Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Evaluate RAG pipelines with Ragas, measuring faithfulness, answer relevancy, context precision and recall, building golden datasets, and wiring threshold gates into CI for retrieval regressions.
A simple example skill that demonstrates the basic structure and functionality. Use when you need to show how skills work or test skill loading.
昔涟原作语气校准。系统自动检测场景并强制注入语气规则到 system prompt，ILM 必须遵守，不可选择忽略。
Sync the agentpool skills implementation with the official Agent Skills Spec by comparing against the reference repository.
Design a loss function and harness for a long-running /goal optimization run (loss-function development, LFD). Use when the user wants to set up an autonomous optimization loop, distill a product from public artifacts, turn a spec into an optimization target, or asks to design a /goal. Observes the existing environment, interrogates the task, ingests or generates the spec, builds a blinded eval, generates and verifies the harness, red-teams the target for cheats, and emits goal.md ready to launch. Re-invoke in patch mode when a running loop cheated and the loss function needs patching.
Give an agent durable, cross-session long-term memory using Databricks MANAGED memory (the Unity Catalog memory-store REST APIs) as tools — governed by UC with no infra the customer needs to run. This works for either OpenAI Agents SDK or LangGraph templates. Use when: the agent should remember a user's (or a team/org's shared) preferences/facts/decisions across conversations; keywords 'long-term memory', 'managed memory', 'memory store', 'agentic memory'. This is separate from the self-hosted Lakebase memory solution with skills in (agent-openai-memory / agent-langgraph-memory).
Replace the client-side agent loop with Databricks Supervisor API (hosted tools + client-side function tools). Use when: (1) User asks about Supervisor API, (2) User wants Databricks to run the agent loop server-side, (3) Connecting Genie spaces, UC functions, agent endpoints, or MCP servers as hosted tools, (4) Mixing client-side function tools (Python callables your app executes) with hosted tools.
Add client-side function tools to the Supervisor API. Use when: (1) User wants to mix Python callables with hosted tools, (2) User asks about function tools with Supervisor API, (3) User needs to execute custom business logic alongside hosted tool calls.
Add tools to your agent and grant required permissions in databricks.yml. Use when: (1) Adding MCP servers, Genie spaces, vector search, or UC functions to agent, (2) Permission errors at runtime, (3) User says 'add tool', 'connect to', 'grant permission', (4) Configuring databricks.yml resources.
Beta — Deploy the autonomous-repo feedback loop into a GitHub repo. Scaffolds the lane workflows, the runtime skill, and one config file as a reviewed PR, then prints the one-time identity/secret setup checklist. Turns a repo into one that triages incoming feedback into issues and prepares human-gated fix PRs. Use when someone wants to make a repo self-managing / "agentify" it / install the feedback loop.