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Landing — Premium HTML Landing Page Generator · alirezarezvani | DecimalAI

Frontendby alirezarezvani

Landing — Premium HTML Landing Page Generator

Generates a premium single-page HTML landing page with 3D CSS animations, GSAP scroll effects, and mouse-parallax depth. Forcing intake (product + elevator pitch, audience register, brand overrides, tone) locks down positioning before any copy or markup is written, so the page reflects the actual product rather than generic boilerplate.

Never made a case worseMaintained

SkillScore

SkillScore95/100

Test results 95

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

36%95%of test cases passed +59 pts

Without skill8 of 22

With skill21 of 22

13 fixed8 passed either way1 still failing0 broke

Measured on gemini-3.6-flash9/1/2026v2

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskIn our single-file HTML landing page stylesheet, at which exact pixel width breakpoints should the 3-column feature grid collapse into 2 columns and 1 column?

Without the skillMissed

With the skillPassed

Same model, same task. Answers are capped and redacted.

Use this skill

Your agent runs on

Adds every file the skill has, pinned so you can update it. No account needed.

pip install "decimalai>=0.15"
decimalai skills add alirezarezvani-landing
claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

The same install from Python, for a notebook or a setup script.

Install

pip install "decimalai>=0.15"

Add the skill

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("alirezarezvani-landing")
if result.exit_code:
    raise SystemExit(result.problems)

print("Added. Start Claude Code with the command below.")

Start Claude Code with it

claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

Type these in a Claude Code session. No account or API key. It writes the skill and any files it bundles, and asks first when its safety rating isn't Passed.

Add the DecimalAI plugin, once

/plugin marketplace add decimal-labs/decimalai-claude-plugins
/plugin install decimalai@decimalai

Install this skill

/decimalai:install alirezarezvani-landing

The plugin needs python3 and curl on your machine.

No Python or account. Claude Code picks it up in its next session.

Save it into your project

curl -fsSL --create-dirs -o .claude/skills/alirezarezvani-landing/SKILL.md https://app.decimal.ai/s/alirezarezvani-landing/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/alirezarezvani-landing/SKILL.md

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Claude Code docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add alirezarezvani-landing

Add it to your agent

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

The same install from Python, for a notebook or a setup script. agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("alirezarezvani-landing")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/alirezarezvani-landing/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from claude_agent_sdk import ClaudeAgentOptions, query

skill = Path(".agents/skills/alirezarezvani-landing/SKILL.md").read_text()
options = ClaudeAgentOptions(system_prompt=f"You are a helpful agent.\n\n{skill}", max_turns=6)

async def main():
    async for message in query(prompt="Hello!", options=options):
        print(message)

# In a notebook or async code: await main()
asyncio.run(main())

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Claude Agent SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai-agents]"
decimalai skills add alirezarezvani-landing

Add it to your agent

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

Install

pip install "decimalai[openai-agents]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("alirezarezvani-landing")
if result.exit_code:
    raise SystemExit(result.problems)

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/alirezarezvani-landing/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/alirezarezvani-landing/SKILL.md").read_text()
agent = Agent(name="support", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

OpenAI Agents docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai]"
decimalai skills add alirezarezvani-landing

Add it to your agent

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

Install

pip install "decimalai[openai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("alirezarezvani-landing")
if result.exit_code:
    raise SystemExit(result.problems)

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/alirezarezvani-landing/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/alirezarezvani-landing/SKILL.md").read_text()
reply = client.chat.completions.create(
    model="gpt-5.4-mini",
    messages=[
        {"role": "system", "content": f"You are a helpful agent.\n\n{skill}"},
        {"role": "user", "content": "Hello!"},
    ],
).choices[0].message

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

OpenAI SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[anthropic]"
decimalai skills add alirezarezvani-landing

Add it to your agent

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

Install

pip install "decimalai[anthropic]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("alirezarezvani-landing")
if result.exit_code:
    raise SystemExit(result.problems)

