データ・AIのスキル

Restore IWE memory from an exocortex backup on a new device or after data loss — NL wrapper around restore-from-exocortex.sh.

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。

Write the postmortem for an incident, in its incident channel, once it is mitigated or resolved. Use on "write up this incident", "postmortem", "incident summary", "what happened here for people who weren't around", "draft the retro", or when the oncall memory says incidents of this severity get one. Reads the incident channel (and the alert thread it came from), the investigation findings and the data behind them, and writes a one-screen, zero-context write-up: what happened, impact with numbers, detection, timeline with durations, why it happened, what fixed it, follow-ups with proposed owners, and one before/during/after chart. Posted and pinned in the incident channel as its final pinned post, with edit requests updating that one pinned post in place — unless the person or the team's conventions say write-ups live elsewhere, in which case it is written there and only a link is posted, nothing pinned. A person edits and publishes it; never assigns blame.

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。

Compose polished charts (timeseries, bar, line, area, pie, scatter, or anything else the data calls for) from tabular data using the chartkit primitives, producing PNG, SVG, or self-contained interactive HTML. Use when the user asks to chart, graph, plot, or visualize data and wants something better than raw matplotlib defaults.

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Use when verifying information (fact, number, quote, event, statement) against authoritative primary sources, or cross-checking a number via one-level metric decomposition (Z = P × Q). Triggers: "verify X", "is this true", "find the original source", "where is this number from", "two sources disagree", "is it true X never did Y". Covers five scenarios: (1) basic truthfulness check, (2) completeness / out-of-context quoting, (3) one-level reasoning verification, (4) negative-statement handling, (5) multi-source conflict side-by-side output. Dig into whitelisted primary sources only (user-supplied files, official websites & databases, authoritative industry sources); cited reports / charts / datasets must be downloaded and read locally to count as verified — if download is blocked, hand the link to the user. If nothing can be found, plainly state "cannot verify" rather than guessing, patching, or citing secondary paraphrases. Always reply in the user's question language.

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。

VNX-specific domain expertise for the intelligence system, central state databases, and dispatch lifecycle. Use when working on quality_intelligence.db / runtime_coordination.db, code_snippets FTS5, snippet_metadata linkage, success_patterns / antipatterns, T0 state projection (t0_state.json), intelligence_injections lifecycle, dispatch → receipt → events → archive flow, central state architecture, project_id propagation across VNX, or any code that reads/writes VNX governance state. Triggers include: editing intelligence injection code, querying central DB for cross-project insights, debugging "intelligence not appearing in T0 state", modifying schema in `schemas/quality_intelligence.sql` or `schemas/runtime_coordination_v*.sql`, working with `scripts/quality_db_init.py` or `scripts/lib/coordination_db.py`.

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。

VNX-specific domain expertise for the intelligence system, central state databases, and dispatch lifecycle. Use when working on quality_intelligence.db / runtime_coordination.db / dispatch_tracker.db, code_snippets FTS5, snippet_metadata linkage, success_patterns / antipatterns, T0 state projection (t0_state.json), intelligence_injections lifecycle, dispatch → receipt → events → archive flow, central state architecture, project_id propagation across VNX, or any code that reads/writes VNX governance state. Triggers include: editing intelligence injection code, querying central DB for cross-project insights, debugging "intelligence not appearing in T0 state", modifying schema in `schemas/quality_intelligence.sql` or `schemas/runtime_coordination_v*.sql`, working with `scripts/quality_db_init.py` or `scripts/lib/coordination_db.py`.

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。

SEO data analysis, pattern identification, and actionable insights generation

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Read a Facebook Page and its audience from real data. Pull any Page's public stats (yours or a competitor's): followers, likes, categories, intro, websites. And pull the commenters on a public Page post, since Facebook hides the reactor and liker roster and shows counts only, so commenters are the signal. Powered by Apify, no login. Triggers on "analyze my Page", "competitor Page stats", "who is commenting", "read the comments", "audience insights". Not for writing Page posts (use fb-post-writer).

