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Coordinate multiple AI agents by adjusting their communication structure and task assignments as workloads change. The skill describes performance monitoring, switching coordination patterns, forecasting agent capacity, and validating changes with rollback when performance worsens.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing a coordination structure for multiple agents
• Redistributing tasks when agent workloads become uneven
• Forecasting how many agents a workload needs

Analyze networks of people, web pages, or systems using PageRank, a method that scores importance from connections. Identify influential nodes and communities, generate personalized recommendations, and examine communication bottlenecks to guide network improvements.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding influential users and communities on social networks
• Analyzing links and page importance on a website
• Generating recommendations based on user preferences

Preserve project decisions and work progress across sessions, and share information between agents. Organize stored knowledge into separate spaces, retrieve relevant records, and manage expiration, compression, and cleanup of saved data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Resume work from a previous session
• Look up earlier architecture decisions
• Share task progress between agents

Coordinate decisions across multiple AI agents by synchronizing shared memory, combining knowledge, and weighing input by expertise. The skill also guides workload distribution, conflict handling, and recording decisions with their rationale.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Combine agent opinions into a shared decision
• Synchronize knowledge across agents
• Redistribute work when an agent is overloaded

Guide GAIA benchmark runs from environment checks and cost estimates to result comparison. Create a signed submission package compatible with HAL, including agent traces, metadata, and an audit report that checks for benchmark exploitation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check readiness for a GAIA benchmark run
• Estimate costs and run an evaluation
• Package existing results for submission

Import Claude Code auto-memory into AgentDB and search past notes by meaning across sessions and projects. The skill checks import status, consolidates near-duplicate entries, and returns search results with their memory sources identified.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Search past notes across projects
• Import all project memories for the first time
• Consolidate near-duplicate memories

Explain a trading model's prediction by ranking the input features that contributed to its signal. Returns a Markdown table and saves an attribution record with calculation settings, adding a digital signature when a signing key is available.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing which inputs drove a trading signal
• Saving signed records of model explanations
• Reproducing rankings with the same signal and seed

Convert chat messages into template strings for local model runtimes such as Ollama or llama.cpp. Optionally route queries among a few registered intents using embeddings, numerical representations of meaning, without calling a model.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing prompt strings for Ollama
• Formatting conversations for a local model
• Routing queries among a few intents

Store useful patterns and solutions in AgentDB, then find them through semantic search, which matches meaning rather than exact wording. Use saved knowledge before starting work and record successful approaches and new lessons afterward.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Saving successful approaches for reuse
• Finding solutions from past work
• Searching stored knowledge by meaning

Classify market conditions as bullish, bearish, ranging, or volatile to guide strategy selection and historical testing. Distinguish synthetic demonstrations from analysis of actual price data, with confidence levels and historical comparisons where available.

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• Reviewing market conditions for a stock symbol
• Choosing a strategy suited to market conditions
• Checking market conditions before a backtest

Compare models available through the HuggingFace router with chat-ui configuration, research missing models, and write short English descriptions of their architecture and capabilities. Add entries to both production and development configuration files and commit the changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding newly available router models to chat-ui
• Writing concise descriptions of model capabilities
• Updating production and development model entries together

Guide market prediction workflows with LSTM, Transformer, and N-BEATS models using neural-trader commands and result storage. It explicitly identifies version 2.8.11 outputs as placeholders, so they cannot be treated as fitted models or evidence of prediction accuracy.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing market prediction model commands
• Checking whether training outputs are placeholders
• Organizing model run records

Test trading strategies against historical market data and collect performance metrics. The skill checks whether data is real or synthetic and can digitally sign results to help detect tampering before a strategy moves into live trading.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Evaluate a strategy against historical market data
• Compare returns, risk measures, and trading results
• Prepare signed backtest results for live-trading review

Manage stored agent memories for backups, project transfers, and team knowledge sharing. Work with RVF, a portable memory format, to export and import records, upgrade formats, inspect storage statistics, and transfer patterns between local projects.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Back up stored agent memories
• Transfer memories and patterns between projects
• Share stored knowledge with a team

Generate RuVLLM settings for local language model inference and create MicroLoRA or SONA instances. Adjust their small internal weights using quality scores as feedback, with instances held in server memory for subsequent adaptation calls.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Configure local language model inference
• Prepare inference tools for an air-gapped environment
• Adjust MicroLoRA using quality feedback

Compare ruflo and HAL systems for running the GAIA benchmark, an AI evaluation. Examine differences in code execution, browsing, file handling, memory, and model selection to prioritize improvements by expected impact and effort.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan the next round of GAIA improvements
• Compare code execution and browser capabilities
• Weigh improvement priorities against implementation effort

Convert text, code, or documents into 384-dimensional vectors, numerical representations used for meaning-based search, similarity comparison, and clustering. Save vectors to files, check their dimensions and magnitude, and optionally record metadata in AgentDB.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing document vectors for meaning-based search
• Converting text or code for similarity comparison
• Preparing vectors for clustering related documents

Represent dependency trees, taxonomies, and other hierarchies inside a Poincare ball, a geometric model for hierarchical distances. Use tree depth for radius and text embeddings for direction, then measure distances to examine the encoded structure.

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• Analyze subtrees in module dependencies
• Examine structural patterns in class hierarchies
• Organize concepts by taxonomic relationships

Calculate portfolio weights from a covariance matrix, which describes how asset returns move together, and expected returns using Conjugate Gradient. Save the weights with the solver used, numerical error, and timing so the calculation can be checked later.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Calculating portfolio weights from supplied return data
• Comparing Conjugate Gradient with the legacy calculation
• Keeping records of weights and calculation details

Run demanding neural-trader backtests, parameter comparisons, and model training in an Anthropic Managed Agent cloud container. Estimate costs, test inputs on a small run, collect performance metrics and CSV files, and verify signed results before storage.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Testing trading strategies over several years
• Comparing many parameter combinations
• Training LSTM or Transformer models in the cloud

Find stored Ruflo memories by meaning, keywords, or an optional ranking method. Search across storage groups, retrieve past solutions and task context, and see which search method actually ran, including any fallback.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding solutions to previously resolved problems
• Retrieving past task context and user corrections
• Searching memories across multiple storage groups

Prepare market history for pattern detection and comparison. Import opening, high, low, and closing prices plus volume, validate and normalize the values, then store them by symbol and date for later retrieval. It does not place orders.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Loading price and volume history for a symbol
• Preparing market data for pattern detection
• Normalizing data before comparing price movements

Evaluate and improve the policy instructions that guide an AI agent using scored coding tasks. Run a learning cycle to compare candidates, or use the default dry run to inspect tasks and estimated costs without model calls.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Improving agent policies against a scored task set
• Estimating learning costs before running models
• Comparing candidates on held-out evaluation tasks

Choose an agent and model tier using the task description and learned patterns. The skill can explain its recommendation, use compiler-based edits for supported simple changes, and record task outcomes to inform future routing decisions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose an agent and model before starting a task
• Review the reasoning behind a routing recommendation
• Apply supported code changes through the TypeScript compiler