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Enable safe and efficient AI agent interactions with websites via declarative HTML extensions—define agent-executable tools and context using <tool> and <context> tags instead of relying on brittle UI inference.
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npx skills add https://github.com/ADu2021/skillXiv --skill web-agent-declarative-frameworkコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
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Convert arXiv papers into ready-to-use agent skills using category-aware extraction. First classifies the paper into one or more of 11 research categories, then applies a specialized extraction pipeline for each category — because different types of papers produce different types of usable knowledge. A single paper can yield multiple skills if it spans categories. Use this skill whenever the user wants to turn a paper into a skill, extract practical techniques from research, build a skill library from papers, convert arXiv papers into reusable agent instructions, or batch-process multiple papers into skills. Also trigger when someone asks about extracting actionable knowledge from papers, making research practical for LLM agents, or systematically converting academic contributions into structured agent capabilities.
Establish regret bounds for behavior cloning with discretized actions combining statistical error and quantization error terms. Prove smoothness requirements for safe quantizer design, show that learning-based quantizers fail these requirements, and propose model-based augmentation to reduce error dependence from H² to H.
Dynamically allocate LoRA ranks per-layer during fine-tuning instead of using fixed uniform ranks. Learn optimal rank for each layer and subject via variational framework with discretized exponential distribution, reducing memory footprint while maintaining fidelity and text-alignment.
| name | web-agent-declarative-framework |
| title | Building the Web for Agents: A Declarative Framework for Agentic Web Interactions |
| version | 0.0.2 |
| engine | skillxiv-v0.0.2-claude-opus-4.6 |
| license | MIT |
| url | https://arxiv.org/abs/2511.11287 |
| keywords | ["Web Agents","Declarative APIs","Tool Specification","Machine-Readable Contracts","Agent Integration"] |
| description | Enable safe and efficient AI agent interactions with websites via declarative HTML extensions—define agent-executable tools and context using <tool> and <context> tags instead of relying on brittle UI inference. |
Web agents typically infer capabilities by analyzing human UI—button labels, form structures, navigation patterns. This approach is fragile: agents misinterpret intent, trigger unintended actions, and fail gracefully on novel UI layouts. The Agentic Web framework (VOIX) shifts responsibility to developers: specify what agents can do explicitly via declarative HTML extensions.
Rather than agents guessing "what this button does," developers declare tools through <tool> and <context> tags. This creates a machine-readable contract for agent behavior, enabling safe, predictable, and auditable agent interactions while preserving user privacy and control.
Current agent-web integration suffers from two problems:
VOIX solves this with two declarative mechanisms:
<tool> tags: Developers explicitly declare actions agents can invoke, specifying inputs, outputs, and side effects<context> tags: Specify relevant state information agents need to make decisions (user preferences, session state, current form data)Developers build the website normally; agents see enhanced, machine-readable augmentations. A hackathon study showed participants regardless of experience could rapidly build functional agent applications.
<tool> Element: Declarative action specification with name, description, parameters (type, required, validation), output format<context> Element: State declaration with visibility scope (user-visible, agent-only, public); prevents oversharing sensitive dataStep 1: Define Tools. Markup website actions with declarative tool specifications.
<!-- Simple search tool -->
<tool name="search_products"
description="Search product catalog by keyword"
parameters="query:string(required), category:string(optional), limit:number(default=10)">
<!-- Tool definition embedded; framework routes agent requests here -->
<action method="POST" endpoint="/api/search">
<param name="q" source="query"/>
<param name="cat" source="category"/>
<param name="limit" source="limit"/>
</action>
<output format="json" schema="product_list"/>
</tool>
<!-- Complex tool with validation -->
<tool name="add_to_cart"
description="Add item to shopping cart"
parameters="product_id:string(required,pattern='p_[0-9]+'), quantity:number(required,min=1,max=100)">
<permission scope="user"/> <!-- Requires user confirmation -->
<action method="POST" endpoint="/cart/add">
<param name="pid" source="product_id"/>
<param name="qty" source="quantity"/>
</action>
<output format="json" schema="cart_status"/>
<side_effect type="user_notification">Cart updated</side_effect>
</tool>
Step 2: Declare Context. Specify application state relevant to agent decision-making.
