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chartgen-ai

Agentify

by ChartGen AI · GitHub ↗ · v1.0.1 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install agentify
Description
Analyze, rewrite, and generate design specs to make web pages and websites more easily usable by AI agents, web scrapers, and automation tools. Use when (1)...
README (SKILL.md)

Agentify

Make web pages and websites more easily navigable, parseable, and operable by AI agents, web scrapers, and automation tools.

Core Capabilities

1. Analyze

Evaluate web pages or code for agent-friendliness. Produce a scored report (0-100) with actionable improvements.

When to use: The user wants to audit a page, URL, or codebase for agent accessibility.

Workflow:

  1. Acquire the content:
    • URL → fetch and retrieve HTML
    • File path → read the file
    • Glob pattern → find matching files, analyze each
    • IDE selection → analyze selected code
  2. Read the scoring reference: references/scoring.md
  3. Read the full checklist: references/checklist.md
  4. Evaluate across 9 categories (100 points total):
# Category Pts Focus
1 Semantic HTML 15 Heading hierarchy, landmarks, semantic tags vs div soup
2 ARIA & Accessibility 15 Roles, labels, live regions, keyboard attributes
3 Structured Data 15 JSON-LD / schema.org presence, completeness
4 Form Readability 10 Label association, autocomplete, fieldset grouping
5 Navigation Clarity 10 Consistent nav, breadcrumbs, skip links, sitemap
6 Automation Attributes 10 data-testid coverage, data-* for key elements
7 CSS Selector Stability 5 Meaningful class names vs generated hashes
8 API Discoverability 10 Canonical URLs, link relations, OpenAPI
9 Meta & Machine Signals 10 robots meta, description, OG tags, sitemap
  1. Output report in this format:
    • Score: X/100 (Grade A-F)
    • Per-category score breakdown table
    • Top 5 priority improvements with before/after code
    • Detailed findings per category
    • Quick wins section (changes under 5 minutes, 5+ point gain)

2. Rewrite

Transform web templates to be agent-friendly while preserving all existing functionality.

When to use: The user wants to improve existing code for agent consumption.

Workflow:

  1. Detect framework from file extension (.html, .jsx, .tsx, .vue, .svelte)
  2. Read the patterns reference: references/patterns.md
  3. Read framework-specific guidance: references/frameworks.md
  4. Read and understand the entire file before modifying
  5. Apply transformations in order:
    • Replace non-semantic elements with semantic equivalents (only when intent is unambiguous)
    • Add ARIA attributes to interactive elements
    • Add data-testid to buttons, links, inputs, content containers (kebab-case naming)
    • Improve form labels, autocomplete, fieldset grouping
    • Insert JSON-LD structured data where content type is identifiable
    • Add meta tags for full HTML pages
    • Add skip links and nav labels
    • Fix heading hierarchy

Safety rules (non-negotiable):

  • Never remove existing code, event handlers, or component logic
  • Never change class names, IDs, or visual appearance
  • Never break framework-specific syntax
  • Only add attributes, never replace unless strictly better
  • Match existing formatting style
  1. After modification, summarize: number of changes per category, changes considered but skipped, and follow-up suggestions needing human judgment

3. Design Spec

Generate a comprehensive agent-friendly design specification document for development teams.

When to use: The user wants to establish standards for agent-friendly web development.

Workflow:

  1. Parse arguments for project name and focus area (e-commerce, dashboard, docs, SaaS, marketing)
  2. Scan the project if in a code directory:
    • Detect framework (package.json, config files)
    • Detect test framework
    • Grep for existing data-testid, aria-*, schema.org patterns
    • Read 2-3 representative components to understand code style
  3. Read the spec template: references/spec-template.md
  4. Read the example spec: references/spec-example.md
  5. Generate a markdown spec to agent-friendly-spec.md covering:
    • Executive summary
    • Semantic HTML guidelines
    • ARIA & accessibility patterns
    • Naming conventions (data-testid, CSS classes, components)
    • Structured data (JSON-LD) per page type
    • Form design patterns
    • Navigation patterns
    • API & discoverability
    • Meta tags & machine signals
    • Component-level checklists
    • Testing for agent-friendliness
    • Migration guide (quick wins → medium → large effort)

Each section must include: priority level (P0/P1/P2), code examples for the detected framework, anti-patterns, and verification methods.