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/alirezarezvani-landing/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/alirezarezvani-landing/SKILL.md").read_text()
reply = client.messages.create(
    model="claude-haiku-5-5",
    max_tokens=4096,
    system=f"You are a helpful agent.\n\n{skill}",
    messages=[{"role": "user", "content": "Hello!"}],
)

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Anthropic docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[google-genai]"
decimalai skills add alirezarezvani-landing

Add it to your agent

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

Install

pip install "decimalai[google-genai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("alirezarezvani-landing")
if result.exit_code:
    raise SystemExit(result.problems)

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/alirezarezvani-landing/SKILL.md

Give it to your agent

from pathlib import Path
from google import genai
from google.genai import types

client = genai.Client()
skill = Path(".agents/skills/alirezarezvani-landing/SKILL.md").read_text()
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Hello!",
    config=types.GenerateContentConfig(system_instruction=f"You are a helpful agent.\n\n{skill}"),
)

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Gemini API docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add alirezarezvani-landing

Add it to your agent

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

Install

pip install "decimalai[langchain]" langchain-openai

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("alirezarezvani-landing")
if result.exit_code:
    raise SystemExit(result.problems)

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/alirezarezvani-landing/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/alirezarezvani-landing/SKILL.md").read_text()
agent = create_agent("openai:gpt-5.4-mini", tools=[], system_prompt=f"You are a helpful agent.\n\n{skill}")
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

LangChain docs

CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add alirezarezvani-landing

Add it to your agent

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

The same install from Python, for a notebook or a setup script. CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("alirezarezvani-landing")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

No DecimalAI package. CrewAI's own loader reads the folder, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/alirezarezvani-landing/SKILL.md

Give it to your agent

from pathlib import Path
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                         # your own CrewAI LLM
    skills=[Path(".agents/skills")],
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

CrewAI docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[adk]"
decimalai skills add alirezarezvani-landing

Add it to your agent

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

Install

pip install "decimalai[adk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("alirezarezvani-landing")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/alirezarezvani-landing/SKILL.md

Give it to your agent

from pathlib import Path
from google.adk.agents import LlmAgent

skill = Path(".agents/skills/alirezarezvani-landing/SKILL.md").read_text()
agent = LlmAgent(
    name="support",
    model="gemini-3.5-flash",
    instruction=lambda context: f"You are a helpful agent.\n\n{skill}",
)

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Google ADK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add alirezarezvani-landing

Add it to your agent

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

Install

pip install "decimalai[pydantic-ai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("alirezarezvani-landing")
if result.exit_code:
    raise SystemExit(result.problems)

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/alirezarezvani-landing/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/alirezarezvani-landing/SKILL.md").read_text()
agent = Agent("openai:gpt-4o-mini", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Pydantic AI docs

Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add alirezarezvani-landing

Add it to your agent

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

The same install from Python, for a notebook or a setup script. Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("alirezarezvani-landing")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/alirezarezvani-landing/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/alirezarezvani-landing/SKILL.md").read_text()
agent = create_deep_agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Deep Agents docs

Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add alirezarezvani-landing

Add it to your agent

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

The same install from Python, for a notebook or a setup script. Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("alirezarezvani-landing")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/alirezarezvani-landing/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/alirezarezvani-landing/SKILL.md").read_text()
agent = Agent(client=client, instructions=f"You are a helpful agent.\n\n{skill}")
asyncio.run(agent.run("Hello!"))

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Microsoft Agent Framework docs

Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add alirezarezvani-landing

Add it to your agent

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

The same install from Python, for a notebook or a setup script. Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("alirezarezvani-landing")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/alirezarezvani-landing/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/alirezarezvani-landing/SKILL.md").read_text()
agent = Agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent("Hello!")

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Strands docs

Adds every file the skill has, pinned so you can update it. Cursor, Copilot, Cline and Warp read this folder by themselves.

pip install "decimalai>=0.15"
decimalai skills add alirezarezvani-landing

No Python or account. Cursor, Copilot, Cline and Warp read this folder by themselves.

Save it into your project

curl -fsSL --create-dirs -o .agents/skills/alirezarezvani-landing/SKILL.md https://app.decimal.ai/s/alirezarezvani-landing/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/alirezarezvani-landing/SKILL.md

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Cursor and other editors docs

Fetch the file and give its text to your model as instructions. No account or package needed.

