데이터·AI 스킬

Use when a human-curated dbt model index must guide BigQuery SQL for a warehouse question. Not for discovering undocumented models or executing warehouse changes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when the user asks "where to help", "contribution opportunities", or "find a good first issue". Returns data-backed first steps. Not for PR review queues: use gh-review-requests.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when bloated code needs clean re-derivation, or the user says "this module is bloated" or "break it and rebuild". Not for untracked data or changes without VCS rollback.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when asked to remove old code, migrate consumers, or decide whether to maintain or sunset a system. Not for untracked data or changes without VCS rollback.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Upgrade applications from Laravel Feeds 1.x to 2.x while preserving registrations, output contracts, and rollback data. Use for runtime compatibility, slug migration, ID-based generation calls, and changed generated registration titles.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create and register Laravel Feeds exports with lazy Eloquent queries, explicit item fields, item date formats, and application-specific registration models. Use when adding a first feed or changing its data source and registration schema.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Optimize a pydantic-ai agent's instructions, tool descriptions, output type, and signature inputs using the gepa CLI. Trigger when the user asks to "optimize this agent", "improve tool descriptions", iterate on prompts driven by eval failures, or otherwise improve a pydantic-ai agent against a dataset. Operates in the user's repo with full filesystem access — YOU are the reflection model; the gepa CLI handles minibatches, evaluation, and Pareto bookkeeping.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Troubleshoot and resolve common issues with the ClickHouse Node.js client (@clickhouse/client). Use this skill whenever a user reports errors, unexpected behavior, or configuration questions involving the Node.js client specifically - including socket hang-up errors, Keep-Alive problems, stream handling issues, data type mismatches, read-only user restrictions, proxy/TLS setup problems, or long-running query timeouts. Trigger even when the user hasn't precisely named the issue; vague symptoms like "my inserts keep failing" or "connection drops randomly" in a Node.js context are strong signals to use this skill. Do NOT use for browser/Web client issues.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyze queried data for trends, week-over-week comparisons, distributions, funnels, cohorts, top-N lists, anomalies, sanity checks, and report-ready findings. Use after or alongside ClickHouse queries when the user wants insight rather than raw rows.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Save, organize, and describe reusable analysis artifacts such as SQL, result snapshots, CSV exports, summaries, caveats, plots, and report-ready files. Use when users ask to save, export, share, cite, reproduce, or organize data-analysis outputs.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create standardized charts and visual assets from analytics query results. Prefer dependency-free plain HTML/CSS/JavaScript/SVG for interactive exploratory charts; use local plotting tools such as matplotlib, seaborn, or Plotly when static/report-ready exports or specialized charting libraries are more appropriate. Use for trends, comparisons, distributions, report assets, CSV-to-chart work, and presentation-ready data visuals.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Read project data documentation (data dictionaries, dbt manifests, model docs, column descriptions, lineage, metric definitions) before writing analytics SQL. Use for mapping business and product terms to concrete models and columns.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Act as an interactive data analyst for ClickHouse-backed analytics. Use when the user asks questions about internal data, metrics, dashboards, telemetry, active users, revenue, funnels, trends, distributions, or wants an analyst-style conversation, ad hoc SQL, charts, or a data export against ClickHouse (local or ClickHouse Cloud).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when the user wants to run SQL - especially analytical SQL - on local files (parquet/csv/json), URLs, S3 paths, or remote databases (Postgres, MySQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake) without setting up a server. Provides chDB - embedded ClickHouse SQL in Python with 1000+ functions, Session for stateful multi-step pipelines, parametrized queries, and cross-source joins via `s3()`, `mysql()`, `postgresql()`, `iceberg()`, `deltaLake()`, `remoteSecure()` table functions. TRIGGER when: user wants SQL on parquet/csv/files or across remote analytical sources; uses ClickHouse SQL features (window functions, windowFunnel, geoToH3, JSON path ops, Session, parametrized queries); imports `chdb` or calls `chdb.query()`. SKIP this skill for pandas-style DataFrame method-chaining (use chdb-datastore instead) or ClickHouse server administration.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