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。

Scout a topic on YouTube. Searches YouTube Data API v3 for the most relevant videos, ranks them by views (or engagement, likes, recency, momentum, breakout), and writes a research workbook in the OWT-Social-Ads layout with Videos, Channels, and Summary sheets, plus optional Comments and Transcripts (hooks, replay hotspots) sheets. Can also append into an existing sheet. Use when the user says "scout <topic>", wants a spreadsheet of top YouTube videos on a topic, wants creator or hook research, or wants YouTube rows added to their social ads sheet.

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。

Core agent-browser usage guide. Read this before running any agent-browser commands. Covers the snapshot-and-ref workflow, navigating pages, interacting with elements (click, fill, type, select), extracting text and data, taking screenshots, managing tabs, handling forms and auth, waiting for content, running multiple browser sessions in parallel, and troubleshooting common failures. Use when the user asks to interact with a website, fill a form, click something, extract data, take a screenshot, log into a site, test a web app, or automate any browser task.

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Discover MotherDuck databases, tables, columns, shares, and sample data to understand an available dataset.

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Design or implement MotherDuck analytical schemas and transformation models, including grain, types, and materialization.

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Plan or implement migrations to MotherDuck with SQL translation, source-to-target validation, cutover, and rollback.

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, consume, or manage MotherDuck data shares, including audience grants, table filters, and refresh policy.

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。

Load files, object storage, dataframes, or external databases into MotherDuck using an appropriate bulk ingestion path.

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。

Write, execute, or optimize analytical DuckDB SQL against MotherDuck data.

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, edit, publish, share, or embed MotherDuck Dives using their React and SQL runtime.

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 MotherDuck analytics into customer-facing applications with tenant isolation, backend routing, and serving APIs.

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Roll out governed MotherDuck analytics to internal teams, choosing trusted datasets, access boundaries, and owners.

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 ingestion-to-serving pipelines on MotherDuck, including stage boundaries, transformations, and publication.

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。

Look up or repair DuckDB SQL syntax and verify MotherDuck-specific command and feature support.

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。

The branded entry point to ServiceGraph — use whenever the user explicitly names **ServiceGraph** — "use ServiceGraph to…", "what datasets does ServiceGraph have", "search ServiceGraph for…", "look this up in ServiceGraph", "pull contacts from ServiceGraph for these domains", "how many credits do I have on ServiceGraph". ServiceGraph is a multi-dataset platform of metrics-enriched business data for founders — where to launch, who to email, who to hire. This skill explains how to drive the API (api.servicegraph.co / mcp.servicegraph.co) against ANY dataset — discover what datasets exist, discover a dataset's schema and filters, search free brief rows, and unlock contact + metric detail with credits. Dataset-agnostic by design — it discovers everything through the API and never assumes which datasets or fields exist. When the user describes an intent WITHOUT naming ServiceGraph (e.g. "find a PR agency in NY"), defer to the matching specific skill (find-pr-agency, find-marketing-agency, find-law-firm, …); this skill is for explicit ServiceGraph requests and for datasets no specific skill covers yet. Skip non-US firms, consumer/personal services, and individual freelancers.

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。

Use whenever the user wants to find, shortlist, vet, or enrich US B2B law firms — corporate, IP/patent, M&A and securities, employment, commercial litigation, regulatory/compliance, data privacy/cyber, real estate, and tax. Triggers on "find three boutique IP law firms in California", "shortlist M&A counsel for a Series-B fundraise", "patent prosecution for our hardware startup", or "pull contact info for these 10 law firm domains", even when described indirectly (outside counsel, cap-table review, GDPR/SOC2 oversight). Drives the ServiceGraph API (api.servicegraph.co) — a 100k+ US firm catalog filterable by industry, services, location, size, ratings. Skip personal/consumer legal services where the user is the end client (divorce, personal injury, criminal defense, family law, estate planning, wills) — the catalog is B2B-only. Also skip in-house GC hires, "is this NDA enforceable" DIY questions, non-US firms, individual freelancers.

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 foreign LLM for chat. Use this skill if a foreign LLM like OpenAI ChatGPT, Google Gemini, DeepSeek or xAI Grok should be queried with a single chat message.

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。