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概要と使いどころ
Map dbt `schema.yml` / `properties.yml` models and sources into ktx semantic-layer overlays and column notes. Covers `sources:` vs `models:`, column `data_tests` (not_null, unique, accepted_values, relationships), and how bundle-time writes complement manifest backfill from git sync. Load when the WorkUnit's `skillNames` includes `dbt_ingest` or when raw files are dbt YAML under `models/` / `sources/`.
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 semantic-layer and knowledge updates from a live database schema snapshot.
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。
ktx's semantic layer - a structured catalog of sources (tables/views), measures, joins, and segments expressed as YAML. Covers the schema and how to query it via `sl_query`. Use when the task involves querying pre-defined metrics (ARR, churn, retention, LTV, MAU) or reading SL source YAML to understand the catalog. Capture is handled by the `sl_capture` skill (memory-agent only).
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。
Installs and configures ktx, the open-source context layer for data agents — runs ktx setup non-interactively with hidden CLI flags, configures database connections and embeddings, installs agent integration, and verifies readiness. Use when the user asks an agent to add ktx to a project, connect data sources, install agent rules, ingest schema, or troubleshoot a local ktx install.
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 one changed historic-SQL table usage bucket into typed table usage evidence for deterministic _schema projection.
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 before claiming that the package can be installed or published. Verifies structure, adapters, schemas, executable scripts, and public-safety boundaries.
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 save/load for game state — choosing what to serialize, file formats, save slots, atomic crash-safe writes, schema versioning and migration, and autosave. Engine-neutral. Use when the user mentions save system, save/load, game state persistence, save slots, autosave, save file corruption, or migrating old saves to a new version.
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。
Gate a prompt universe for schema and provenance completeness, target or campaign contamination, evidence entailment, naturalness, one-concept clarity, architecture consistency, aided status, answer leakage, and semantic duplicates. Use after realistic prompt generation and before human panel selection.
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 Thinking coaching foundation knowledge: coach identity and philosophy, quality and fidelity constraints, method sequencing, coaching state schema, and the canonical deck workflow
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 LLM-as-Judge evaluators for subjective criteria that code-based checks cannot handle. Use when a failure mode requires interpretation (tone, faithfulness, relevance, completeness). Do NOT use when the failure mode can be checked with code (regex, schema validation, execution tests); use `write-code-eval`. To validate an existing judge, use `validate-evaluator`.
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。
Plan tests for an API endpoint or service — functional, negative, and contract. Use when asked to test an API, write API test cases, plan REST/GraphQL endpoint testing, or validate an API contract. Produces an API test plan — per-endpoint cases (status codes, schema, auth, validation, errors), boundary/negative cases, contract checks, and non-functional notes — so the API is verified beyond the happy 200.
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。
Specify the tracing, metrics, and alerting for an AI agent or LLM feature in production. Use when asked what to log for an LLM app, design agent tracing or spans, define quality and cost monitors, or answer 'how do we know if the agent is misbehaving?'. Produces an observability spec with a trace schema, metric definitions with owners and alert thresholds, sampling and retention policy, and a privacy note for logged content.
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。
Search DigiKey for electronic components and download datasheets — primary source for prototype orders and the preferred API method for fetching datasheets. Find parts by keyword or MPN, check pricing/stock, download datasheets via API, analyze specifications. Sync and maintain a local datasheets directory — extract components from schematics, download missing datasheets, keep them up to date. Also supports batch MPN-list seeding (`--mpn-list`) for bulk workflows without a KiCad project. Use when the user asks about electronic components, part specs, datasheets, pricing, stock, footprints, or needs to download a datasheet — even without mentioning "DigiKey". Also for "sync datasheets", "download datasheets for my board/project", or mentions a datasheets directory. DigiKey is the default distributor for prototyping. For BOM workflows, see the bom skill.
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 specifications from electronic component datasheet PDFs — pinouts, electrical characteristics, peripherals, topology, and features. Cache extractions per project for consumption by schematic and PCB analyzers. Primary consumer infrastructure for `kicad`, `emc`, `spice`, and `thermal` analyzers. Use this skill whenever the user asks to extract, verify, or read specs from a component datasheet; when analyzers need verified IC knowledge (EN pin thresholds, PG presence, USB peripheral speed); or when a review mentions datasheet coverage, extraction quality, or per-MPN specifications. Also triggers on "extract this datasheet", "what are the specs for MPN X", "verify datasheet extraction", or "check pin functions for part Y".
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。
BOM (Bill of Materials) management for electronics projects — the workflow skill that coordinates DigiKey, Mouser, LCSC, element14, JLCPCB, PCBWay, and KiCad skills around a unified BOM lifecycle. Create, update, and maintain BOMs with part numbers, costs, quantities stored as KiCad symbol properties. ALWAYS trigger this skill for any task involving component sourcing, pricing, ordering, distributor searches, BOM export, or fabrication preparation — even if the user names a specific distributor or fab house (e.g. "search DigiKey for...", "generate JLCPCB BOM", "order from Mouser"). This skill analyzes the schematic for sourcing gaps, recommends which distributor/fab skills to call for each gap, and writes results back as symbol properties — the agent (or user) performs the actual searches via the called skills. Also trigger on phrases like "what parts do I need", "order components", "how much will this cost", "export for JLCPCB", "find parts for this board", "compare pricing", or "check stock".
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。
Designs database schemas, indexing strategies, query optimization, and migration patterns for SQL and NoSQL databases. Use when designing tables, optimizing queries, fixing N+1 problems, planning migrations, or when asked about database performance, normalization, ORMs, or data modeling.
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。
Convex backend development guidelines. Use when writing Convex functions, schemas, queries, mutations, actions, or any backend code in a Convex project. Triggers on tasks involving Convex database operations, real-time subscriptions, file storage, or serverless functions.
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。
Pre-launch and pre-commit audit for vibe coding projects. Use when asked to check whether a project is ready to ship, deploy, merge, or commit, especially for common AI-built app mistakes: broken project structure, committed secrets or cache files, environment variable hygiene, database migrations, ORM/schema drift, unsafe raw SQL, unused legacy code, dead routes/components, weak auth, missing tests, build failures, and deployment footguns.
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。
Read and write Replicate: look up model versions and input schemas, run predictions, poll status, cancel runs. Trigger phrases: replicate, ai models.
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。
Plan and implement OpenMeld PostgreSQL business persistence, schemas and migrations, transactions, Hyperdrive access, and data cutover. Use for the shared relational database transition or PostgreSQL work; not for unrelated D1 performance fixes or OpenConnector's upstream database.
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 touching retiring old logic, collapsing duplicate owners, removing fallbacks, or schema/persistence/source-of-truth boundaries; identify opportunities automatically; destructive execution requires explicit confirmation.
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。
Implement server APIs, authentication, and application data access. Schema modeling and query tuning use oma-db.
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 schemas and migrations, tune queries, or plan vector retrieval and database operations. Application API implementation uses oma-backend.
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。