End-to-end system for creating supervised fine-tuning datasets from books and training style-transfer models. Covers text extraction, intelligent segmentation, synthetic instruction generation, Tinker-compatible output, LoRA training, and validation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Data migration and synchronization to SurrealDB from MongoDB, PostgreSQL, MySQL, Neo4j, Kafka, and JSONL. Full and incremental CDC sync. Part of the surreal-skills collection.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Recover from stale RFDB nodes that survive `commit_batch` cleanup despite having their `file` field listed in `changed_files`. Use when: (1) re-running an enricher/orchestrator step but old data with the same file field stays in the graph, (2) `commit_batch(changed_files=[X], ...)` reports success but a query later still returns nodes with `file = X`, (3) you re-pointed structural nodes (e.g., DIRECTORY/FILE) from a synthetic file path to real file paths and the old synthetic-path nodes won't go away, (4) graph-stream / find_by_type returns a mix of "stale" and "new" nodes for the same logical entity. Root cause: `handle_commit_batch` calls `engine.find_by_attr({file: X})` to enumerate nodes-to-delete, but that query path returns only a PARTIAL set after compaction (L1 segments and the file→nodes index can desync). Workaround: enumerate stale nodes via the TS rfdb client `queryNodes({file: X})` (different code path that finds all of them) and call `deleteNode(id)` per node, then re-commit the intended state. Related: rfdb-manifest-l1-carryforward (same L1 family).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Diagnose and fix RFDB data disappearing after compaction, where rfdb-server reports a tiny node count (e.g. "15 nodes") on startup despite manifest_index.json showing hundreds of thousands of nodes and segment files existing on disk. Use when: (1) rfdb-server logs "Default database: N nodes" with N orders of magnitude smaller than recent analysis output, (2) /api/stats returns only the most recent commit's nodes, (3) the issue appears AFTER a compaction event (manifest with l1_node_segments populated and node_segments: []), (4) subsequent commits dropped L1 references. Root cause: ManifestStore::create_manifest() initializes l1_node_segments/l1_edge_segments to Vec::new() instead of cloning from current manifest, so any commit AFTER compaction silently orphans all L1 data even though segment .seg files still exist on disk. Compaction injects L1 fields explicitly after create_manifest, but regular commits do not.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Verify which process actually owns a TCP port before assuming your fresh restart succeeded. Use when: (1) you killed an old server and started a new one but behaviour matches the OLD code/data, (2) `pkill -f pattern` returned 0 but old behaviour persists, (3) you embedded a new asset/binary but the served version is stale, (4) any "I just restarted X but I'm getting old results" situation. `pkill -f` matches against the process command line — if the live old process was started with `--port 0` (auto-assigned) and got the port you now want, the pattern won't match it. Always verify with `lsof -i :PORT` before debugging deeper.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Decide what to PERSIST in your data structures (graph nodes, DB tables, cache layouts) based on the QUERIES you must answer, not the DEFINITIONS that justify the data. Definitions tell you logical identity; storage strategy is independent. Materializing every definitional component leads to O(N×M) blow-ups that masquerade as memory leaks. Use when: (1) designing a new graph-node type, edge type, DB table, or cache layout; (2) about to name a `subgraph` / `comprises` / `members` / `contains` collection on a parent entity; (3) memory growth proportional to (entities × per-entity-elements); (4) "memory leak" debugging that survives multiple targeted patches — the leak may be storage shape, not retention; (5) pre-implementation sketch includes "for each X, store all reached nodes"; (6) research/spec doc says "X = ⟨A, B, C⟩" and you're tempted to make it three storage edges per X.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Pattern for adding derived edge types in the Grafema Rust orchestrator (main.rs). Use when: (1) need to create new graph edges derived from existing edges (e.g., MODULE-level DEPENDS_ON from IMPORTS_FROM), (2) Datalog joins on large edge sets time out, (3) adding enrichment steps that combine data from multiple resolver outputs, (4) need to understand how resolver outputs flow through the orchestrator pipeline.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Compress any conversation into a portable JSON context graph for handoff to a new chat or LLM. Trigger on: "compress this chat", "save context", "new chat", "hand off", "transfer context", "too many tokens", "start fresh", "export conversation", "context graph", "continue in new session". Also trigger proactively when the user mentions starting over in a long session.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Turn any installed skill (gstack, custom, third-party) into an ATO agent the user can summon into war-rooms. Reads a SKILL.md, extracts the persona, strips runtime boilerplate, and writes an agent file at `.claude/agents/<slug>.md` (project-scoped) or `~/.claude/agents/<slug>.md` (global). Prompts for a model roster (primary + 1-2 alts) so cross-family dispatch in war-rooms produces real disagreement. Companion to `ato-warroom` — that skill summons agents this skill creates. Use when asked "turn this skill into an agent", "register X as a war-room agent", or when scoping a new persona before a war-room runs.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Pre-landing PR review. Analyzes diff against the base branch for SQL safety, LLM trust boundary violations, conditional side effects, and other structural issues. Use when asked to "review this PR", "code review", "pre-landing review", or "check my diff".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.