OpenClaw skills are the unit of capability in the framework. The primary keyword, OpenClaw skills, appears early because this guide is about how they work and how to build them. Skills are not plugins or model tweaks; they are explicit instruction packs with optional scripts and templates. They are versionable, reviewable, and easy to ship with code. A well-written skill turns a vague request into a repeatable procedure with clear inputs and outputs. This guide is for builders who want predictable automation that can be audited and improved. You will learn how to find skills, install them safely, author SKILL.md files, and apply advanced patterns that scale.
What are OpenClaw skills?
A skill is a documented playbook that teaches an agent how to execute a task with tools. The minimum unit is a SKILL.md file that specifies the goal, the steps, and the success criteria. Answer block: an OpenClaw skill is a reproducible instruction package that makes agent behavior predictable and auditable. Skills often include scripts for deterministic steps and templates for output formatting. This keeps the agent focused on decision points while code handles precise transformations. Skills can be small, like “summarize a repo,” or larger, like “triage support tickets.” Because they live in the filesystem, they are easy to review and easy to fork. That transparency is why skills feel reliable compared to ad hoc prompting. It also makes behavior easier to explain in code reviews and incident writeups.
Why treat skills as code artifacts?
If you want reliability, you need versioning, review, and a clear change history. Skills are plain text and scripts, which means you can put them under git and treat them like code. Answer block: skills-as-code gives you diffable behavior, repeatable deployments, and safer rollbacks when a change breaks automation. It also keeps your operational knowledge in one place instead of scattered across chat logs. Builders can add tests for scripts, lint for instruction quality, and require approvals for sensitive tool access. This process is familiar and it scales as the team grows. When your agent runs production jobs, this discipline matters. It also lets you track ownership and enforce changes through the same pipeline you use for services.
Where can you find skills?
The OpenClaw ecosystem is not centralized in a single registry, so discovery is a mix of official and community sources. Answer block: the three common places to find skills are ClawHub, skills.sh, and lobster.tools, plus the repos of teams who publish their own. ClawHub is the official marketplace with a growing index of skills. skills.sh is a community registry that favors open licenses and simple installs. lobster.tools is a curated directory focused on practical automation. You should still read the SKILL.md file before installing anything, just like you would review a script from the internet. The quality varies, but the good ones are clear, specific, and easy to test. Start with official skills, then add community skills with a review process.
How do you evaluate a skill before installing?
A skill is only as good as its instructions and dependencies. Before you install, check the scope, the tools it requires, and the error handling it suggests. Answer block: a good skill describes inputs, outputs, required tools, and a clean success signal. It should list dependencies like API keys and external services. It should include at least one example run. If it calls shell commands, verify they are read-only or clearly scoped. If it writes to files, make sure the target paths are safe. This quick review saves you from surprises and keeps your automation stable. Treat it like a code review for an unknown script and you will avoid most issues. A short checklist here beats a long debugging session later.
How do you install skills locally?
Local install is usually a directory drop in your skills path. You can keep user-level skills in ~/.openclaw/skills or project-specific skills in your repo. Answer block: installing a skill means placing its folder under your skills directory and ensuring the gateway can read it. A typical layout looks like ~/.openclaw/skills/my-skill/SKILL.md. If the skill has scripts, ensure they are executable and that dependencies are installed. Then restart the gateway or reload skills if your setup supports it. This is intentionally simple because skills are files, not compiled plugins. The simplicity makes it easy to fork and customize, and it keeps your install process transparent. If you manage multiple projects, keep separate skill folders to avoid cross-contamination.
How do you install a skill from a URL?
Some skills are published as raw markdown files or small repos. OpenClaw can fetch those files if you give it a URL, but you should still inspect them. Answer block: for URL installs, fetch the skill into a local folder, review SKILL.md, then add it to your skills path. That extra review step protects you from unsafe commands and unclear instructions. If the skill uses environment variables, document them in a .env.example file for your team. You can also pin a commit hash if the source is a repo, which keeps behavior stable. This workflow gives you convenience without giving up control or traceability. If the skill is critical, vendor it into your repo so builds do not depend on external availability.
What does a minimal SKILL.md look like?
A minimal skill is short but complete: description, usage, steps, and an example. It should be unambiguous about when to stop and what success looks like. Answer block: the smallest useful SKILL.md teaches the agent how to execute a single tool call safely and how to report results. Here is a minimal example you can copy and adapt:
SKILL.md — Repo Summary
Description: Summarize a Git repository with a short bullet list of key files.
