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Key Findings
  • OpenClaw Skills is a modular capability system centered on the skill.md specification. The official marketplace currently hosts over 100 skills, spanning browser automation, file system operations, calendar integration, Shell execution, and other major categories.
  • skill.md uses a hybrid format of YAML frontmatter plus Markdown description. Through three key fields — capabilities, permissions, and inputs — the OpenClaw agent can fully understand a skill's boundaries before execution.
  • BlogWatcher and Supermemory are the two most widely used community Skills: the former handles periodic web content monitoring, while the latter provides cross-session long-term memory vector storage.
  • A February 2026 report by The Register revealed that some community-submitted Skills leaked API keys in log output. Security teams should establish mandatory standards for input validation and key management.[4]
  • For enterprise deployment, it is recommended to establish a Private Skill Registry, combined with an approval workflow and version pinning mechanism, to ensure production environment stability and compliance.

Since emerging as an "open-source super agent" in late 2025, OpenClaw has rapidly evolved from a niche developer tool into a strong contender for enterprise-grade AI automation platforms.[5] One of the keys to its success is the design of a skill extension system that balances flexibility and security, allowing any developer to add capabilities to the agent in a standardized way without modifying core code.[6]

This article takes a developer's perspective to provide an in-depth analysis of the complete OpenClaw Skills lifecycle: from understanding the skill.md specification syntax, step-by-step building your first custom skill, studying implementation patterns of popular official Skills, to safely publishing to the Skills marketplace and managing private Skill ecosystems in enterprise environments. Whether you are an engineer looking to build custom automation tools for your internal team, or an independent developer interested in commercializing Skills, this guide provides directly applicable technical knowledge.[1]

1. Skills Ecosystem Overview

1.1 What Is an OpenClaw Skill

In the OpenClaw architecture, a Skill is a self-contained capability module that tells the agent "what I can do" and "what resources I need to do it" through a standardized interface. Unlike traditional plugin systems that require the host program to mount specific APIs, OpenClaw Skills use a declarative design: developers describe skill capabilities in human-readable format within the skill.md file, and the OpenClaw core engine dynamically reads these descriptions when planning tasks to determine when to invoke which skill.[2]

This design brings two important advantages: first, the agent can understand each skill's capability boundaries before execution, avoiding trial-and-error invocations; second, humans can directly read the skill.md to audit skill behavior without needing to reverse-engineer code. This is especially important for enterprise compliance reviews.

Each Skill is represented as an independent directory in the file system, structured as follows:

my-skill/
├── skill.md          # Skill specification (required)
├── index.js          # Main logic entry point (required)
├── package.json      # npm dependency declaration (optional)
├── tests/            # Unit test directory (recommended)
│   └── skill.test.js
└── README.md         # Additional documentation (optional)

1.2 Official Skills Marketplace Status

As of February 2026, the OpenClaw official Skills Marketplace has cataloged over 100 skills, contributed by the official core team, verified partners, and community developers.[3] The distribution by functional category is as follows:

Additionally, the monthly growth in new Skills is trending upward, and the vitality of the community ecosystem has become an important factor in OpenClaw's differentiation from competitors.

1.3 Skill Invocation Mechanism

When a user submits a request to OpenClaw, the agent executes the following workflow: first, it scans all installed Skills' skill.md files to build a capability index; then it matches the most suitable Skill based on task semantics; after confirming that the required permissions have been authorized by the user, it actually executes the Skill's index.js logic. The entire process is nearly transparent to users, but understanding this workflow is critical for developers to design effective skill descriptions.[9]

2. skill.md Specification in Detail

2.1 Overall Structure

skill.md uses a hybrid format of YAML frontmatter plus Markdown body, where the YAML block contains machine-readable structured metadata, and the Markdown body provides detailed descriptions for AI models and human developers to read. Both are indispensable: YAML determines the skill's discoverability and execution boundaries, while Markdown determines whether the agent can correctly understand when and how to use the skill.[2]

Below is a complete skill.md structure example:

