データ・AIのスキル

Search DigiKey by keyword or part number, check stock and price breaks, retrieve component specifications, and download or synchronize datasheets for an electronics project. Use when the user explicitly wants DigiKey data, a DigiKey prototype-order comparison, or direct datasheet synchronization. For distributor-neutral sourcing, multi-vendor price comparison, or order-file generation, use the bom skill instead.

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 a task iteratively over a user-specified duration by dispatching subagents. The orchestrator stays strictly linear and time-checked, but each iteration fans out MULTIPLE subagents in parallel over independent units (and those subagents may fan out further). Shared state — a compact progress digest and a growing environment cheatsheet — lives on disk and is passed to every subagent BY REFERENCE, killing the per-subagent rediscovery tax. Every subagent first reads an initialiser preamble that points it at that shared state and makes it write findings back, so the cheatsheet populates itself; role prompts are produced ONCE on the filesystem by a single scaffold command and dispatched by path, never re-typed per dispatch. Subagents persist their results to disk and return only a tiny status, so the orchestrator's context stays small and lasts. The orchestrator classifies the goal as FINITE or OPEN-ENDED: finite lists may finish early; open-ended goals use the full duration, never idle, never manufacture busywork. Use when the user gives a task and a duration and wants it ground out iteratively by subagents over that time.

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 custom modules for [Harbor Boost](https://github.com/av/harbor/tree/main/boost), an optimizing LLM proxy. Use when building Python modules that intercept/transform LLM chat completions—reasoning chains, prompt injection, structured outputs, artifacts, or custom workflows. Triggers on requests to create Boost modules, extend LLM behavior via proxy, or implement chat completion middleware.

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 when the user wants to systematically fix AI code slop — duplicated logic, over-engineering, silent error swallowing, convention drift, cargo-cult patterns, and other LLM-introduced architectural decay — over a specified duration

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。

Performs static analysis of binary code using radare2 and Ghidra — function enumeration, cross-reference tracing, decompilation, and control flow graphs without executing the binary. Use when disassembling or decompiling code, mapping functions, tracing data flow, or building hypotheses before dynamic verification.

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。

Stand up reporting for a new audience — an external client, a business unit, a region, an executive team — end to end: what they need to see, whether the data exists, access and scoping, their place in the pipeline, the standard report set, and validation against numbers they can check themselves. Use when a new audience is onboarded, when an existing one adds a site, a region or a business unit, when their reporting is rebuilt after a change on their side, or when they say the numbers do not match their own records.

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。

Fix data that has gone wrong and change shape without breaking what reads it: duplicates, nulls, inconsistent document shapes, a field that means two things, a column that has to change. Use when a report cannot be built because the data is not in the right shape, when duplicates, nulls or inconsistent shapes are producing wrong numbers, when a field is needed or has to change, before a migration, or when onboarding data from a new source that does not match the standard shape.

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。

Check that the scheduled data work behind the reports actually ran and produced something sensible: the daily reshaping scripts, the API pulls, the tables the reports read from. Use each morning before anyone opens a report, when a report looks wrong and nobody knows whether it is the data or the query, after a schema or API change, when a new audience or source is added to the pipeline, or when a scheduled job failed or finished suspiciously fast.

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。

Turn a request for a report or a dashboard widget into something buildable: the question it answers, the metric and its definition, where the data comes from, the query shape, and what it will not answer. Use when a new report or dashboard is requested, when an existing one needs a metric added or changed, when a widget is asked for in a sentence and needs pinning down, or when two reports disagree about the same number.

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 SQL or MongoDB query that answers a question and keeps answering it: the right grain, no fan out, parameterised window, checked against something known. Use when a number is needed that no report carries, when an existing query returns something that looks wrong, when a query is slow or expensive, or when somebody asks for a one off pull.

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 any pile of documents (PDF, Word, HTML pages, wiki exports, PowerPoint slides) into clean, LLM-ready markdown context: inventories the sources, picks the right extractor per file type (pdftotext or PyMuPDF for PDF, pandoc or mammoth for docx, readability extraction for HTML.

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 a production-ready data API from scratch: scaffold REST endpoints with models and validation, generate integration tests that verify every route, then load test for scalability. Use when you need an API backend, data service, CRUD layer, or microservice with verified correctness and performance.

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 game analytics and telemetry implementation -- event tracking completeness, FTUE and monetization funnel coverage, retention metric infrastructure, A/B testing framework, heatmap data collection, churn prediction signals, and LTV modeling 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。

Audit research data management infrastructure -- score FAIR principles compliance (Findable, Accessible, Interoperable, Reusable) across all sub-principles, evaluate data governance frameworks and lifecycle management, assess metadata schema quality (Dublin Core, DataCite, DDI.

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 program impact measurement software for logic model completeness, indicator tracking rigor, data collection methodology, causal attribution modeling, cost-effectiveness analysis, beneficiary feedback integration, and funder reporting accuracy..

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。

Pull and analyze Google Search Console data via the GSC API. Produces: weekly query × page performance with WoW/MoM/YoY deltas, top movers report (winners + losers ranked by absolute click change), click-loss attribution (lost rank? Triggers: "GSC", "Google Search Console".

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 mobile app analytics implementation including event tracking completeness and naming conventions, SDK configuration audit (Firebase Analytics, Amplitude, Mixpanel, PostHog), attribution tracking with AppsFlyer/Adjust/Branch and SKAdNetwork conversion values.

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 retail merchandising systems including planogram optimization (space-to-sales alignment, fair share index, sales per linear foot), visual merchandising effectiveness for in-store displays and e-commerce product pages, market basket analysis with association rule mining (Apriori.

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 the internal link graph for a site, run PageRank-style authority distribution, detect orphan pages, and recommend new internal links via embedding-based semantic similarity (not keyword matching)..

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 production-grade OEE (Overall Equipment Effectiveness) measurement pipeline for a manufacturing line — data ingestion from MQTT/OPC-UA/Modbus/CSV, A×P×Q calculation engine, Six Big Losses categorizer, MTBF/MTTR/TEEP metrics, Grafana or Streamlit 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。

Evaluate elder care fall risk prediction systems including wearable sensor integration, gait and balance analytics, risk scoring algorithms, environmental hazard detection, medication interaction flagging, mobility trend tracking, and alert/response workflow effectiveness.

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 disaster prediction and early warning systems — model accuracy for flood, earthquake, wildfire, hurricane, and tsunami hazards, data pipeline reliability from sensor networks and satellite feeds, alert distribution latency and channel coverage.

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。

Review clinical data models and APIs for HL7 FHIR conformance, terminology standards, interoperability, and clinical workflow correctness. Triggers: 'check FHIR compliance', 'review clinical data model', 'audit HL7 conformance', 'validate medical terminology codes'.

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。

Analyzes apparel demand prediction systems for trend forecasting, size curve optimization, color and style analytics, sell-through rate tracking, and markdown optimization following CPFR collaborative planning and GTIN product identification standards..

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。