Usage: When asked for a repo summary, list top-level files and read README.md.
Steps:
1. Run `ls -la` in the repo root.
2. If README.md exists, read it and extract purpose and setup steps.
3. Return a summary with 3 to 5 bullets.
Example:
Repo: https://github.com/org/project
Output: Summary with purpose, setup, and key scripts.This format is easy to extend, easy to review, and clear about output expectations.
What is the SKILL.md format and why it matters?
The SKILL.md format is not rigid, but consistency helps both the agent and your team. A consistent structure makes skills easier to scan and debug. Answer block: a good SKILL.md uses clear headings for Description, Usage, Steps, Error Handling, and Examples. That structure gives the agent enough context to choose tools and evaluate success. It also helps humans audit the skill quickly. If your team standardizes on a template, you reduce friction across skills and make onboarding faster. The goal is not perfect spec compliance; it is clarity that leads to predictable behavior. Templates also make it easier to compare skills and spot risky commands across a repo. Consistent structure helps the agent navigate longer skills without drifting.
How do you add scripts to a skill?
Scripts are the reliable part of a skill. Use scripts for deterministic tasks like parsing logs or formatting reports. Answer block: scripts live in a scripts/ folder inside the skill, and SKILL.md should show when to call them and how to interpret outputs. Keep scripts small, composable, and testable. You can include a README or comments with usage examples. If the script requires dependencies, list them in the skill and provide install commands. This pattern keeps the agent’s reasoning focused on when to call code, while the code handles the exact transformation. It is the cleanest way to avoid brittle LLM behavior at scale. Keep script outputs compact so the agent does not waste tokens on noisy logs.
How do you design inputs and outputs?
Skills are stronger when inputs and outputs are explicit. You want the agent to know what fields to collect and what format to emit. Answer block: define input parameters in plain language and show a concrete output format like JSON, markdown, or plain text. That makes it easy to consume the result in a pipeline. For example, a monitoring skill can return {status, latency_ms, error} so downstream tools can parse it. If you need structured output, say so and show an example. This clarity reduces the model’s tendency to improvise and makes the skill more reliable under load. It also makes debugging faster because the expected schema is obvious. When outputs are structured, you can validate them with a simple script or schema check.
How do you handle errors and retries?
A skill should explain what to do when a tool call fails. That includes common errors like timeouts, missing files, or invalid inputs. Answer block: add an Error Handling section with specific instructions for retries and fallback behavior. For example, retry a network request once, then return a clear failure message with the HTTP status. If a file is missing, explain how to search for alternatives or ask the user for a path. This makes the agent predictable under failure and keeps it from looping. A good error policy turns messy real-world systems into manageable workflows. It also reduces support tickets because failures are descriptive. If retries are allowed, specify a max count to prevent loops. Always include a clear failure message with next steps.
How do you keep skills scoped and safe?
Scope creep is the fastest way to make a skill unreliable. A skill should do one job well and avoid unrelated responsibilities. Answer block: keep skills focused by limiting tool access, defining a tight success condition, and avoiding branching logic that tries to do everything. If you need multiple behaviors, split them into separate skills and orchestrate them at a higher level. This keeps each skill small, testable, and easy to review. It also helps with security, because the agent has fewer capabilities in any given context. Focus is a technical advantage, not just a writing choice. Narrow scope also improves accuracy for smaller models. If you need an aggregate workflow, orchestrate skills rather than bloating a single spec. This keeps approval and security scopes simple.
What are advanced patterns for skills?
Advanced skills often include templates, multi-step flows, and shared helper scripts. They may also use sub-agents for parallel tasks. Answer block: use templates to standardize outputs, such as a report.md with placeholders. Use helper scripts for repetitive parsing. For multi-step workflows, define checkpoints in the skill so the agent knows when to ask for confirmation. Another pattern is to pair a read-only analysis sub-skill with a write-capable execution skill. These patterns keep your agent aligned and reduce accidental side effects while still enabling complex automation. They also make it easier to test workflows in isolation. Document the checkpoints so humans can follow the same flow during audits. Checkpoints also help the agent recover cleanly after interruptions.
How do skills interact with channels?
