Data analysis, machine learning, LLM apps, and prompt design (9K skills)
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.
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.
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.
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.
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.
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.
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.
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 Identify repeated rules, contradictions, outdated content, and material better moved into skills, then receive evidence-backed recommendations ranked by estimated token savings and change risk.
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.
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.
Add Codex as the agent that plans tasks and coordinates tools in NanoClaw. Choose it for individual groups alongside Claude, with conversation history and session resume using a ChatGPT subscription or OpenAI API key.
Connect a container agent to local Ollama models through MCP, a protocol for agent tools. The agent can list models and generate responses, with optional tools to download, delete, inspect, and check loaded models.
Add Tavily web search and URL content extraction to selected NanoClaw agent groups without an API key. Configures MCP, a protocol for connecting agent tools, checks availability, and prepares an upgrade path when the shared allowance runs out.
Let a NanoClaw container agent call local models running in Atomic Chat. The skill adds an MCP server, a connection that exposes model listing and text generation as tools, while Claude continues to coordinate the work.
Analyze CSV files with Python and get concise numerical summaries. The skill checks column structure before calculating requested aggregates, includes units when available, and explains assumptions when data is missing or incorrectly formatted.
Create or improve AI prompts through a guided conversation about goals, roles, examples, and output formats. Receive a complete, reusable prompt with an explanation of its design, including behavioral boundaries for public-facing AI roles when needed.
Install and configure the memory-tencentdb plugin for OpenClaw to retain and retrieve conversation information. Verify the setup through logs and searches, adjust memory settings, and troubleshoot missing recall or vector search results.
Connect an AI agent client to Memory Proxy, which adds team memory, skills, and knowledge to model requests. The guided workflow checks existing settings, tests connectivity, and writes the appropriate client configuration.
Maintain an application's supported language models by checking provider availability, adding models, and keeping documentation, live tests, and model-specific request settings aligned. It also checks provider model identifiers and candidates for the recommended list.
Design and audit an agent's system prompt, the instructions that guide its work. Define a concrete outcome, starting input, available tools, completion checks, fallback behavior, and final report so the prompt describes a complete run.
Design research agents that search, read, and turn web or internal sources into clear reports. The playbook defines source freshness, citations, search limits, and a consistent format that separates findings from information the agent could not verify.
Design a general-purpose assistant for tasks such as email summaries, short reply drafts, and note capture. The playbook defines task boundaries, completion criteria, and concise responses, with only the tools needed for the requested work.
Build a local interactive interface for exploring calculations, simple simulations, and weighted comparisons. Adjust inputs to see how results change, with visible formulas, units, and assumptions that explain what the numbers mean.
Plan and evaluate product A/B tests with clear hypotheses, success metrics, sample size estimates, and stopping rules. Rank proposed experiments and interpret results using effect sizes and uncertainty to support practical product decisions.