---
name: my-custom-skill
version: 1.2.0
description: "A one-line description of this skill's core functionality"
author: your-username
license: MIT

capabilities:
  - id: fetch-webpage
    description: "Fetch the content of a specified URL"
  - id: extract-text
    description: "Extract plain text paragraphs from HTML"

permissions:
  network: true
  filesystem: false
  shell: false
  env:
    - MY_API_KEY

inputs:
  - name: url
    type: string
    required: true
    description: "The full URL of the target webpage"
  - name: selector
    type: string
    required: false
    default: "body"
    description: "CSS selector to specify the extraction scope"

outputs:
  - name: text
    type: string
    description: "The extracted plain text content"
  - name: wordCount
    type: number
    description: "Word count statistic"

tags:
  - browser
  - scraping
  - text-extraction

minOpenClawVersion: "2.1.0"
---

## Skill Description

This skill is used to extract text content from specified web pages,
suitable for scenarios requiring quick access to web information,
content analysis, or building data pipelines.

## Usage Examples

Please extract the main body text from https://example.com and count the words.

## Notes

- Does not support pages requiring login authentication
- JavaScript dynamically rendered content may not be fully captured
- Please comply with the target website's robots.txt rules

2.2 Required Fields Explained

name: The unique identifier for the skill, using kebab-case format (e.g., blog-watcher). Names must not be duplicated within the same OpenClaw instance. When published to the marketplace, names are prefixed with the author, e.g., @username/blog-watcher.

version: Follows the Semantic Versioning specification, formatted as MAJOR.MINOR.PATCH. Major version changes indicate breaking API changes, minor versions indicate new features, and patch versions indicate bug fixes.

capabilities: This is the most critical field in skill.md, directly determining whether the OpenClaw agent can invoke this skill at the appropriate time. Each capability requires an id (machine-readable identifier) and a description (natural language description for AI reasoning). Descriptions should be as specific as possible, avoiding vague terms like "process" or "manage," and instead using verb-plus-object structures such as "Send a Slack message to a specified channel."

permissions: Declares the system resource access permissions required for skill execution. OpenClaw follows the principle of least privilege, and only permissions declared here can be executed. The main permission types include:

2.3 Input/Output Specification

The inputs field defines the parameters a skill accepts, with each parameter containing: name (parameter name), type (data type: string, number, boolean, array, object), required (whether mandatory), default (default value), and description (explanation). Clear inputs definitions enable the agent to automatically extract parameters from conversations, reducing the number of times users need to explicitly specify parameters.

The outputs field describes the data structure returned by the skill, helping the agent correctly pass this skill's output to the next step when planning multi-step tasks.

2.4 Version Compatibility Management

The minOpenClawVersion field specifies the minimum OpenClaw version required by the skill, which is especially important for skills that use newer API features. If the user's OpenClaw version does not meet the requirement, the system will display a warning before installation, rather than letting the skill silently fail at runtime.

3. Building Your First Custom Skill from Scratch

3.1 Environment Preparation

Before starting development, confirm that your environment has OpenClaw 2.1.0 or above and Node.js 20.0.0 or above installed. OpenClaw provides an official CLI tool to assist with skill development:[9]

# Install OpenClaw CLI (if not already installed)
npm install -g @openclaw/cli

# Confirm version
openclaw --version

# View installed skills
openclaw skills list

3.2 Create the Skill Directory

This tutorial will create a "Daily Weather Summary" skill (daily-weather) that fetches the day's weather information for a user-specified city and generates a brief summary.

# Manually create the skill directory structure
mkdir daily-weather && cd daily-weather

# Create the following structure:
# daily-weather/
# ├── skill.md
# ├── index.js
# ├── package.json
# └── tests/
#     └── skill.test.js

3.3 Write the skill.md

Open the skill.md file and fill in the complete skill specification:

---
name: daily-weather
version: 1.0.0
description: "Fetch the current day's weather information for a specified city and generate a human-readable weather summary report"
author: your-username
license: MIT

capabilities:
  - id: get-weather-summary
    description: "Get today's weather conditions for a specified city, including temperature, humidity, precipitation probability, and wind speed"
  - id: format-weather-report
    description: "Format weather data into a natural language summary suitable for direct reporting in conversations"

permissions:
  network: true
  filesystem: false
  shell: false
  env:
    - OPENWEATHER_API_KEY

inputs:
  - name: city
    type: string
    required: true
    description: "City name, supports Chinese or English, e.g., 'Taipei' or 'Tokyo'"
  - name: language
    type: string
    required: false
    default: "en"
    description: "Summary language code, defaults to English"
  - name: units
    type: string
    required: false
    default: "metric"
    description: "Temperature units: metric (Celsius) or imperial (Fahrenheit)"

