Clawdbot skills are modular capability packages that teach the agent how to perform specific tasks and how to use tools safely. A skill is not just “code that runs”; it is usually a folder with structured instructions that define what the skill does, when it should be used, what inputs it expects, and what constraints it must follow. This matters because Clawdbot is built around an assistant that chooses tools based on context: the skill gives the agent a reliable playbook so it can, for example, fetch a webpage, query a service, run a workflow, or manipulate local files in a controlled way. In everyday use, skills are what turn Clawdbot from “a chat interface” into “an operator that can do useful work,” and they are also the boundary where you decide how much power the assistant has.
Technically, Clawdbot skills follow an “AgentSkills-compatible” structure: each skill is a directory that includes a SKILL.md file with YAML frontmatter and clear instructions. Clawdbot loads built-in (bundled) skills and can also load locally installed skills, applying filtering at load time based on environment, configuration, and whether required binaries are present. That filtering is important: it prevents skills from advertising capabilities that won’t work on your machine. To add skills, you generally install them into a known skills directory in your workspace (or a legacy location that Clawdbot can discover) and then sync/enable them using Clawdbot’s skill tooling. Many users manage skills via a skill registry workflow: you search for a skill, install it into your workspace, and Clawdbot records what’s installed in a local lockfile so upgrades are deterministic. After installation, you restart (or reload) the Gateway so it re-scans skills, then you test in a single channel with a simple prompt that should trigger the skill. If it doesn’t trigger, you inspect: is the skill loaded, is it compatible with your OS, and is the relevant plugin slot enabled?
Skills become especially valuable when you connect Clawdbot to a data backend, because you can keep the assistant’s behavior consistent across channels while swapping storage implementations behind the scenes. For instance, you can create a “Memory Search” skill that summarizes messages into chunks, generates embeddings, and writes them to a vector database such as Milvus or Zilliz Cloud. The skill can also provide a “recall” tool: given a user question, it queries Milvus for the top-K similar chunks and returns them as context for the agent. That keeps the assistant’s memory behavior explicit, testable, and versioned like code. Just as importantly, skills are where you implement safety and scope: you can restrict a “shell” skill to a fixed set of scripts, require an allowlist for destructive operations, and keep secrets out of prompts. In practice, adding skills is a developer workflow: pick a skill, install it into the workspace, verify it loads, test it in one channel, and only then expose it broadly—especially if the skill touches local files, executes commands, or writes to shared systems like Milvus/Zilliz Cloud.