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Taste · affaan-m | DecimalAI

Design & Mediaby affaan-m

Taste

A creative-direction (taste) layer for music videos and short-form edits in the angelcore / cloud-trance / hyperpop visual family. Distills a named-genre aesthetic vocabulary, a mood + color + light system, and a beat-synced editing grammar, then chains ECC's video skills (video-editing, fal-ai-media, remotion-video-creation, motion-*, content-engine) into one production pipeline.

Never made a case worse

SkillScore

SkillScore91/100

Test results 91

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

32%91%of test cases passed +59 pts

Without skill7 of 22

With skill20 of 22

13 fixed7 passed either way2 still failing0 broke

Measured on gemini-3.6-flash8/3/2026v1

Track record

Agent runs: 0; All time

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 affaan-m-taste
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("affaan-m-taste")
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 affaan-m-taste

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/affaan-m-taste/SKILL.md https://app.decimal.ai/s/affaan-m-taste/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/affaan-m-taste/SKILL.md

This gets SKILL.md only. The skill's 1 other file is 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 affaan-m-taste

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("affaan-m-taste")
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/affaan-m-taste/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/affaan-m-taste/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 1 other file is 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 affaan-m-taste

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("affaan-m-taste")
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/affaan-m-taste/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/affaan-m-taste/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 1 other file is 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 affaan-m-taste

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("affaan-m-taste")
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/affaan-m-taste/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/affaan-m-taste/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 1 other file is 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 affaan-m-taste

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("affaan-m-taste")
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/affaan-m-taste/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/affaan-m-taste/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 1 other file is 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 affaan-m-taste

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("affaan-m-taste")
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/affaan-m-taste/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/affaan-m-taste/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 1 other file is 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 affaan-m-taste

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("affaan-m-taste")
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/affaan-m-taste/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/affaan-m-taste/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 1 other file is 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 affaan-m-taste

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("affaan-m-taste")
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/affaan-m-taste/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 1 other file is 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 affaan-m-taste

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("affaan-m-taste")
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/affaan-m-taste/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/affaan-m-taste/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 1 other file is 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 affaan-m-taste

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("affaan-m-taste")
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/affaan-m-taste/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/affaan-m-taste/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 1 other file is 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 affaan-m-taste

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("affaan-m-taste")
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/affaan-m-taste/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/affaan-m-taste/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 1 other file is 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 affaan-m-taste

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("affaan-m-taste")
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/affaan-m-taste/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/affaan-m-taste/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 1 other file is 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 affaan-m-taste

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("affaan-m-taste")
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/affaan-m-taste/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/affaan-m-taste/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 1 other file is 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 affaan-m-taste

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/affaan-m-taste/SKILL.md https://app.decimal.ai/s/affaan-m-taste/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/affaan-m-taste/SKILL.md

This gets SKILL.md only. The skill's 1 other file is 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/affaan-m-taste/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/affaan-m-taste.json

This gets SKILL.md only. The skill's 1 other file is not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/3/2026
Source: affaan-m/ECC ↗
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

Taste

Most AI video advice stops at how to render frames. This skill is the layer above that: what the frames should look like, in what order, cut to what rhythm, so the result reads as one intentional thing instead of a pile of generations.

It encodes a specific taste — the angelcore / cloud-trance / hyperpop family (Bladee "Silver Surfer"-era ethereal trance crossed with heavy angelcore) — distilled from a corpus of saved Reels and a tour through a ~70-entry visual-genre library. It is opinionated on purpose. Taste is a point of view, not a menu.

The companion file references/genre-taxonomy.md holds the full named-genre catalog. This file is the actionable layer: mood, grammar, pipeline, and a beat-mapped shot plan.

When to Activate

Building a music video, lyric video, fancam, or visualizer.
Making a short-form edit / reel where the feel matters more than the information.
Driving AI b-roll generation (fal.ai, Veo, Kling, etc.) and the prompts need a coherent direction instead of one-off vibes.
Assembling a moodboard or choosing a visual genre before any rendering.
The user says "taste", "make it feel like X", "give it a direction", "angelcore", "cloud trance", "hyperpop edit", "Bladee", "dreamcore", or names a saved reference.
The current edit works but reads as flat, generic, AI-slop, or stylistically incoherent.

This skill sits on top of video-editing (the mechanics) and remotion-video-creation (the renderer). Use those for how. Use this for what and why.