outputs:
  - name: summary
    type: string
    description: "Weather summary text, suitable for direct display in conversations"
  - name: rawData
    type: object
    description: "Raw weather API response data"
  - name: city
    type: string
    description: "Standardized city name confirmed by the API"

tags:
  - weather
  - data-fetching
  - productivity

minOpenClawVersion: "2.1.0"
---

## Skill Description

The daily-weather skill can fetch real-time weather information for major cities
worldwide and convert technical meteorological data into natural language
summaries for easy reading directly in the OpenClaw conversation interface.

## Usage Examples

- "What's the weather like in Taipei today?"
- "Check today's weather in Tokyo for me"
- "What's the temperature and humidity in Singapore right now?"

## Prerequisites

Requires setting the `OPENWEATHER_API_KEY` environment variable.
A free API key can be obtained at https://openweathermap.org/api.

## Notes

- Weather data is updated every 10 minutes
- The free plan limits API calls to 60 per minute
- Some remote area cities may not have precise data available

3.4 Implement the Skill Logic

Next, implement the core logic in index.js. The main program of an OpenClaw Skill must export a standard asynchronous execution function:

// index.js
import { SkillContext } from '@openclaw/skill-sdk';

/**
 * @param {Object} inputs - Input parameters defined in skill.md
 * @param {SkillContext} context - Execution context provided by OpenClaw
 * @returns {Promise<Object>} Return object conforming to skill.md outputs definition
 */
export async function execute(inputs, context) {
const { city, language = 'en', units = 'metric' } = inputs;

// Read API key from secure environment variable
// Note: never output the apiKey value in logs
const apiKey = context.env.get('OPENWEATHER_API_KEY');

if (!apiKey) {
throw new Error(
'Missing required environment variable OPENWEATHER_API_KEY. ' +
'Please add this environment variable to your OpenClaw configuration.'
);
}

context.log.info(`Querying weather for city: ${city}`);

const url = new URL('https://api.openweathermap.org/data/2.5/weather');
url.searchParams.set('q', city);
url.searchParams.set('appid', apiKey);
url.searchParams.set('units', units);
url.searchParams.set('lang', language === 'zh-TW' ? 'zh_tw' : 'en');

const response = await context.fetch(url.toString());

if (!response.ok) {
if (response.status === 404) {
throw new Error(`Could not find weather data for city "${city}". Please verify the city name is correct.`);
}
throw new Error(`Weather API request failed with status code: ${response.status}`);
}

const data = await response.json();

const tempUnit = units === 'metric' ? '°C' : '°F';
const summary = formatWeatherSummary(data, tempUnit, language);

return {
summary,
rawData: data,
city: data.name,
};
}

function formatWeatherSummary(data, tempUnit, language) {
const temp = Math.round(data.main.temp);
const feelsLike = Math.round(data.main.feels_like);
const humidity = data.main.humidity;
const description = data.weather[0].description;
const windSpeed = data.wind.speed;

if (language === 'zh-TW') {
return (
`${data.name} today: ${description}. ` +
`Temperature ${temp}${tempUnit}, feels like ${feelsLike}${tempUnit}, ` +
`humidity ${humidity}%, wind speed ${windSpeed} m/s.`
);
}

return (
`${data.name} today: ${description}. ` +
`Temperature ${temp}${tempUnit}, feels like ${feelsLike}${tempUnit}, ` +
`humidity ${humidity}%, wind speed ${windSpeed} m/s.`
);
}

3.5 Local Testing

OpenClaw provides a skill sandbox testing environment, allowing developers to verify skill behavior without affecting actual agent workflows:

# Run unit tests
cd daily-weather && npm test

# Check if skill requirements are ready
openclaw skills check

After confirming tests pass, install the skill to your local OpenClaw instance for integration testing:

# Install local skill via plugins install
openclaw plugins install ./daily-weather

# Test in an OpenClaw conversation
# Input: "What's the weather like in Taipei today?"

4. In-Depth Analysis of Popular Official Skills

4.1 Browser Automation Skill

Browser automation skills are the highest-download category in the OpenClaw Skills marketplace. The core skill @openclaw/browser is built on Playwright, providing complete headless browser control capabilities.[1]

Its skill.md declares the following main capabilities:

It is worth noting that the Browser Skill clears cookies and session data by default after each task. If you need to maintain login state for multi-step tasks, pass the persistSession: true parameter when invoking, and clearly understand the associated security implications.

