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Expert data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling, and business intelligence.

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Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms.

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。

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

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使用自然语言查询金融数据，支持A股股票、基金、期货等上市品种，覆盖基本资料、财务数据、日频行情信息、持仓信息及各类分析指标等数据。也支持宏观经济数据，包括世界经济数据、全球经济数据、中国经济数据、区域经济数据、行业经济数据、利率走势数据、商品数据和特色数据等。当需要查询上述金融相关数据查询时使用此 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。

Use when the task requires reading A-share stock data, financial data, valuation context, or querying local stock collections through core.skill_runtime runtime interfaces.

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。

审计、修复并验证 TradingAgentsCN 中 agent 与提示词工作流。用于持仓分析、workflow prompt、MongoDB prompt_templates、adapter prompt assembly、合规修复、运行期模板与代码同步。触发词：修改 agent 提示词、提示词治理、DB 模板同步、workflow 提示词修复、持仓分析提示词排查、prompt compliance、runtime prompt audit。

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 building your own cognee processing — writing custom tasks, chaining them into a pipeline with run_custom_pipeline or the lightweight run_pipeline (from cognee.pipelines import run_pipeline), storing custom DataPoints with add_data_points, running custom extraction/enrichment over the existing graph with memify, checking pipeline run status, or debugging how data flows between tasks (batch_size, data_per_batch, ctx, Drop, enriches).

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 dealing with cognee database migrations — understanding when they run automatically, checking or repairing migration state with cognee-cli upgrade/downgrade/stamp/current, a write blocked by a failed migration, authoring a new Alembic (relational schema) revision or a graph/vector data migration, or moving data between systems (relational DB import, memory export/import).

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 putting data into cognee memory with remember() — choosing inputs (text, files, folders, URLs, repos, databases), datasets and node_sets, loaders, ontologies, the graph extractor (LLM or GLiNER), chunking, dry-run cost estimates, or when remember() raises on a keyword argument.

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Use when querying cognee memory with recall() (or search()) — picking a search type, understanding auto-routing, scoping to datasets, node sets or sessions, getting context or citations instead of an answer, reading the results, or debugging empty or unexpected results.

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Use when working with cognee's users, permissions, and multi-tenancy — creating users, tenants and roles, sharing datasets (read/write/delete/share grants), acting as a specific user in the SDK or authenticating over HTTP (login, API keys), turning access control on or off, debugging PermissionDeniedError or missing datasets, or understanding where permissions are enforced and how datasets are isolated.

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 removing data from cognee memory with forget() in the SDK, HTTP API, or CLI — finding which dataset and document hold the content to delete (listing datasets and data items, reading raw content), choosing between deleting one document, a whole dataset, or only the graph/vector memory, and doing it safely.

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Use this skill to build a multi-element Excel dashboard — Dashboard sheet on open, multiple formula-driven KPI cards, multiple charts, sparklines, and conditional formatting — from CSV or tabular input. Trigger on: 'dashboard', 'KPI dashboard', 'analytics dashboard', 'executive dashboard', 'metrics dashboard', 'CSV to dashboard', 'data visualization'. Output is a single .xlsx. Scene-layer on officecli-xlsx: inherits every xlsx hard rule. DO NOT invoke for: a single budget tracker / one-sheet CSV-with-formatting (use xlsx), a 3-statement / DCF / LBO financial model (use financial-model), a weekly report with at most 1 chart and fewer than 10 rows (use xlsx).

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。

Google Model Armor: Sanitize a user prompt through a Model Armor template.

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。

Add a persistent wiki knowledge base to a NanoClaw group. Based on Karpathy's LLM Wiki pattern. Triggers on "add wiki", "wiki", "knowledge base", "llm wiki", "karpathy wiki".

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Share brain pages as beautiful password-protected HTML with zero LLM calls

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。

Tiered LLM extraction pattern for large corpus processing (email archives, document dumps, transcript libraries). A utility-tier model triages and classifies at speed; the reasoning tier does the default deep read; the deep tier is the escalation for the highest-value content. Prevents spending deep-tier money on noise while ensuring the important content gets the best eyes. A deterministic privacy wall runs before any LLM call.

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。

Keep gbrain current. When a `gbrain` invocation prints an `UPGRADE_AVAILABLE <old> <new>` marker (or `gbrain self-upgrade --check-only` reports an update), apply it per the configured self_upgrade.mode: notify (prompt the operator with a 4-option question + snooze) or auto (apply silently). The action is always the hardcoded `gbrain self-upgrade` — never a command read from the marker.

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。

Structured data research: search sources, extract structured data, archive raw sources, maintain canonical tracker pages, deduplicate. Parameterized via YAML recipes for investor updates, donations, company updates, or any email-to-structured-data pipeline.

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Compress an agent's routing file (RESOLVER.md or AGENTS.md) by converting granular skill-per-row tables into functional-area dispatchers. Each area lists sub-skills in a "(dispatcher for: ...)" clause. The LLM reads one area entry and routes to the correct sub-skill. Proven via held-out A/B eval: dispatcher pattern outperforms naive pipe-table compression.

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Day-one data bootstrapping for a new brain. Sequences the highest-leverage data sources to go from empty brain to useful brain in one session. Uses ClawVisor for safe credential handling — the agent never holds raw API keys. Covers Gmail import, calendar sync, contacts seeding, X/Twitter archive, conversation imports, and file archives. Use when a user has just finished gbrain setup and asks "now what?"

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。

Confirmation gate before any bulk delete, cleanup, or destructive operation that could result in data loss — shell-level (rm -rf, git rm, bulk sed) or brain-level (bulk forget, delete sweeps, purge-deleted, source removal, raw-SQL truncation). Presents a recoverability card and requires an explicit "yes" from the user before proceeding. Routing convention, not an operation-boundary enforcement.

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When the user corrects a factual error, root-cause it immediately. Don't just note the correction — trace the error to its source, fix the source, and prevent recurrence. Every factual error is either a data error (bad brain page, bad memory file, bad rendered SOUL/USER identity, bad facts row) or a hallucination (LLM confabulated from partial signals).

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。

Systematic claim-by-claim verification for any content before it ships. Modeled on professional fact-checking desks (The New Yorker, ProPublica, IFCN standards): extract every verifiable claim, check each against live citable sources (never training data), assign a 6-level confidence status, apply corrections, and produce a scored pass/fail report. Includes a data-derived-claims gate for outputs produced FROM the brain or a database: PRODUCER ≠ VERIFIER (re-derive each claim via a different query path) and AFFILIATION ≠ AUTHORSHIP (person→thing claims resolve through typed edges), with delivery hard-blocked on unsupported claims.

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。