Core Thesis

Taste is the last layer, and it must be decided first. video-editing correctly says taste is the final human pass. The trap: if you only decide taste at the end, every generation and cut upstream was a guess. Pick the direction before the first prompt, then let it constrain everything.
Coherence beats novelty. One look executed across 30 shots beats 30 looks. A named genre (below) is a constraint that buys coherence for free.
Cut to the song, not to the footage. In a music video the timeline is the waveform. Every hard cut lands on a beat or a transient. Frame math is in the pipeline section.
Generate selectively, edit ruthlessly. AI makes b-roll that does not exist; it does not make taste. You still throw away 80%.

The Aesthetic Vocabulary (distilled)

The reference corpus tours a large library of named visual genres. The full list lives in references/genre-taxonomy.md. The useful move is not memorizing 70 names — it is seeing that a genre name is a complete prompt-and-grade preset. When you pick one, you inherit its palette, texture, lighting, and subject matter as a unit.

The genres cluster into families. Pick a primary family and at most one accent:

Family	Genres in it	Reads as
Ethereal / divine	spiritualism, glacial folk, beacons, zen core, fairy tale	weightless, holy, glowing, soft
Hyperpop / Y2K-cyber	cyberdelia, acid house, acid nora, neo aggressano, new liquid	glossy, chrome, neon, kawaii-cyber
Dark / occult	dark academia, smoke nostalgia, communist core, abstract tech	high-contrast, ominous, grain
Retro / print	retro surfers, art deco, adventure pulp, classic advertising, magazine collage, bumper stickers	flat, graphic, halftone, nostalgic
Organic / textural	microbiology core, weaving patterns, fruitage retro, cozy blanket, pacific punk wave	tactile, macro, woven, wet
Systemic / data	numbers, mazes, code web, heatmap, pixel, 8-bit	gridded, generative, schematic

For the current project, the primary is Ethereal / divine with a Hyperpop / Y2K-cyber accent — i.e. holy light and crystalline bloom, punctuated by chrome and neon. That pairing is the angelcore × cloud-trance brief.

The Mood System — angelcore × cloud-trance

Distilled directly from the strongest reference reels. This is the concrete grade.

Palette

Base: near-black void (#05060a) and bone white (#f4f1ea). Most frames are one or the other.
Divine accent: molten gold / ember orange (#ffb24d → #ff7a18) — the one warm light in the dark.
Crystalline accent: iridescent violet→cyan→magenta bokeh (#8a6bff, #4fc3ff, #ff6ad5) — the hyperpop bloom, used in bright frames.
Danger accent (sparingly): a single glowing red (#ff2a2a) on monochrome — for one or two shock cuts only.
Hyperpop subject: candy pink hair / chrome / glossy white against blue sky.

Rule: one accent per shot. Gold lives in dark frames; iridescence lives in light frames; never both in one shot.

Light & texture

Darkness pierced by a single warm source (ember bloom, divine shaft). High contrast, deep blacks.
Crystalline / glitter bokeh, lens flares, bloom, light leaks — heavenly, not dirty.
Film grain + subtle chromatic aberration on the dark frames; clean gloss on the bright frames.
Macro detail on negative space: a hero object centered on black (key, eye, gear, petal, water).
Subjects: winged figures, clouds, halos, angels, crystalline structures, candy-cyber portraits.

Motion

Slow, floating, weightless camera (drift, slow push, slow orbit) — cloud trance.
Bursts of speed only at the drop. Otherwise everything breathes.
Particles rising (embers, dust, glitter) — upward motion = ascension.

The Editing Grammar (distilled)

From the reference edits, the techniques that recur and define the style:

Beat-locked hard cuts. No dissolves in the verse/drop. Cut on the kick. The eye should feel the BPM.
Hero-on-black macro inserts. A single sharp object centered in black negative space, held for 1–2 beats, then cut. Rhythmic montage of these = the cloud-trance signature.
Bloom / explosion reveal. A white or ember bloom that blows out the frame on a transient, then resolves into the next shot. The "divine flash" transition.
Color-pop on monochrome. Run a passage in B&W, then a single colored element (red eye, gold flame, pink hair) punches through on the downbeat.
Speed-ramp into the drop. Ramp footage from slow to fast across the last bar before the drop, hard-cut to tempo on the one.
Caption keyword highlight (for talking-head / lyric sections only). All-caps, one or two words highlighted in the accent color, synced to the vocal. Use for lyric video, not for the pure visualizer.
Reaction PiP (for explainer/edit-commentary only). Picture-in-picture talking head over b-roll. Out of scope for the music video itself; documented because the corpus uses it heavily.

Do-nots: crossfade transitions in tempo sections; more than one accent color per shot; a shot held past its musical phrase; readable on-screen UI chrome (crop it out); mixed aspect ratios in one timeline.