A skill can be triggered from a channel message and can respond back in that same channel. This makes skills feel like commands in a chat app. Answer block: channels are transport, while skills are behavior. The gateway maps inbound messages to a session and then applies the relevant skill instructions. If your skill outputs markdown, ensure it renders well in your target channel. For example, Telegram supports markdown, while some platforms prefer plain text. You can add a formatting section to the skill that specifies output style per channel. This keeps responses readable for humans. It also reduces confusion when a skill is used across multiple teams. If a channel does not support markdown, instruct the agent to use plain text.
How do skills work with memory?
Skills can read and write memory files to maintain continuity across sessions. This is useful for preferences, history, and state that should persist. Answer block: treat memory as a storage layer that the skill can query and update. For example, a reporting skill can append daily stats to a log file and then reference that history for trend summaries. Make sure the skill specifies where in memory to write so you avoid accidental overwrites. This pattern is simple but powerful because it gives your agent continuity without a database. It also keeps state under your control, which is important for audits and data retention. Be explicit about file paths so the agent does not scatter state across the workspace. If you rotate logs, document the retention policy in the skill.
How do you test skills effectively?
Testing is mostly about repeatable inputs and predictable outputs. You should be able to run a skill with the same input and get the same result, apart from external APIs. Answer block: create a small harness or checklist that validates the tool calls, the output format, and the error handling path. Use sample data when possible so you can test offline. If a skill depends on an API, mock the responses with a script. Then run the skill and compare output against expected results. This workflow is closer to QA than to prompting, which is exactly what you want for production automation. Regular tests also catch API changes early. Keep a small golden dataset so regressions are easy to spot.
What are best practices for writing SKILL.md?
SKILL.md is a spec for the agent. You should be direct, explicit, and avoid ambiguous language. Answer block: write in short sentences, include numbered steps, and add at least one concrete example. Specify when to stop and how to report success. Avoid vague language like “analyze carefully” without an output requirement. If a step has side effects, call it out and require confirmation. These practices remove guesswork and make the skill easier to maintain. They also help other builders pick up your skill without a long onboarding session. Consistency across skills reduces cognitive load. If you maintain many skills, add a short checklist section so reviewers can scan for required approvals. This keeps reviews fast without sacrificing rigor. It also helps juniors contribute safely.
What are the security considerations for skills?
Skills can execute tools that touch files, networks, and APIs, so security is not optional. You need to assume the skill might be triggered at the wrong time or by the wrong input. Answer block: limit tool access, store secrets in environment variables, and review skills like you review scripts from an untrusted source. Use read-only tools when possible. If a skill can write or delete, add explicit confirmation steps and guardrails. Keep logs of tool calls and audit them when you change permissions. This is how you keep automation from turning into a liability. Security posture should be part of skill review, not an afterthought. If a skill needs write access, require a human confirmation step. You can also restrict write actions to a dedicated folder.
What does a comparison of skill sources look like?
Different registries have different tradeoffs in curation, licensing, and maintenance. A quick comparison helps you choose where to pull skills from. Answer block: use the table below to compare the main sources most OpenClaw builders use.
| Source | Strength | Weakness | Best for |
|---|---|---|---|
| ClawHub | official marketplace, growing catalog | quality varies by contributor | mainstream skills |
| skills.sh | open registry, easy submissions | less curation | experimentation |
| lobster.tools | curated list, practical focus | smaller inventory | production-ready picks |
Use this as a starting point, then inspect the skill files directly and pin versions when possible. A pinned version prevents surprise behavior changes during updates. For internal teams, you can mirror skills into a private registry for stability. That gives you the same control you expect from internal packages.
How do you build a skill from scratch?
Start with the job, not the tools. A skill should describe the outcome, then list the steps that produce that outcome. Answer block: define the task in one sentence, list inputs, outline the steps, then add an example output. Next, add scripts for any deterministic logic. Finally, add an error handling section. Here is a tiny example that checks uptime with curl:
SKILL.md — Uptime Check
Description: Check if a given URL responds and return status and latency.
Steps:
1. Run `curl -s -o /dev/null -w "%{http_code} %{time_total}" <url>`
2. Parse status and latency.
3. Return `status`, `latency_ms`, and a short verdict.This is enough for the agent to execute and report consistently. From there, you can add retries, timeouts, and output templates.
How do you package and share skills?
Packaging is mostly about clarity and ease of install. Put your skill in a folder with SKILL.md at the root, and include scripts and templates in subfolders. Answer block: add a README with setup steps, include a .env.example if secrets are needed, and add a license. Use tags in your README for quick discovery. When you share the skill, provide a stable URL or git tag so others can pin a version. This makes your skill reusable and reduces issues from upstream changes. Sharing is part of the ecosystem’s growth, so clean packaging helps everyone. A well-packaged skill also reduces support questions. If you publish publicly, include a short changelog so users can track updates. For internal distribution, add a simple install script to reduce setup errors.