<!-- User context: agent needs to know user's preferences -->
<context name="user_prefs" scope="agent">
<field name="preferred_category">electronics</field>
<field name="budget_max">500</field>
<field name="shipping_address">123 Main St, City</field>
</context>
<!-- Session context: agent state for conversation continuity -->
<context name="session" scope="agent">
<field name="conversation_id">sess_12345</field>
<field name="previous_queries">last 5 user queries</field>
<field name="current_task">help_choose_laptop</field>
</context>
<!-- Public context: information agents can expose to humans -->
<context name="product_listing" scope="public">
<field name="featured_products">list of top 10 products</field>
<field name="sale_items">current promotions</field>
</context>
Step 3: Build Tool Registry. Parse declarative specs and index tools for agent access.
class ToolRegistry:
def __init__(self, html_content):
self.tools = {}
self.context = {}
self.parse_html(html_content)
def parse_html(self, html):
"""Extract <tool> and <context> declarations from HTML."""
soup = BeautifulSoup(html, 'html.parser')
# Parse tools
for tool_elem in soup.find_all('tool'):
tool_name = tool_elem.get('name')
description = tool_elem.get('description')
param_spec = tool_elem.get('parameters')
self.tools[tool_name] = {
'description': description,
'params': self.parse_parameters(param_spec),
'endpoint': tool_elem.find('action').get('endpoint'),
'method': tool_elem.find('action').get('method'),
'requires_permission': bool(tool_elem.find('permission')),
'output_schema': tool_elem.find('output').get('schema')
}
# Parse context
for ctx_elem in soup.find_all('context'):
ctx_name = ctx_elem.get('name')
scope = ctx_elem.get('scope')
fields = {}
for field in ctx_elem.find_all('field'):
fields[field.get('name')] = field.text
self.context[ctx_name] = {
'scope': scope,
'fields': fields
}
def parse_parameters(self, param_spec):
"""Parse parameter string: 'name:type(constraints), ...'"""
params = {}
for param_str in param_spec.split(','):
# Parse: "query:string(required)" or "limit:number(default=10)"
parts = param_str.strip().split(':')
name = parts[0]
type_and_constraints = parts[1] if len(parts) > 1 else "string"
params[name] = self.parse_type_spec(type_and_constraints)
return params
def get_available_tools(self):
"""Return list of tools available to agents."""
return list(self.tools.keys())
def get_tool_spec(self, tool_name):
"""Retrieve full specification for a tool."""
return self.tools.get(tool_name)
def validate_tool_call(self, tool_name, args):
"""Validate agent tool call against declared spec."""
tool_spec = self.get_tool_spec(tool_name)
if not tool_spec:
raise ValueError(f"Unknown tool: {tool_name}")
# Validate each parameter
for param_name, param_spec in tool_spec['params'].items():
if param_spec['required'] and param_name not in args:
raise ValueError(f"Missing required parameter: {param_name}")
if param_name in args:
# Type and constraint checking
self.validate_parameter(args[param_name], param_spec)
return True
Step 4: Agent-Server Integration. Agents query registry and execute validated tool calls.
@app.route('/agent/tools', methods=['GET'])
def list_tools():
"""Agent queries available tools."""
return {
'tools': registry.get_available_tools(),
'context': registry.context
}
@app.route('/agent/execute', methods=['POST'])
def execute_tool():
"""Execute agent tool call with validation."""
data = request.json
tool_name = data['tool']
args = data['args']
# Validate against spec
try:
registry.validate_tool_call(tool_name, args)
except ValueError as e:
return {'error': str(e)}, 400
# Execute
tool_spec = registry.get_tool_spec(tool_name)
response = requests.request(
method=tool_spec['method'],
url=tool_spec['endpoint'],
json=args
)
return response.json()
When to Use: Web applications requiring agent integration (customer service bots, shopping assistants, workflow automation). Use whenever you want agents to interact with your site predictably.
Declarative Best Practices:
<tool> descriptions: be explicit about side effects (e.g., "Permanently deletes user account")<permission>Pitfalls:
When NOT to Use: Static websites with no agent interaction needs; internal tools not exposed to agents.
Integration: Pairs naturally with any agent framework (ReAct, LangChain, custom). Can be layered atop existing websites without major refactoring.
Reference: https://arxiv.org/abs/2511.11287