4.2 BlogWatcher Skill

BlogWatcher (@openclaw/blog-watcher) is one of the most popular monitoring skills in the OpenClaw community, designed to periodically track new content from specified websites or RSS sources and trigger notifications or follow-up actions when new articles appear.

BlogWatcher's technical highlight is its combination of two advanced patterns: Scheduled Skill (timed execution) and Event Emission. The key section of its skill.md is as follows:

capabilities:
  - id: watch-blog
    description: "Monitor a specified blog or RSS source and trigger notifications when new articles are published"
  - id: list-watched-sources
    description: "List all currently monitored sources and their last check times"
  - id: unwatch-source
    description: "Stop monitoring a specified source"
  - id: get-latest-posts
    description: "Immediately fetch the latest article list from a specified source"

schedule:
  type: interval
  intervalMinutes: 30
  capability: check-for-updates

events:
  - id: new-post-detected
    description: "Emitted when a monitored source has a new article"
    payload:
      - name: sourceUrl
        type: string
      - name: postTitle
        type: string
      - name: postUrl
        type: string
      - name: publishedAt
        type: string

Typical use cases for BlogWatcher include: competitor activity tracking, research field preprint monitoring, tech blog update notifications, and automatically summarizing new content and pushing it to a Slack channel.

4.3 Supermemory Skill

Supermemory (@openclaw/supermemory) addresses one of the most fundamental limitations of AI agents: the finite context window. It provides OpenClaw with cross-session long-term memory capability through a vector database, enabling the agent to "remember" user preferences, completed tasks, and learned information from past interactions.[1]

Supermemory's capabilities design is highly granular:

On the technical implementation side, Supermemory uses a local embedding model (defaulting to nomic-embed-text) to vectorize memory content, storing it in a SQLite + vector index within the OpenClaw data directory, ensuring all memory data remains local and is not uploaded to any external service.

4.4 Send Message Skills

The OpenClaw Skills marketplace has an important skill subcategory: send message skills, which enable the agent to send notifications and messages across platforms on behalf of users. The main skills include:

@openclaw/send-slack: Supports sending messages to Slack channels or direct messages, with support for Block Kit formatted structured content. Its capability is declared as "Send a Slack message to a specified workspace channel or user."

@openclaw/send-email: Integrates with SMTP or SendGrid API, capable of sending plain text or HTML formatted emails.

@openclaw/send-telegram: Sends messages to individuals or groups via the Telegram Bot API.

@openclaw/send-discord: Supports both Discord Webhook and Bot API sending modes.

All send message skills explicitly declare requiresUserConfirmation: true in their skill.md, meaning the agent must confirm with the user before actually sending, preventing accidental mass message triggers.

4.5 Shell Execution Skill

@openclaw/shell is a powerful but cautiously-used system execution skill that allows the agent to execute Shell commands in a controlled environment. Because this skill's permissions.shell: true declaration grants elevated system access, OpenClaw displays an additional security warning during installation and requires explicit user confirmation.[7]

The Shell Skill restricts executable command categories through a whitelist mechanism. Default allowed commands include: ls, cat, grep, find, git, npm, python3, and other commonly used development tools, while high-risk commands like rm -rf, sudo, and network configuration commands are blocked in the default configuration.

5. Advanced Skill Development Patterns

5.1 Stateful Skills

Most skills are stateless: each execution is independent, retaining no data from previous invocations. However, certain scenarios require skills to maintain state across multiple calls — for example, BlogWatcher needs to record "the latest article from the last check" to determine whether there are updates.

The OpenClaw Skill SDK provides the context.state API for secure state persistence:

export async function execute(inputs, context) {
// Read previously saved state
const prevState = await context.state.get('lastCheck') || {
lastPostId: null,
lastCheckedAt: null,
};

// Execute logic...
const latestPosts = await fetchLatestPosts(inputs.sourceUrl);
const newPosts = latestPosts.filter(
post => post.id !== prevState.lastPostId
);

// Update state (automatically persisted to encrypted storage)
await context.state.set('lastCheck', {
lastPostId: latestPosts[0]?.id,
lastCheckedAt: new Date().toISOString(),
});

return { newPosts };
}

State data is stored in OpenClaw's encrypted data directory, bound to the skill name, and persists across sessions.

