데이터·AI 스킬

Store conversations, agent activity, and feedback in cognee sessions, then carry them into long-term memory. This skill explains improve(), its processing stages, result statuses, and settings for diagnosing skipped work or controlling automatic runs.

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• Saving conversations and agent activity by session
• Moving session content into long-term memory
• Investigating skipped or blocked processing stages

Query documents and conversations stored in cognee to retrieve answers, supporting passages, or context for an AI model. This skill guides search method selection, source filtering, result interpretation, and troubleshooting empty or unexpected results.

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• Searching selected datasets or conversation sessions
• Retrieving supporting passages or model context
• Choosing a search method for a question

Build custom cognee processing by chaining Python tasks, defining data types, and saving results to graph and vector databases. Use it to enrich existing data, inspect run status, and troubleshoot how tasks pass data.

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• Building custom extraction and processing steps
• Saving custom data types to graph and vector databases
• Adding information to an existing graph

Tune cognee ingestion speed, query response time, and LLM costs by adjusting concurrency, text chunk sizes, request limits, and model choices. Estimate ingestion costs before processing and assess options for background work and deployment scaling.

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• Estimating costs before importing documents
• Adjusting ingestion speed and request limits
• Reducing query response time

Find and remove stored content from cognee using its SDK, HTTP API, or CLI. Inspect documents before deletion, choose document or dataset removal, or keep raw files while clearing derived graph relationships and search vectors.

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• Find and delete a specific stored document
• Remove an entire dataset
• Clear derived memory while keeping original files

Operate cognee from the terminal to store, retrieve, update, and remove information. Covers dataset and configuration management, ingestion cost estimates, session history access, and commands for inspecting or applying database migrations.

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• Storing documents and asking questions from the terminal
• Estimating token usage and cost before importing data
• Managing datasets and saved configuration

Connect cognee to additional databases and data sources through community packages. The skill covers installing and registering adapters, importing data through connectors, configuring monitoring, and developing extension packages with examples and tests.

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• Connect cognee to Qdrant or another database
• Import data from Slack or Gmail
• Configure monitoring with Keywords AI

Estimate the distribution of stockholders' purchase costs for Chinese A-share stocks using price and volume data. A triangular model produces cost concentration metrics, the estimated share below the current price, and visualization charts.

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• Examining estimated purchase cost distributions for Chinese A-share stocks
• Checking the estimated share of holdings below the current price
• Identifying price ranges with concentrated holdings

Investigate and fix agent behavior in TradingAgentsCN by checking active database prompt templates, prompt assembly code, and output processing. Apply targeted changes and verify them with focused tests, template checks, and workflow logs.

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• Investigating prompt changes that do not affect runtime behavior
• Synchronizing database templates with prompt assembly code
• Tracing incorrect dates or price references in position analysis

Design and generate agents in Nanobot by finding reusable tools, skills, prompts, and existing agent configurations. Guide requirements and confirmation, create focused agent assets, and follow recorded real-data testing and evaluation before publication.

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• Designing an agent around existing project resources
• Adapting existing agent configurations and prompts
• Identifying missing tools and calculation capabilities

Retrieve locally stored Chinese A-share prices, financial statements, and valuation context through the project's shared interfaces. Look up technical indicators, inspect available data fields, and check data dates when results disagree.

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• Checking a stock's prices and valuation context
• Retrieving financial data across reporting periods
• Looking up technical indicators and their data dates

Check how much conversation capacity context-mode has saved during the current session. View token consumption, the context savings ratio, and a breakdown by tool in full tables, with one sentence highlighting the main savings figure.

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• Checking context savings in the current session
• Reviewing token consumption
• Comparing usage statistics across tools

Route data analysis, large-file processing, and web retrieval through context-mode tools, returning only relevant results to the conversation. This helps preserve the context window, the limited amount of information an AI can work with at once.

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• Counting or filtering large datasets
• Retrieving relevant information from web pages
• Collecting and searching results from multiple commands

Turn text into embeddings, numeric representations of meaning, through 9Router. Choose a model and process individual texts or batches to prepare data for meaning-based search, similarity comparisons, or RAG systems that retrieve reference material.

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• Preparing text vectors for meaning-based search
• Generating embeddings for text similarity comparisons
• Batch-processing reference passages for RAG

Ask AI questions, generate code, and summarize text through 9Router using OpenAI or Anthropic request formats. Receive responses incrementally with streaming, and use provider combinations that automatically try another provider when needed.

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• Ask an AI model questions through 9Router
• Generate code from written instructions
• Summarize text with a selected model

Set up access to 9Router, a shared gateway for multiple AI providers, and discover available models. Find the relevant capability skills for chat, images, speech, embeddings, web search, and webpage retrieval.

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• Configure a local or remote 9Router connection
• Check available models by capability
• Find instructions for image or speech tasks

Add GBrain memory to an existing agent or connect a hosted brain while preserving current instructions and identity. Start with storage and keyword search that need no API key, then verify saving, retrieval, and recall across conversations.

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• Add memory to an existing Claude Code or Codex agent
• Connect an agent to a hosted brain
• Diagnose an incomplete installation while preserving memory

Process large email, document, and transcript archives by routing items to AI models according to their importance. Rule-based privacy checks run before model calls, while extracted information becomes linked knowledge pages with progress checkpoints.

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• Extract useful information from large email archives
• Organize decisions and advice from meeting transcripts
• Route reports to suitable models by importance

Consolidate inconsistent gbrain page types into the standard gbrain-base-v2 classification. Preview proposed changes, convert redirect pages into aliases and relationship pages into links, then verify the migration with original type information retained for rollback.

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• Consolidate inconsistent page types
• Preview a migration to gbrain-base-v2
• Convert redirect pages into aliases

Adapt a knowledge collection's schema, the rules defining page types and relationships. Review untyped pages, propose new types, update classification settings, and apply those types to existing pages after previewing the changes and checking consistency.

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• Adding page types for researchers and papers
• Classifying existing pages that have no type
• Proposing new types from stored content

When a user corrects a factual error, trace it through stored knowledge, memory, identity settings, and fact records. Fix the underlying source, check related records where needed, and report the cause, changes, and safeguards against recurrence.

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• Tracing the source of a repeated factual error
• Correcting mistaken facts about people or companies
• Updating outdated information and related records

Extract figures and fields from emails, websites, and APIs into source-linked tracking tables. Configurable recipes guide collection, preserve original material, and check duplicates for records such as investor updates, expenses, and company metrics.

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• Track financial metrics from investor update emails
• Collect expenses from receipt emails
• Maintain a table of company updates

Build a workflow for importing large collections of audio, email, documents, or chats into knowledge pages. Test varied samples first, track progress in a persistent JSON file, and verify outputs before marking work complete.

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• Importing a large email export into knowledge pages
• Converting document collections into linked pages
• Resuming imports that span multiple sessions

Audit instructions and memory files loaded on every agent turn. Identify repeated rules, contradictions, outdated content, and material better moved into skills, then receive evidence-backed recommendations ranked by estimated token savings and change risk.

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• Reduce the size of instructions loaded on every turn
• Find conflicting rules across instruction and memory files
• Identify workflow instructions to move into separate skills