The Pipeline — mixing the ECC video skills

This skill is the conductor. Each ECC skill is an instrument. Do not skip layers.

0. TASTE (this skill)        decide genre + mood + grammar BEFORE anything renders
1. STRUCTURE (video-editing) map the song: timestamps for intro/verse/drop/bridge/outro
2. GENERATE (fal-ai-media)   make b-roll per genre prompt-presets; throw away 80%
3. CUT (video-editing/FFmpeg) beat-cut + reframe to 9:16; assemble selects on the grid
4. COMPOSE (remotion-video-creation) overlays, blooms, lyric text, beat-synced sequencing
5. MOTION (motion-* skills)  easing curves, light-leak/particle motion, transition timing
6. AUDIO (fal-ai-media)      transition risers/impacts to sell the cuts (track itself is in Ableton)
7. POLISH                    grade to the palette above, final pacing pass, export
8. DISTRIBUTE (content-engine) platform-native versions + caption/cover

Step	ECC skill to load	What it does here
Structure & cut	`video-editing`	FFmpeg cut/concat/reframe, EDL, scene/silence detection
Generate b-roll	`fal-ai-media`	image/video models per genre preset
Compose & overlay	`remotion-video-creation`	beat-synced `<Sequence>`s, text, blooms, masks
Motion timing	`motion-foundations`, `motion-patterns`, `motion-advanced`, `motion-ui`	easing, springs, light/particle motion
Server-side video	`videodb`	smart reframe, indexing if footage is large
Distribution	`content-engine`	per-platform cuts, covers, captions
Voice/lyric VO	`video-editing` (ElevenLabs section)	only if a spoken layer is needed

Beat Math (lock cuts to the song)

The current track is 138 BPM, B minor. Constants:

seconds_per_beat = 60 / 138 = 0.43478s
frames_per_beat = fps × 0.43478 → 24fps: 10.43, 30fps: 13.04, 60fps: 26.09
1 bar (4 beats) = 1.7391s → 30fps: 52.17 frames
8-bar phrase = 13.913s → the loop length from the track

In Remotion, snap every from={} to a beat:

const FPS = 30;
const BPM = 138;
const beat = (n: number) => Math.round(n * (60 / BPM) * FPS); // beat(n) → frame
// cut on beats 0,4,8,... :  <Sequence from={beat(0)} durationInFrames={beat(4)}> ...

Beat-Mapped Shot Plan (this music video)

The song arrangement (from the project's own notes) is Intro → Verse → Drop → Bridge → Drop → Outro (~2:05). Map taste to each section:

Section	Genre/mood lean	Grammar	Shot ideas
Intro	Ethereal/divine, near-black	slow push, no cuts	ember bloom in the void; a single shaft of gold; dust rising
Verse	Dark + macro hero-on-black	hard cuts every 2 beats	key, eye, gear, water drop, petal — rhythmic macro montage
Drop	Hyperpop bloom + crystalline	speed-ramp in, cut on the one, fast	candy-pink figure, chrome, iridescent bokeh, winged ascension
Bridge	Spiritualism, weightless	one long held shot, color-pop	clouds + halo; single red accent punches once
Drop 2	as Drop, intensify	add divine-flash blooms on transients	wings open, glitter burst, light leaks maxed
Outro	Glacial folk, cold calm	slow fade to black	crystalline structure dissolving; ember dies out

fal.ai Prompt Presets (per mood)

Use with fal-ai-media. Each preset is the genre rendered to the project palette. Append 9:16, vertical, cinematic, film grain, volumetric light, no text, no watermark to all.

Divine void: "a single molten-gold ember bloom rising in an infinite near-black void, deep shadow, one warm light source, weightless dust particles, holy, high contrast"
Macro hero: "extreme macro of an antique brass key / a human eye / interlocking gears, centered on pure black negative space, razor-sharp detail, single rim light"
Crystalline bloom: "iridescent violet-cyan-magenta crystalline bokeh, glittering light refraction, dreamy lens flares, heavenly glow, soft focus, hyperpop angelcore"
Candy-cyber portrait: "candy-pink-haired figure, glossy chrome accents, bright blue sky, Y2K hyperpop, clean gloss, saturated, kawaii-cyber"
Winged ascension: "a winged figure ascending into clouds, halo of light, bone-white and gold, volumetric god-rays, ethereal, religious iconography, soft"
Cold outro: "pale crystalline ice structure slowly dissolving, glacial folk, cold blue and bone white, minimal, calm, fading to black"

Generate 6–10 per preset, keep 2–3. For motion, animate stills with an image-to-video model or generate short clips directly; keep camera moves slow per the Motion rules.

