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HyperFrames · nousresearch | DecimalAI

Design & Mediaby nousresearch

HyperFrames

Render MP4/WebM videos from HTML compositions.

Never made a case worseMaintained

SkillScore

SkillScore77/100

Test results 77

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

41%77%of test cases passed +36 pts

Without skill9 of 22

With skill17 of 22

8 fixed9 passed either way5 still failing0 broke

Measured on gemini-3.6-flash8/17/2026v219 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskI just finished reviewing my motion graphics preview in the browser on port 3002. What system commands should I run to ensure background headless browser processes do not hog CPU cores?

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 nousresearch-hyperframes
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("nousresearch-hyperframes")
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 nousresearch-hyperframes

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/nousresearch-hyperframes/SKILL.md https://app.decimal.ai/s/nousresearch-hyperframes/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/nousresearch-hyperframes/SKILL.md

This gets SKILL.md only. The skill's 7 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 nousresearch-hyperframes

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("nousresearch-hyperframes")
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/nousresearch-hyperframes/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/nousresearch-hyperframes/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 7 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 nousresearch-hyperframes

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("nousresearch-hyperframes")
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/nousresearch-hyperframes/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/nousresearch-hyperframes/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 7 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 nousresearch-hyperframes

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("nousresearch-hyperframes")
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/nousresearch-hyperframes/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/nousresearch-hyperframes/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 7 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 nousresearch-hyperframes

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("nousresearch-hyperframes")
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/nousresearch-hyperframes/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/nousresearch-hyperframes/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 7 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 nousresearch-hyperframes

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("nousresearch-hyperframes")
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/nousresearch-hyperframes/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/nousresearch-hyperframes/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 7 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 nousresearch-hyperframes

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("nousresearch-hyperframes")
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/nousresearch-hyperframes/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/nousresearch-hyperframes/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 7 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 nousresearch-hyperframes

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("nousresearch-hyperframes")
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/nousresearch-hyperframes/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 7 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 nousresearch-hyperframes

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("nousresearch-hyperframes")
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/nousresearch-hyperframes/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/nousresearch-hyperframes/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 7 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 nousresearch-hyperframes

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("nousresearch-hyperframes")
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/nousresearch-hyperframes/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/nousresearch-hyperframes/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 7 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 nousresearch-hyperframes

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("nousresearch-hyperframes")
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/nousresearch-hyperframes/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/nousresearch-hyperframes/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 7 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 nousresearch-hyperframes

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("nousresearch-hyperframes")
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/nousresearch-hyperframes/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/nousresearch-hyperframes/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 7 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 nousresearch-hyperframes

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("nousresearch-hyperframes")
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/nousresearch-hyperframes/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/nousresearch-hyperframes/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 7 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 nousresearch-hyperframes

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/nousresearch-hyperframes/SKILL.md https://app.decimal.ai/s/nousresearch-hyperframes/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/nousresearch-hyperframes/SKILL.md

This gets SKILL.md only. The skill's 7 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/nousresearch-hyperframes/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/nousresearch-hyperframes.json

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v2, tested 8/17/2026
Source: nousresearch/hermes-agent ↗
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

HyperFrames

HTML is the source of truth for video. A composition is an HTML file with data-* attributes for timing, a GSAP timeline for animation, and CSS for appearance. The HyperFrames engine captures the page frame-by-frame and encodes to MP4/WebM with FFmpeg.

Complement to manim-video: Use manim-video for mathematical/geometric explainers (equations, 3B1B-style). Use hyperframes for motion-graphics, talking-head with captions, product tours, social overlays, shader transitions, and anything driven by real video/audio media.