Get the file

curl -fsSL https://app.decimal.ai/s/alirezarezvani-landing/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/alirezarezvani-landing.json

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v2, tested 9/1/2026
Source: alirezarezvani/claude-skills ↗
License: MIT

Manage it in DecimalAI

Saved prompts and skills, pinned versions, and traces of this skill in your runs. DecimalAI does not run your agent.

Claude Code reads files from your project, so DecimalAI config does not apply there.

On Claude Agent SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI Agents: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Anthropic: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Gemini API: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On LangChain: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On CrewAI: set DECIMAL_API_KEY and add decimalai.init(crewai=True) to record runs and skill use. The skill version stays in your repo.

On Google ADK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Pydantic AI: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Deep Agents: set DECIMAL_API_KEY and add decimalai.init(langchain=True) to record runs and skill use. The skill version stays in your repo.

On Microsoft Agent Framework: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

On Strands: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

Editors read files from your project, so DecimalAI config does not apply there.

Anywhere else: record a use with decimalai.log_skill_activation() inside a trace.

Use this skill

SKILL.md

Landing — Premium HTML Landing Page Generator

Distinct from product-team/skills/landing-page-generator/. That skill outputs Next.js TSX components optimized for conversion / lead-gen. THIS skill outputs a single self-contained .html file optimized for premium visual experience with GSAP animations. Pick by use case.

Generate a polished, self-contained .html landing page from a text prompt or brief. The output is ONE HTML file: all CSS inline in <style>, all JS inline in <script>, only external dependencies being Google Fonts + GSAP via CDN. The page is visually distinctive, animated, and production-quality.

Invocation Triggers

"create a landing page"
"build a landing page"
"make a landing page for X"
"I need a web page for Y"
"promotional page"
"product page"
"one-pager"
"web presence"
"sales page"
"landing for X"

Delivery Mode

In Claude Code CLI, write the file to disk at the specified path. In Claude.ai web, create an HTML artifact with the same content.

Phase 0: Grill-Me Intake (4 forcing questions, one at a time)

Dependency-ordered. Each question carries explicit "why I'm asking". Stop condition: max 4.

Q1 (root) — Product / Service

What's the product or service? Give me the name + a 1–2 sentence elevator pitch — what does it do, and who's it for?

Why I'm asking: The headline, subtext, and feature copy all derive from this. "App for productivity" produces generic boilerplate; "Async standup tool for remote engineering teams who hate Zoom" produces a landing page that converts.

Refuse mush. If user gives just a name with no pitch, push back once: "What does it do? Who's it for?" If still no pitch after push-back, deliver with explicit "generic positioning" caveat.

Q2 (depends on Q1) — Audience Register

Who's the audience? Pick one:

Technical buyers (engineers, ops, security)

Business buyers (PMs, execs, ops leaders)

Consumers (general public, hobbyists)

Internal (employees, partners — not for public sale)

Why I'm asking: Audience dictates copy register, jargon level, social-proof choices, and CTA framing. Technical buyers want specifics; consumers want benefits; internal pages can skip persuasion.

Forcing choice.

Q3 (always) — Brand Overrides

Brand colors / fonts to override the default (dark navy + teal + Inter)? Provide as: primary HEX, accent HEX, optional bg HEX. Or say "default" if you want the polished default.

Why I'm asking: The default is intentionally beautiful, but matching your brand makes the page feel native to your existing site. Even just a primary color override goes a long way.

Accept "default" or partial overrides (e.g., just primary). If only primary provided, derive accent algorithmically (lighten / darken).

Q4 (depends on Q1) — Tone

Tone — pick one:

Professional — confident, restrained, B2B-friendly

Playful — warm, light, occasional humor

Authoritative — expert, data-forward, trust-building

Minimal — terse, design-led, low copy density

Why I'm asking: Tone affects every sentence — headlines, microcopy, button text, closing copy. Picking upfront prevents tonal whiplash across sections.