FFmpeg Recipes (cut + reframe)

# Reframe any landscape/raw clip to 9:16 (center crop)
ffmpeg -i in.mp4 -vf "crop=ih*9/16:ih,scale=1080:1920" v.mp4

# Beat-cut a clip to exactly N beats at 138 BPM (e.g. 2 beats = 0.8696s)
ffmpeg -i in.mp4 -t 0.8696 -c copy beat2.mp4

# Concatenate beat-selects into the verse montage
for f in selects/*.mp4; do echo "file '$f'"; done > concat.txt
ffmpeg -f concat -safe 0 -i concat.txt -c copy verse.mp4

# Strip UI chrome / status bar from a screen-recorded reference (crop top+bottom)
ffmpeg -i reel.mp4 -vf "crop=iw:ih-300:0:150" clean.mp4

Remotion Composition Skeleton (beat-synced)

import { AbsoluteFill, Sequence, Video, Img, useCurrentFrame, interpolate } from "remotion";

const FPS = 30, BPM = 138;
const beat = (n: number) => Math.round(n * (60 / BPM) * FPS);

const Bloom: React.FC = () => {
  const f = useCurrentFrame();
  const o = interpolate(f, [0, 3, 12], [0, 1, 0], { extrapolateRight: "clamp" }); // divine flash on a transient
  return <AbsoluteFill style={{ background: "radial-gradient(#fff,#ffb24d)", opacity: o, mixBlendMode: "screen" }} />;
};

export const AngelcoreMV: React.FC = () => (
  <AbsoluteFill style={{ background: "#05060a" }}>
    {/* Verse: macro hero-on-black, hard cut every 2 beats */}
    <Sequence from={beat(0)} durationInFrames={beat(2)}><Video src="/selects/key.mp4" /></Sequence>
    <Sequence from={beat(2)} durationInFrames={beat(2)}><Video src="/selects/eye.mp4" /></Sequence>
    <Sequence from={beat(4)} durationInFrames={beat(2)}><Video src="/selects/gear.mp4" /></Sequence>
    {/* Drop: crystalline bloom + flash on the one */}
    <Sequence from={beat(8)} durationInFrames={beat(16)}><Video src="/selects/crystalline.mp4" /></Sequence>
    <Sequence from={beat(8)} durationInFrames={beat(1)}><Bloom /></Sequence>
  </AbsoluteFill>
);

Render: npx remotion render src/index.ts AngelcoreMV out.mp4. See remotion-video-creation for project setup, audio track binding, and render flags.

Key Principles

Decide the genre before the first generation. Pick one primary family + one accent.
One accent color per shot. Gold in the dark, iridescence in the light, red once.
Every hard cut lands on a beat. Use the beat math; no transitions in tempo sections.
Hero-on-black macro is the signature move. Master it; it carries the verses.
Generate 10, keep 2. Coherence comes from rejection, not from prompting harder.
Crop the chrome. No status bars, captions, or UI in the final frame.
Taste is decided first and judged last. Set the direction, then defend it on every cut.

Related Skills

video-editing — the mechanical pipeline (FFmpeg, reframe, EDL, polish) this sits on top of
remotion-video-creation — programmable beat-synced composition and rendering
fal-ai-media — generate the b-roll, transition SFX, and risers
motion-foundations, motion-patterns, motion-advanced, motion-ui — easing and motion timing
videodb — server-side smart reframe and indexing for large footage
content-engine — platform-native distribution, covers, captions
frontend-design-direction — the same "decide a direction first" discipline, for UI

When it activates

Automatically, when a task matches. Use when the work is not just making a video function but making it feel intentional, when building a music video, a

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 v4 — 8/1/2026

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IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

affaan-m / ECC

skills/taste/SKILL.md

View on GitHub ↗

License MITImported 8/3/2026Version v1Bundled 1 file

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

Related skills

Skills that do similar work, with their headline benchmark lift.

From the same source and nearby in the catalogue.

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What it changes

Track record

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Taste

When to Activate

Core Thesis

The Aesthetic Vocabulary (distilled)

The Mood System — angelcore × cloud-trance

Palette

Light & texture

Motion

The Editing Grammar (distilled)

The Pipeline — mixing the ECC video skills

Beat Math (lock cuts to the song)

Beat-Mapped Shot Plan (this music video)

fal.ai Prompt Presets (per mood)

FFmpeg Recipes (cut + reframe)

Remotion Composition Skeleton (beat-synced)

Key Principles

Related Skills

When it activates

SkillSafety

Source

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