When to Use

User asks for a rendered video from text, a script, or a website
Animated title cards, lower thirds, or typographic intros
Captioned narration video (TTS + captions synced to waveform)
Audio-reactive visuals (beat sync, spectrum bars, pulsing glow)
Scene-to-scene transitions (crossfade, wipe, shader warp, flash-through-white)
Social overlays (Instagram/TikTok/YouTube style)
Website-to-video pipeline (capture a URL, produce a promo)
Any HTML/CSS/JS animation that must render deterministically to a video file

Do not use this skill for:

Pure math/equation animation (→ manim-video)
Image generation or memes (→ meme-generation, image models)
Live video conferencing or streaming

Quick Reference

npx hyperframes init my-video               # scaffold a project
cd my-video
npx hyperframes lint                        # validate before preview/render
npx hyperframes preview                     # live-reload preview (long-lived server, port 3002)
npx hyperframes render --output final.mp4   # render to MP4
npx hyperframes doctor                      # diagnose environment issues

preview is a long-lived Next.js server that holds Chrome render workers open. Always stop it when done (see Cleanup) — a forgotten preview keeps idle chrome-headless-shell workers alive that, on GPU-less hosts (WSL, containers, CI), spin a CPU core each indefinitely via software WebGL (swiftshader).

Full CLI reference: references/cli.md.

Setup (one-time)

bash "$(dirname "$(find ~/.hermes/skills -path '*/hyperframes/SKILL.md' 2>/dev/null | head -1)")/scripts/setup.sh"

The script:

Verifies Node.js >= 22 and FFmpeg are installed (prints fix instructions if not).
Installs the hyperframes CLI globally (npm install -g hyperframes@>=0.4.2).
Pre-caches chrome-headless-shell via Puppeteer — required for best-quality rendering via Chrome's HeadlessExperimental.beginFrame capture path.
Runs npx hyperframes doctor and reports the result.

See references/troubleshooting.md if setup fails.

Procedure

1. Plan before writing HTML

Before touching code, articulate at a high level:

What — narrative arc, key moments, emotional beats
Structure — compositions, tracks (video/audio/overlays), durations
Visual identity — colors, fonts, motion character (explosive / cinematic / fluid / technical)
Hero frame — for each scene, the moment when the most elements are simultaneously visible. This is the static layout you'll build first.

Visual Identity Gate (HARD-GATE). Before writing ANY composition HTML, a visual identity must be defined. Do NOT write compositions with default or generic colors (#333, #3b82f6, Roboto are tells that this step was skipped). Check in order:

DESIGN.md at project root? → Use its exact colors, fonts, motion rules, and "What NOT to Do" constraints.
User named a style (e.g. "Swiss Pulse", "dark and techy", "luxury brand")? → Generate a minimal DESIGN.md with ## Style Prompt, ## Colors (3-5 hex with roles), ## Typography (1-2 families), ## What NOT to Do (3-5 anti-patterns).
None of the above? → Ask 3 questions before writing any HTML:
- Mood? (explosive / cinematic / fluid / technical / chaotic / warm)
- Light or dark canvas?
- Any brand colors, fonts, or visual references?
Then generate a DESIGN.md from the answers. Every composition must trace its palette and typography back to DESIGN.md or explicit user direction.

2. Scaffold

npx hyperframes init my-video --non-interactive

Templates: blank, warm-grain, play-mode, swiss-grid, vignelli, decision-tree, kinetic-type, product-promo, nyt-graph. Pass --example <name> to pick one, --video clip.mp4 or --audio track.mp3 to seed with media.

3. Layout before animation

Write the static HTML+CSS for the hero frame first — no GSAP yet. The .scene-content container must fill the scene (width:100%; height:100%; padding:Npx) with display:flex + gap. Use padding to push content inward — never position: absolute; top: Npx on a content container (content overflows when taller than the remaining space).

Only after the hero frame looks right, add gsap.from() entrances (animate to the CSS position) and gsap.to() exits (animate from it).

See references/composition.md for the full data-attribute schema and composition rules.

4. Animate with GSAP

Every composition must:

Register its timeline: window.__timelines["<composition-id>"] = tl
Start paused: gsap.timeline({ paused: true }) — the player controls playback
Use finite repeat values (no repeat: -1 — breaks the capture engine). Calculate: repeat: Math.ceil(duration / cycleDuration) - 1.
Be deterministic — no Math.random(), Date.now(), or wall-clock logic. Use a seeded PRNG if you need pseudo-randomness.
Build synchronously — no async/await, setTimeout, or Promises around timeline construction.

See references/gsap.md for the core GSAP API (tweens, eases, stagger, timelines).

5. Transitions between scenes

Multi-scene compositions require transitions. Rules:

Always use a transition between scenes — no jump cuts.
Always use entrance animations on every scene element (gsap.from(...)).
Never use exit animations except on the final scene — the transition IS the exit.
The final scene may fade out.