Shared Knowledge Base

For the canonical reference on all agent-friendly web patterns (semantic HTML, ARIA, structured data, data attributes, forms, navigation, APIs, meta tags, CSS stability, interaction patterns), read: references/knowledge-base.md

Usage Guidance
This skill appears coherent and low-risk: it only contains instructions and reference docs and asks for no credentials or installs. Before using: (1) only run it against URLs and local directories you control or have permission to scan, since it will fetch URLs and read files/globs; (2) avoid granting it access to directories containing secrets (keys, .env, system config); (3) review any suggested code changes before applying them (the SKILL.md promises not to remove logic, but automated edits should be human-verified); (4) consider running it in a sandboxed workspace or branch when performing large rewrites; and (5) note the repository lacks a LICENSE file—verify licensing if you plan to redistribute.
Capability Analysis
Type: OpenClaw Skill Name: agentify Version: 1.0.1 The 'agentify' skill is a utility designed to enhance the accessibility and machine-readability of web pages for AI agents, scrapers, and automation tools. It provides structured workflows in SKILL.md for auditing web content (Analyze), enriching templates with semantic markup and stable selectors (Rewrite), and generating development standards (Design Spec). The instructions include explicit safety constraints to preserve existing application logic and visual styles during code transformations. No indicators of data exfiltration, malicious execution, or harmful prompt injection were found across the codebase or reference documents.
Capability Assessment
Purpose & Capability
Name/description match the runtime instructions: the skill analyzes HTML/code, rewrites templates to add semantic markup/ARIA/data-testid/JSON-LD, and generates design specs. No unrelated binaries, credentials, or installs are requested.
Instruction Scope
SKILL.md explicitly instructs the agent to fetch URLs, read files by path/glob, scan a code directory (package.json, components), and read representative source files. Those actions are expected for this capability, but they do give the agent access to local files and external URLs when invoked — keep that in mind before granting access or invoking autonomously.
Install Mechanism
No install spec or code files; this is instruction-only so nothing is written to disk by an installer. Lowest-risk install posture.
Credentials
The skill declares no environment variables, credentials, or config paths. The instructions reference reading project files and fetching HTTP(S) content, which are proportional to an analyzer/rewriter skill.
Persistence & Privilege
always is false and autonomous invocation is the platform default. The skill does not request persistent system privileges or modify other skills; no elevated persistence observed.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install agentify
  3. After installation, invoke the skill by name or use /agentify
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.1
- Added multilingual documentation: README files now available in Japanese (README.ja.md) and Chinese (README.zh.md), alongside English. - No functional or workflow changes to core capabilities.
v1.0.0
Initial release of Agentify: a tool to analyze, improve, and standardize web pages for agent, scraper, and automation usability. - Analyzes web pages or code for agent-friendliness, scoring across 9 technical categories with detailed reports - Automatically rewrites web templates to add semantic markup, accessibility features (ARIA), structured data, and automation-friendly selectors (e.g., data-testid), preserving all existing logic and appearance - Generates agent-friendly design spec documents tailored for development teams, including code-style guidance, migration plans, and component checklists - Supports multiple frameworks (HTML, JSX, Vue, Svelte) and integrates with automation tools (Playwright, Puppeteer, Cypress, Selenium) - Provides structured workflows, safety rules, and knowledge base references for consistency and best practices
Metadata
Slug agentify
Version 1.0.1
License MIT-0
All-time Installs 1
Active Installs 1
Total Versions 2
Frequently Asked Questions

What is Agentify?

Analyze, rewrite, and generate design specs to make web pages and websites more easily usable by AI agents, web scrapers, and automation tools. Use when (1)... It is an AI Agent Skill for Claude Code / OpenClaw, with 187 downloads so far.

How do I install Agentify?

Run "/install agentify" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Agentify free?

Yes, Agentify is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Agentify support?

Agentify is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Agentify?

It is built and maintained by ChartGen AI (@chartgen-ai); the current version is v1.0.1.

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