДКЛЮЧИТЬ Telegram на ЭТОЙ машине — оба стека сразу: Telethon-рельса (роботы, 0 LLM) и MCP (живые сессии). Триггеры «/tg-connect», «/tg-login», «подключи телегу», «подключи телеграм на этой машине», «залогинь телегу», «телега отвалилась», «AuthKeyDuplicated», «перелогинь аккаунт <имя>», «настрой telethon и mcp», «connect telegram», «relogin telegram»

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。

- Research companies, directors, shareholders and ultimate beneficial ownership in official corporate registries, filings and offshore datasets — OpenCorporates, UK Companies House and the PSC register, SEC EDGAR, US Secretary of State registries, EU business registers, GLEIF LEI… Use when asked who owns or controls a company, to find a person's other directorships, or to unpick a group structure

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。