Use npx hyperframes add <transition-name> to install shader transitions (flash-through-white, liquid-wipe, etc.). Full list: npx hyperframes add --list.

6. Audio, captions, TTS, audio-reactive, highlighting

Audio: always a separate <audio> element (video is muted playsinline).
TTS: npx hyperframes tts "Script text" --voice af_nova --output narration.wav. List voices with --list. Voice ID first letter encodes language (a/b=English, e=Spanish, f=French, j=Japanese, z=Mandarin, etc.) — the CLI auto-infers the phonemizer locale; pass --lang only to override. Non-English phonemization requires espeak-ng installed system-wide.
Captions: npx hyperframes transcribe narration.wav → word-level transcript. Pick style from the transcript tone (hype / corporate / tutorial / storytelling / social — see the table in references/features.md). Language rule: never use .en whisper models unless the audio is confirmed English — .en translates non-English audio instead of transcribing it. Every caption group MUST have a hard tl.set(el, { opacity: 0, visibility: "hidden" }, group.end) kill after its exit tween — otherwise groups leak visible into later ones.
Audio-reactive visuals: pre-extract audio bands (bass / mid / treble) and sample per-frame inside the timeline with a for loop of tl.call(draw, [], f / fps) — a single long tween does NOT react to audio. Map bass → scale (pulse), treble → textShadow/boxShadow (glow), overall amplitude → opacity/y/backgroundColor. Avoid equalizer-bar clichés — let content guide the visual, audio drive its behavior.
Marker-style highlighting: highlight, circle, burst, scribble, sketchout effects for text emphasis are deterministic CSS+GSAP — see references/features.md#marker-highlighting. Fully seekable, no animated SVG filters.
Scene transitions: every multi-scene composition MUST use transitions (no jump cuts). Pick from CSS primitives (push slide, blur crossfade, zoom through, staggered blocks) or shader transitions (flash-through-white, liquid-wipe, cross-warp-morph, chromatic-split, etc.) via npx hyperframes add. Mood and energy tables live in references/features.md#transitions. Do not mix CSS and shader transitions in the same composition.

7. Lint, validate, inspect, preview, render

npx hyperframes lint              # catches missing data-composition-id, overlapping tracks, unregistered timelines
npx hyperframes validate          # WCAG contrast audit at 5 timestamps
npx hyperframes inspect           # visual layout audit — overflow, off-frame elements, occluded text
npx hyperframes preview           # live browser preview
npx hyperframes render --quality draft --output draft.mp4    # fast iteration
npx hyperframes render --quality high --output final.mp4     # final delivery

hyperframes validate samples background pixels behind every text element and warns on contrast ratios below 4.5:1 (or 3:1 for large text). hyperframes inspect is the layout-side companion — runs the page at multiple timestamps and flags issues that a static lint can't see (a caption that wraps past the safe area only at 4.5s, a card that overflows when its title is the longest variant, an element that ends up behind a transition shader). Run inspect especially on compositions with speech bubbles, cards, captions, or tight typography.

8. Website-to-video (if the user gives a URL)

Use the 7-step capture-to-video workflow in references/website-to-video.md: capture → DESIGN.md → SCRIPT.md → storyboard → composition → render → deliver.

Cleanup

render is one-shot (workers exit when it finishes). preview is not — it runs a background Next.js server that keeps Chrome workers resident until you stop it. Never leave one running: on GPU-less hosts each idle worker's swiftshader process pegs a CPU core, and a preview left open for days stacks up multiple.

Stop a preview when the user is done reviewing (or before starting a new one):

pkill -f "hyperframes.*preview"     # the Studio server (frees port 3002)
pkill -f chrome-headless-shell      # its render workers; only safe if nothing else uses them

If unsure whether other tools use chrome-headless-shell, check first: pgrep -af chrome-headless-shell. Recover a wedged host (many idle workers spinning CPU) the same way — see references/troubleshooting.md.