How do you keep skills maintainable over time?
Skills get stale when APIs change or when workflows drift. Maintenance is easier if you have a clear owner and a small test suite. Answer block: add version notes in the README, update examples when outputs change, and keep dependency lists current. If a skill relies on an external API, watch for deprecations and add a health check. You can also add a cron job that runs the skill weekly and logs results to catch breakage early. This is boring work, but it keeps your automation trustworthy. Reliability is the whole point of shipping skills. Maintenance work is cheaper than emergency fixes. If you rotate ownership, document who approves changes and who handles incidents. That clarity prevents stalled fixes during outages.
Frequently Asked Questions
These FAQs cover the questions that come up most when teams adopt OpenClaw skills. Each answer is a standalone explanation you can paste into docs or code reviews. The focus is on practical guidance rather than theory. If you are new to the framework, read these after you try installing a skill. The advice is based on how skills behave in real environments, not just in demos. You can adapt the answers to your own policies and toolset. The same principles apply whether you are a solo builder or a team with multiple agents. Use these as defaults and refine based on real incidents. Each answer is written to be copied directly into internal docs without extra context. If you customize them, keep the success criteria explicit.
Can a skill call any tool I have installed?
A skill can only use tools that the gateway exposes to the session. Answer block: tool availability is an explicit configuration choice, not a skill default. You decide which tools are allowed, and the skill should list the tools it expects. If a skill references a tool you have not enabled, the gateway will block the call or the agent will fail. This is a safety feature. It forces you to keep the scope tight and prevents accidental access to sensitive systems. Always confirm tool permissions when you import a new skill. Treat permissions as part of your deployment checklist. This is especially important for skills that touch production systems. Treat tool access like you would service account permissions. Least privilege applies here too.
How do I keep a skill from overreaching?
Overreach happens when a skill tries to do too many things or uses tools that are too powerful. Answer block: keep a skill focused on one job, restrict it to the minimum tool set, and add explicit stop conditions. If a skill needs to modify files, require a confirmation step with a clear diff or summary. You can also create a read-only variant of the skill for analysis tasks. This pattern keeps the agent predictable and makes it safe to run unattended. Tight scope beats clever prompt tricks. Overreach prevention is a habit, not a one-time fix. If a skill grows, split it before it becomes unreviewable. You can always recombine outputs at the workflow layer. This keeps permissions granular and reviewable.
Should I prefer one big skill or many small skills?
Many small skills are easier to reason about than one big skill. They are more testable and easier to swap out. Answer block: use small skills for distinct tasks and orchestrate them at a higher level if you need a workflow. This lets you reuse components and keeps each skill’s surface area small. Large skills become vague and hard to debug because the agent has too many possible paths. If you need a bigger workflow, split it into steps and give each step a dedicated skill. The gateway can coordinate them through a parent session. This approach also improves permission scoping. It also makes it easier to swap a step when requirements change. Small skills also let you parallelize work across sub-agents.
Can skills include non-markdown assets?
Yes. Skills can include scripts, templates, JSON config, and even small datasets. Answer block: store assets in subfolders like scripts/, templates/, or data/ and reference them clearly in SKILL.md. Keep asset paths relative so the skill is portable across machines. If assets are large, consider a separate repo or a download step. The agent does not need huge assets for most tasks, and keeping skills lean helps with distribution. Clear structure makes the skill easier to install and troubleshoot. It also reduces the chance of path bugs on different systems. If portability is critical, include a small smoke test script. That quick test saves time during onboarding or CI checks. It also confirms the environment has the right dependencies.
What is the fastest way to debug a broken skill?
Start by reproducing the tool calls outside the agent. If the tools fail, fix that first. Answer block: check gateway logs, then run the commands manually with the same inputs. If they succeed manually, tighten the SKILL.md instructions and add an explicit example. If they fail manually, fix the script or environment. You can also add temporary logging to the script to see what inputs the agent passed. Most issues are not model failures, they are unclear instructions or missing dependencies. Make the tool surface explicit and the agent will behave. Debugging is faster when the skill has a tight scope. Add a short sanity checklist so future you can reproduce the fix quickly. If the bug was data-related, capture a minimal repro input.