5.2 Skill-to-Skill Communication

In advanced application scenarios, one skill may need to invoke another skill's capabilities. OpenClaw provides the context.skills.invoke API for inter-skill collaboration:

export async function execute(inputs, context) {
// Use the browser skill to extract page content
const pageContent = await context.skills.invoke(
'@openclaw/browser',
'extract-content',
{ url: inputs.targetUrl, selector: 'article' }
);

// Use the Supermemory skill to store extracted information
await context.skills.invoke(
'@openclaw/supermemory',
'remember-fact',
{
content: pageContent.text,
tags: ['web-research', inputs.topic],
}
);

return { status: 'saved', contentLength: pageContent.text.length };
}

Dependencies must be declared in skill.md to ensure users are prompted when installing the main skill:

dependencies:
  skills:
    - name: "@openclaw/browser"
      minVersion: "3.0.0"
    - name: "@openclaw/supermemory"
      minVersion: "2.0.0"

5.3 Scheduled Skills

The scheduled execution pattern represented by BlogWatcher allows skills to run periodically in the background without requiring user initiation. Declare the schedule configuration in skill.md:

schedule:
  type: cron
  expression: "0 9 * * 1-5"  # Every weekday at 9 AM
  capability: generate-daily-report
  timezone: "Asia/Taipei"

Scheduled skills run in the OpenClaw background daemon process — even if the user has not opened a conversation window, the skill will still execute automatically at the specified time. Execution results are stored in the notification queue and presented when the user next opens OpenClaw.

5.4 Asynchronous Long-Running Task Skills

When a skill needs to execute a task that takes longer than 30 seconds, it should adopt the asynchronous long-running task pattern to avoid blocking the conversation interface:

export async function execute(inputs, context) {
// Mark as long-running task mode
context.setLongRunning(true);

// Return initial confirmation so the user knows the task has started
context.sendProgressUpdate('Processing started, estimated 2-5 minutes...');

// Execute long-running task
const result = await processLargeDataset(inputs.dataPath, {
onProgress: (percent) => {
context.sendProgressUpdate(`Processing progress: ${percent}%`);
}
});

return { result, completedAt: new Date().toISOString() };
}

6. Skills Marketplace Publishing Process

6.1 Pre-Publishing Preparation

Before submitting a skill to the Skills marketplace, developers must complete the following checklist:[3]

6.2 Packaging and Submission

Use the OpenClaw CLI to package the skill and submit it for review:

# Package as a tarball in the skill directory
npm pack

# This will generate a daily-weather-1.0.0.tgz archive

# Run security audit
openclaw security audit

# Publish via npm (requires a verified npm account)
npm publish --access public

openclaw security audit performs a local security scan, focusing on: whether configuration file permissions are correct, whether known security vulnerabilities exist, and whether package.json dependencies have known vulnerabilities.

6.3 Review Process

After submission, the skill enters the OpenClaw official review process, which typically takes 3-7 business days:

  1. Automated scanning (immediate): Security vulnerabilities, malicious code, dependency compliance
  2. Manual review (1-3 days): Accuracy of skill functionality descriptions, user experience evaluation
  3. Sandbox testing (1-2 days): Actually executing the skill in an isolated environment to verify behavior matches declarations
  4. Listing or rejection: If the review passes, the skill is published to the marketplace; if issues are found, it is returned to the developer with detailed explanations

6.4 Version Update Mechanism

Version updates for published skills follow these rules: patch version (PATCH) updates require no re-review and are published directly; minor version (MINOR) updates require automated scanning to pass before publishing; major version (MAJOR) updates involving breaking API changes require a complete re-review, and the old version will be marked as "deprecated" in the marketplace but retained for 90 days to give users time to migrate.