Pitfalls

Leaving preview running — it's a long-lived server holding Chrome workers; on WSL/containers/CI those idle workers spin a CPU core each (software WebGL). Stop it when done — see Cleanup.
HeadlessExperimental.beginFrame' wasn't found — Chromium 147+ removed this protocol. Ensure you're on hyperframes@>=0.4.2 (auto-detects and falls back to screenshot mode). Escape hatch: export PRODUCER_FORCE_SCREENSHOT=true. See hyperframes#294 and references/troubleshooting.md.
System Chrome (not chrome-headless-shell) — renders hang for 120s then timeout. Run npx puppeteer browsers install chrome-headless-shell (setup.sh does this). hyperframes doctor reports which binary will be used.
repeat: -1 anywhere — breaks the capture engine. Always compute a finite repeat count.
gsap.set() on clip elements that enter later — the element doesn't exist at page load. Use tl.set(selector, vars, timePosition) inside the timeline instead, at or after the clip's data-start.
<br> inside content text — forced breaks don't know the rendered font width, so natural wrap + <br> double-breaks. Use max-width to let text wrap. Exception: short display titles where each word is deliberately on its own line.
Animating visibility or display — GSAP can't tween these. Use autoAlpha (handles both visibility and opacity).
Calling video.play() or audio.play() — the framework owns playback. Never call these yourself.
Building timelines async — the capture engine reads window.__timelines synchronously after page load. Never wrap timeline construction in async, setTimeout, or a Promise.
Standalone index.html wrapped in <template> — hides all content from the browser. Only sub-compositions loaded via data-composition-src use <template>.
Using video for audio — always muted <video> + separate <audio>.

Verification

Before and after rendering:

Lint + validate + inspect pass: npx hyperframes lint --strict && npx hyperframes validate && npx hyperframes inspect (lint catches structural issues, validate catches contrast, inspect catches visual layout / overflow issues — see troubleshooting.md if warnings appear).
Animation choreography — for new compositions or significant animation changes, run the animation map. npx hyperframes init copies the skill scripts into the project, so the path is project-local:
```
node skills/hyperframes/scripts/animation-map.mjs <composition-dir> \
  --out <composition-dir>/.hyperframes/anim-map
```
Outputs a single animation-map.json with per-tween summaries, ASCII Gantt timeline, stagger detection, dead zones (>1s with no animation), element lifecycles, and flags (offscreen, collision, invisible, paced-fast <0.2s, paced-slow >2s). Scan summaries and flags — fix or justify each. Skip on small edits.
File exists + non-zero: ls -lh final.mp4.
Duration matches data-duration: ffprobe -v error -show_entries format=duration -of default=nw=1:nk=1 final.mp4.
Visual check: extract a mid-composition frame: ffmpeg -i final.mp4 -ss 00:00:05 -vframes 1 preview.png.
Audio present if expected: ffprobe -v error -show_streams -select_streams a -of default=nw=1:nk=1 final.mp4 | head -1.

If hyperframes render fails, run npx hyperframes doctor and attach its output when reporting.

References

composition.md — data attributes, timeline contract, non-negotiable rules, typography/asset rules
cli.md — every CLI command (init, capture, lint, validate, inspect, preview, render, transcribe, tts, doctor, browser, info, upgrade, benchmark)
gsap.md — GSAP core API for HyperFrames (tweens, eases, stagger, timelines, matchMedia)
features.md — captions, TTS, audio-reactive, marker highlighting, transitions (load on demand)
website-to-video.md — 7-step capture-to-video workflow
troubleshooting.md — OpenClaw fix, env vars, common render errors

About this skill

Automatic

Marketing & Content pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v5 — 8/17/2026

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

nousresearch / hermes-agent

optional-skills/creative/hyperframes/SKILL.md

View on GitHub ↗

License Apache-2.0Imported 8/17/2026Version v2Bundled 7 files · scripts

Illustrative Want to see what nousresearch/hyperframes 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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Use this skill

HyperFrames

When to Use

Quick Reference

Setup (one-time)

Procedure

1. Plan before writing HTML

2. Scaffold

3. Layout before animation

4. Animate with GSAP

5. Transitions between scenes

6. Audio, captions, TTS, audio-reactive, highlighting

7. Lint, validate, inspect, preview, render

8. Website-to-video (if the user gives a URL)

Cleanup

Pitfalls

Verification

References

SkillSafety

Source

Other skills for the same job

Related skills

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