Forcing choice. Recommended default: professional if Q2 = technical/business; playful if Q2 = consumer; minimal if the product is design-led.

Stop condition: After Q4, commit and generate. No follow-up questions during generation.

Content Extraction (with Fallback Strategy)

From Q1's elevator pitch, derive:

Hero headline — punchy version of "what it does" (8–12 words)
Hero subtext — version of "who it's for + payoff" (1–2 sentences)
3–6 feature bullets — distilled from pitch + audience (Q2) + tone (Q4)
CTA text — action-oriented, matches tone
Closing copy — short, emotive, matches tone

Fallback when input is sparse: invent compelling content from product-name semantics + audience register. Flag inferred content with a comment in the HTML source (). Don't stall waiting for more input.

Brand System Specification

Default Color Palette (Dark Navy + Teal)

:root {
  --navy:       #0A1628;
  --navy-mid:   #0D1F38;
  --teal:       #00D4AA;
  --teal-glow:  rgba(0, 212, 170, 0.12);
  --amber:      #F5A623;
  --off-white:  #F7F7F2;
  --text-muted: rgba(247, 247, 242, 0.68);
  --card-bg:    rgba(0, 212, 170, 0.06);
  --card-border:rgba(0, 212, 170, 0.15);
}

Override Pattern

When Q3 provides custom brand values, the skill substitutes them into the :root block:

Brand override:
- primary: #FF6B35    →  --navy / hero bg
- accent:  #2EC4B6    →  --teal / CTA / highlights
- bg:      #011627    →  --navy-mid / section bg
- text:    #FDFFFC    →  --off-white

If only primary provided, derive accent algorithmically (lighten 15% for accent; darken 8% for navy-mid; convert to rgba at 0.12 alpha for glow). Use scripts/brand_palette_validator.py for the deterministic derivation.

See references/brand_system_design.md for color theory + WCAG + algorithmic palette derivation canon.

Typography

Font family: Inter (via Google Fonts)
Weight scale: 400 (body), 500 (eyebrow), 600 (links), 700 (subtitle), 800 (H1 + H2)
Size scale:
- Hero H1: 68–82px
- Section H2: 52–62px
- Card titles: 22px
- Body: 17–19px
- Eyebrow: 13px (uppercase, letter-spaced)
- CTA button: 18px (500 weight)

Components (Must Specify CSS)

.btn-primary — CTA button with hover state (lift + brightness)
.feature-card — card with hover lift (translateY(-6px) + border-brighten)
.eyebrow — letter-spaced (0.2em) uppercase category label

Section 1: Hero

min-height: 100vh, flex-centered content
Optional eyebrow label above H1
H1 (68–82px, 800 weight)
Subtitle (17–19px, 1–2 sentences)
CTA button (.btn-primary)
Scroll-down indicator (animated chevron, CSS bounce)
Depth layers (mouse parallax):
- .hero-shapes-back — large blurred circles, absolute-positioned, low opacity
- .hero-shapes-mid — smaller shapes, sharper edges, higher opacity
- Content layer (H1 + subtitle) — moves subtly in same direction as mouse

Section 2: Features

3 columns default (repeat(3, 1fr) grid)
Responsive:
- 2 columns at 900px breakpoint
- 1 column at 580px breakpoint
Each card:
- SVG icon (28px, stroke=var(--teal), no fill)
- Title (22px, 700 weight)
- Description (15–16px, --text-muted)
Hover state:
- transform: translateY(-6px)
- border-color: var(--teal) (brighten from --card-border)
- transition: 0.3s ease

Section 3: Closing CTA

Full-width, background: var(--navy-mid)
padding: 120px 24px, text-align: center
Large closing headline (52–62px, 800 weight)
Short subtext (--text-muted, 1–2 sentences)

CTA button with ambient radial-gradient glow behind it:

background: radial-gradient(circle, var(--teal-glow) 0%, transparent 70%);

Animation Patterns

See references/gsap_animation_patterns.md for the canon. Five patterns required:

1. Hero Entrance (GSAP timeline)

// MUST use gsap.set() FIRST to prevent FOUC
gsap.set([".eyebrow", ".hero h1", ".hero .subtitle", ".btn-primary", ".scroll-down"], {
  opacity: 0,
  y: 30
});

const tl = gsap.timeline({ defaults: { ease: "power3.out" } });
tl.to(".eyebrow", { opacity: 1, y: 0, duration: 0.6 })
  .to(".hero h1", { opacity: 1, y: 0, duration: 0.8 }, "-=0.3")
  .to(".hero .subtitle", { opacity: 1, y: 0, duration: 0.6 }, "-=0.5")
  .to(".btn-primary", { opacity: 1, y: 0, duration: 0.5 }, "-=0.3")
  .to(".scroll-down", { opacity: 1, y: 0, duration: 0.4 }, "-=0.2");

2. Mouse Parallax

const hero = document.querySelector(".hero");
hero.addEventListener("mousemove", (e) => {
  const x = (e.clientX / window.innerWidth - 0.5) * 2;
  const y = (e.clientY / window.innerHeight - 0.5) * 2;
  gsap.to(".hero-shapes-back", { x: x * 45, y: y * 22, duration: 0.8 });
  gsap.to(".hero-shapes-mid",  { x: x * 22, y: y * 11, duration: 0.8 });
  gsap.to(".hero .container",  { x: x * 8,  y: y * 5,  duration: 0.8 });
});

3. Scroll-Triggered Feature Cards

gsap.set(".feature-card", { opacity: 0, y: 55, rotateX: 18 });

ScrollTrigger.batch(".feature-card", {
  start: "top 80%",
  onEnter: batch => gsap.to(batch, {
    opacity: 1, y: 0, rotateX: 0,
    duration: 0.8,
    stagger: 0.11,
    ease: "power2.out"
  })
});

4. Floating Decorative Shapes (CSS keyframes — NOT GSAP)

CSS handles ambient continuous motion (smoother, cheaper than GSAP for indefinite animations):

@keyframes floatA {
  0%, 100% { transform: translate(0, 0) rotate(0deg); }
  50%      { transform: translate(20px, -30px) rotate(8deg); }
}
@keyframes floatB { /* different duration + rotation */ }
@keyframes floatC { /* different duration + rotation */ }

.hero-shapes-back .shape-a { animation: floatA 12s ease-in-out infinite; }

5. Scroll Indicator (CSS bounce)

@keyframes bounce {
  0%, 100% { transform: translateY(0); }
  50%      { transform: translateY(8px); }
}
.scroll-down { animation: bounce 2s ease-in-out infinite; }

Required CDN Dependencies

<link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800&display=swap" rel="stylesheet">
<script src="https://cdnjs.cloudflare.com/ajax/libs/gsap/3.12.2/gsap.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/gsap/3.12.2/ScrollTrigger.min.js"></script>

NO other external CSS or JS files. All custom CSS in <style>, all custom JS in <script> blocks within the same HTML file.

See references/single_file_html_discipline.md for the inline-only rationale.

Layout Rules

Container max-width: 1200px, centered
Section padding: 120px 24px (vertical 120, horizontal 24, scales down on mobile)
Responsive breakpoints:
- 900px → features grid 3-col → 2-col
- 580px → all grids → 1-col; H1 scales down to ~52px
Viewport meta: <meta name="viewport" content="width=device-width, initial-scale=1">

Output Spec

Path: ${OUTPUT_DIR}/<product-name-kebab>.html
Default ${OUTPUT_DIR}: ./landing-pages/
Filename: lowercase kebab-case from product name ("Quill AI" → quill-ai.html). Use scripts/kebab_slug_generator.py for deterministic slug generation + duplicate detection.
Self-contained: all CSS in <style>, all JS in <script>, only Google Fonts + GSAP CDN external.