# Publish a patch version update
npm version patch
npm publish

# Publish a major version update (also update the version number in skill.md)
npm version major
npm publish

7. Security Review and Best Practices

7.1 API Key Leak Risk

In February 2026, The Register reported a serious security issue: some community skills listed in the Skills marketplace output API keys in plaintext in execution logs, making them easily accessible to anyone with access to OpenClaw log files.[4] This issue exposed a widespread lack of security awareness among skill developers.

CrowdStrike's security research report further revealed that some malicious skills even exfiltrated API keys to attacker-controlled servers by embedding environment variable values in logs or error messages.[7] Cisco's research also emphasized that personal AI agent systems' reliance on API keys makes them high-value attack targets.[10]

Below are typical anti-patterns that cause leaks, which developers should avoid:

// DANGEROUS: Never do this
export async function execute(inputs, context) {
const apiKey = context.env.get('MY_API_KEY');

// Mistake 1: Outputting the key in logs
context.log.info(`Using API key: ${apiKey}`);

// Mistake 2: Including the key in error messages
throw new Error(`API call failed, key ${apiKey} is invalid`);

// Mistake 3: Including the key in return values
return { status: 'ok', usedKey: apiKey };
}

The correct way to handle keys:

// SAFE: Correct API key handling
export async function execute(inputs, context) {
const apiKey = context.env.get('MY_API_KEY');

if (!apiKey) {
// Error message only says "missing," without exposing any key information
throw new Error('Missing required environment variable MY_API_KEY');
}

// Log only the first 4 characters of the key (for debugging identification)
const keyPrefix = apiKey.substring(0, 4) + '****';
context.log.info(`Using API key: ${keyPrefix}`);

// Pass directly when in use, never store in any visible location
const result = await callExternalAPI(inputs.data, apiKey);

// Never include the key in return values
return { status: 'ok', result };
}

7.2 Input Validation

Although skill.md defines input types and required fields, developers should not rely on framework auto-validation. Instead, implement proactive input validation in index.js to prevent injection attacks and unexpected behavior:

export async function execute(inputs, context) {
// Explicitly validate URL format
let targetUrl;
try {
targetUrl = new URL(inputs.url);
} catch {
throw new Error('Invalid URL format. Please provide a complete URL starting with https://');
}

// Restrict to HTTPS only
if (targetUrl.protocol !== 'https:') {
throw new Error('For security reasons, only HTTPS protocol URLs are supported');
}

// Validate string length to prevent oversized input
if (inputs.query && inputs.query.length > 1000) {
throw new Error('Query string too long. Maximum 1000 characters supported');
}

// Continue execution...
}

7.3 Understanding Sandbox Limitations

OpenClaw's skill sandbox is not a fully isolated container environment, but rather a soft isolation based on the Node.js module system. This means malicious skills could theoretically still access some system resources. When evaluating third-party skills, developers should prioritize: skills that have passed official review, skills with complete open-source code, skills with active maintenance records on GitHub, and skills with installation counts exceeding 1,000 and positive reviews.[7]

7.4 Principle of Least Privilege

Skill developers should strictly follow the principle of least privilege, declaring only the permissions the skill truly needs:

8. Enterprise Internal Skill Management

8.1 Private Skill Registry

In enterprise environments, allowing employees to install arbitrary skills from the public marketplace poses security and compliance risks. It is recommended to establish a Private Skill Registry, which can use npm private registries (such as Verdaccio or GitHub Packages) as the backend:[8]

# In the enterprise OpenClaw configuration, specify the private registry
# openclaw.config.json
{
"skillRegistry": {
"primary": "https://skills.internal.company.com",
"fallback": null,
"allowPublicMarketplace": false
},
"skillApproval": {
"required": true,
"approvers": ["[email protected]"]
}
}

Setting allowPublicMarketplace to false ensures all Skills installation requests must target the private registry, effectively preventing employees from installing unreviewed public skills.