Validation (Post-Generation)

Run scripts/html_validator.py --file ${OUTPUT_DIR}/<slug>.html after generation. Checks:

All 3 required sections present (.hero, .features, .closing-cta)
CDN deps present (Inter + GSAP + ScrollTrigger)
gsap.set() initial states precede any gsap.timeline or gsap.to (FOUC prevention)
Responsive breakpoints at 900px + 580px
No external <link rel="stylesheet"> other than Google Fonts
No external <script src=> other than GSAP CDN
<meta name="viewport"> present
All animated elements have initial-state declarations

Error Handling

Situation	Behavior
Input is just a name with no context	Invent compelling content from name semantics + audience register; flag as `<!-- inferred -->` in HTML source
Input file is large or PDF	Read fully before generating; don't truncate
Brand colors insufficient (only 1 HEX provided)	Use as primary; derive secondary/accent algorithmically (lighten/darken via brand_palette_validator.py)
Features count not specified	Default to 4
Output dir doesn't exist	Create it
Existing file at output path	Append timestamp suffix or ask user (kebab_slug_generator.py flags duplicates)
html_validator returns FAIL	Regenerate ONLY the failing sections in one targeted pass; do NOT abandon the file

Portability

Claude Code CLI: Native — writes HTML file directly to filesystem.
Claude.ai web: Native — produces HTML as an artifact instead of file.

Tooling

Script	Role
`scripts/brand_palette_validator.py`	Validates HEX format, checks WCAG AA contrast, generates derived palette from primary (algorithmic lighten/darken).
`scripts/kebab_slug_generator.py`	Product name → kebab-case filename + duplicate detection in output dir.
`scripts/html_validator.py`	Post-generation structural check: 3 sections, CDN deps, gsap.set() initial states, responsive breakpoints, no external files.

References

references/brand_system_design.md — color theory + WCAG + algorithmic palette derivation (7+ sources)
references/gsap_animation_patterns.md — entrance timeline + ScrollTrigger reveals + mouse parallax + CSS floats + scroll indicator (7+ sources)
references/single_file_html_discipline.md — why inline + CDN-only externals + accessibility minimums + no-build rationale (7+ sources)

Anti-Patterns To Reject

Hardcoded absolute paths in output directory
Single brand palette without override documentation
Outlining before writing — write in one pass
External CSS or JS files (must be inline; only Google Fonts + GSAP CDN allowed)
Skipping gsap.set() initial states (causes FOUC)
More than 6 features in default grid (becomes unscannable)
Brand-specific content references in the skill itself

Version: 1.0.0 Source spec: megaprompts/04-landing-megaprompt.md (maintainer-local draft spec — gitignored, not present in the public repository) Build pattern: Path B (direct conversion). Distinct from product-team/skills/landing-page-generator/.

When it activates

Automatically, when a task matches. Use whenever the user says 'landing for X', 'create a landing page', 'build a landing page', 'make a landing page for X', 'I need a web page for Y', or provides

About this skill

Automatic

Frontend / UI pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v5 — 9/1/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

alirezarezvani / claude-skills

.gemini/skills/landing/SKILL.md

View on GitHub ↗

License MITImported 11/7/2026Version v2Bundled 6 files · scripts

Illustrative Want to see what alirezarezvani/landing would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

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From the same source and nearby in the catalogue.

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SkillScore

Safety

What it changes

Track record

Use this skill

Landing — Premium HTML Landing Page Generator

Invocation Triggers

Delivery Mode

Phase 0: Grill-Me Intake (4 forcing questions, one at a time)

Q1 (root) — Product / Service

Q2 (depends on Q1) — Audience Register

Q3 (always) — Brand Overrides

Q4 (depends on Q1) — Tone

Content Extraction (with Fallback Strategy)

Brand System Specification

Default Color Palette (Dark Navy + Teal)

Override Pattern

Typography

Components (Must Specify CSS)

Section 1: Hero

Section 2: Features

Section 3: Closing CTA

Animation Patterns

1. Hero Entrance (GSAP timeline)

2. Mouse Parallax

3. Scroll-Triggered Feature Cards

4. Floating Decorative Shapes (CSS keyframes — NOT GSAP)

5. Scroll Indicator (CSS bounce)

Required CDN Dependencies

Layout Rules

Output Spec

Validation (Post-Generation)

Error Handling

Portability

Tooling

References

Anti-Patterns To Reject

When it activates

SkillSafety

Source

Related skills

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