8.2 Enterprise Skill Approval Workflow

Establish a standardized skill approval process to ensure every skill entering the enterprise environment undergoes security assessment:

  1. Application phase: Employees submit a skill installation request, explaining business requirements and skill source
  2. Security scanning: Automatically execute openclaw skills audit and generate a scan report
  3. Code review: The security team reviews the skill's skill.md and index.js, focusing on permission declarations and external calls
  4. Sandbox testing: Actually execute the skill in an isolated test environment to confirm behavior matches descriptions
  5. Publish to private registry: After review approval, pin the skill version and publish to the enterprise private registry
  6. Periodic re-review: Conduct routine security re-reviews of approved skills every quarter

8.3 Version Control and Dependency Management

Enterprise production environments should pin skill versions to prevent upstream updates from introducing unexpected behavior:

# Enterprise skill pinning configuration
# skills.lock.json (should be under version control)
{
"@openclaw/browser": {
"version": "3.2.1",
"integrity": "sha512-abc123...",
"approvedAt": "2026-01-15",
"approvedBy": "security-team"
},
"@company/internal-crm-skill": {
"version": "2.0.0",
"integrity": "sha512-def456...",
"approvedAt": "2026-02-01",
"approvedBy": "it-team"
}
}

8.4 Internal Skill Development Standards

For custom skills developed internally within the enterprise, the following development standards are recommended:

9. Common Development Issues and Troubleshooting

9.1 Skill Not Correctly Invoked by the Agent

One of the most common issues is that after completing skill implementation, developers find that the agent fails to automatically invoke the skill for relevant tasks. The root cause is usually that the capabilities.description in skill.md is not specific enough or differs too much from how users actually phrase things.

Troubleshooting approach:

# Check skill readiness status
openclaw skills check

# View detailed information for a specific skill
openclaw skills info daily-weather

If the matching score is low (below 0.6), try modifying the capability description: use phrasing closer to users' natural language, and include specific verbs ("query," "fetch," "extract") and objects ("weather information," "temperature data").

9.2 Environment Variable Read Failure

The skill declared environment variables in skill.md's permissions.env, but context.env.get() returns undefined at runtime. Common causes:

# Check skill requirements status
openclaw skills check

# View specific skill details and requirements
openclaw skills info daily-weather

9.3 Network Requests Blocked

The skill declared network: true, but outgoing network requests are intercepted by OpenClaw. Possible causes:

# Use context.fetch correctly in skills (not the global fetch)
// Wrong: Using global fetch directly, may bypass security checks
const response = await fetch(url);

// Correct: Using context.fetch to ensure security interception is active
const response = await context.fetch(url);

9.4 Skill Execution Timeout

OpenClaw defaults to a 30-second skill execution timeout. If a skill needs more time, it should explicitly declare this in skill.md:

execution:
  timeout: 120        # Seconds, maximum allowed is 600
  longRunning: true   # Mark as long-running task, enable progress reporting mechanism

9.5 Skill State Corruption

Stateful skills may leave inconsistent state data after abnormal termination, causing subsequent executions to fail. Implementing defensive state reading is recommended:

export async function execute(inputs, context) {
let state;
try {
state = await context.state.get('myState');
// Validate state data structure integrity
if (!state || typeof state.lastId !== 'string') {
context.log.warn('State data format is incorrect, resetting to initial values');
state = getInitialState();
}
} catch (error) {
context.log.error('Unable to read state, resetting to initial values', error.message);
state = getInitialState();
}

// Continue execution...
}

function getInitialState() {
return { lastId: null, lastCheckedAt: null };
}

9.6 Performance Analysis

If a skill executes too slowly, use OpenClaw's performance analysis tools to identify bottlenecks:

# View Gateway logs to trace skill execution
openclaw logs --follow

Common performance issues include: making serial API requests in loops (should use Promise.all for parallel execution), not caching repeated external requests, and reading/writing state too frequently (should batch state updates).

Conclusion: Building a Trustworthy Skills Ecosystem

The OpenClaw Skills system represents a new paradigm for extending AI agent capabilities: using declarative skill.md specifications instead of traditional plugin APIs, making skill capability boundaries clearly readable by both humans and machines. This design not only lowers the development barrier but also provides a clear entry point for security reviews.[1]

However, the API key leak incidents revealed by The Register remind us that the good intentions of technical design must be supplemented with rigorous developer security education and marketplace review mechanisms.[4] Whether you are an individual developer or an enterprise engineering team, security should be the top priority in Skill development, not an afterthought.

As the OpenClaw Skills marketplace continues to expand, core skills like BlogWatcher and Supermemory mature, and enterprise private skill ecosystems are gradually established, we are witnessing the formation of an agent capability marketplace with skills as the atomic unit. Understanding and mastering this skill development paradigm will be a core competency for AI agent engineers in 2026.