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Design System Builder — Figma MCP Skill · warpdotdev | DecimalAI

Design & Mediaby warpdotdev

Design System Builder — Figma MCP Skill

Build or update a professional-grade design system in Figma from a codebase.

Maintained

SkillScore

SkillScore77/100

Test results 77

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

36%77%of test cases passed +41 pts

Without skill8 of 22

With skill17 of 22

9 fixed8 passed either way5 still failing0 broke

Measured on gemini-3.6-flash9/27/2026v119 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskAt what phase of the design system build workflow should Code Connect mappings be finalized and verified against React component source files?

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 warpdotdev-figma-generate-library
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("warpdotdev-figma-generate-library")
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 warpdotdev-figma-generate-library

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/warpdotdev-figma-generate-library/SKILL.md https://app.decimal.ai/s/warpdotdev-figma-generate-library/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/warpdotdev-figma-generate-library/SKILL.md

This gets SKILL.md only. The skill's 16 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 warpdotdev-figma-generate-library

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("warpdotdev-figma-generate-library")
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/warpdotdev-figma-generate-library/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/warpdotdev-figma-generate-library/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 16 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 warpdotdev-figma-generate-library

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("warpdotdev-figma-generate-library")
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/warpdotdev-figma-generate-library/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/warpdotdev-figma-generate-library/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 16 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 warpdotdev-figma-generate-library

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("warpdotdev-figma-generate-library")
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/warpdotdev-figma-generate-library/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/warpdotdev-figma-generate-library/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 16 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 warpdotdev-figma-generate-library

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("warpdotdev-figma-generate-library")
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/warpdotdev-figma-generate-library/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/warpdotdev-figma-generate-library/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 16 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 warpdotdev-figma-generate-library

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("warpdotdev-figma-generate-library")
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/warpdotdev-figma-generate-library/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/warpdotdev-figma-generate-library/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 16 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 warpdotdev-figma-generate-library

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("warpdotdev-figma-generate-library")
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/warpdotdev-figma-generate-library/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/warpdotdev-figma-generate-library/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 16 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 warpdotdev-figma-generate-library

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("warpdotdev-figma-generate-library")
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/warpdotdev-figma-generate-library/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 16 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 warpdotdev-figma-generate-library

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("warpdotdev-figma-generate-library")
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/warpdotdev-figma-generate-library/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/warpdotdev-figma-generate-library/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 16 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 warpdotdev-figma-generate-library

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("warpdotdev-figma-generate-library")
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/warpdotdev-figma-generate-library/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/warpdotdev-figma-generate-library/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 16 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 warpdotdev-figma-generate-library

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("warpdotdev-figma-generate-library")
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/warpdotdev-figma-generate-library/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/warpdotdev-figma-generate-library/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 16 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 warpdotdev-figma-generate-library

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("warpdotdev-figma-generate-library")
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/warpdotdev-figma-generate-library/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/warpdotdev-figma-generate-library/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 16 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 warpdotdev-figma-generate-library

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("warpdotdev-figma-generate-library")
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/warpdotdev-figma-generate-library/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/warpdotdev-figma-generate-library/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 16 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 warpdotdev-figma-generate-library

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/warpdotdev-figma-generate-library/SKILL.md https://app.decimal.ai/s/warpdotdev-figma-generate-library/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/warpdotdev-figma-generate-library/SKILL.md

This gets SKILL.md only. The skill's 16 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/warpdotdev-figma-generate-library/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/warpdotdev-figma-generate-library.json

This gets SKILL.md only. The skill's 16 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 9/27/2026
Source: warpdotdev/warp ↗
License: AGPL-3.0

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

Design System Builder — Figma MCP Skill

Build professional-grade design systems in Figma that match code. This skill orchestrates multi-phase workflows across 20–100+ use_figma calls, enforcing quality patterns from real-world design systems (Material 3, Polaris, Figma UI3, Simple DS).

Prerequisites: The figma-use skill MUST also be loaded for every use_figma call. It provides Plugin API syntax rules (return pattern, page reset, ID return, font loading, color range). This skill provides design system domain knowledge and workflow orchestration.

Always pass skillNames: "figma-generate-library" when calling use_figma as part of this skill. This is a logging parameter — it does not affect execution.

1. The One Rule That Matters Most

This is NEVER a one-shot task. Building a design system requires 20–100+ use_figma calls across multiple phases, with mandatory user checkpoints between them. Any attempt to create everything in one call WILL produce broken, incomplete, or unrecoverable results. Break every operation to the smallest useful unit, validate, get feedback, proceed.

2. Mandatory Workflow

Every design system build follows this phase order. Skipping or reordering phases causes structural failures that are expensive to undo.

Phase 0: DISCOVERY (always first — no use_figma writes yet)
  0a. Analyze codebase → extract tokens, components, naming conventions
  0b. Inspect Figma file → pages, variables, components, styles, existing conventions
  0c. Search subscribed libraries → use search_design_system for reusable assets
  0d. Lock v1 scope → agree on exact token set + component list before any creation
  0e. Map code → Figma → resolve conflicts (code and Figma disagree = ask user)
  ✋ USER CHECKPOINT: present full plan, await explicit approval

Phase 1: FOUNDATIONS (tokens first — always before components)
  1a. Create variable collections and modes
  1b. Create primitive variables (raw values, 1 mode)
  1c. Create semantic variables (aliased to primitives, mode-aware)
  1d. Set scopes on ALL variables
  1e. Set code syntax on ALL variables
  1f. Create effect styles (shadows) and text styles (typography)
  → Exit criteria: every token from the agreed plan exists, all scopes set, all code syntax set
  ✋ USER CHECKPOINT: show variable summary, await approval

Phase 2: FILE STRUCTURE (before components)
  2a. Create page skeleton: Cover → Getting Started → Foundations → --- → Components → --- → Utilities
  2b. Create foundations documentation pages (color swatches, type specimens, spacing bars)
  → Exit criteria: all planned pages exist, foundations docs are navigable
  ✋ USER CHECKPOINT: show page list + screenshot, await approval

Phase 3: COMPONENTS (one at a time — never batch)
  For EACH component (in dependency order: atoms before molecules):
    3a. Create dedicated page
    3b. Build base component with auto-layout + full variable bindings
    3c. Create all variant combinations (combineAsVariants + grid layout)
    3d. Add component properties (TEXT, BOOLEAN, INSTANCE_SWAP)
    3e. Link properties to child nodes
    3f. Add page documentation (title, description, usage notes)
    3g. Validate: get_metadata (structure) + get_screenshot (visual)
    3h. Optional: lightweight Code Connect mapping while context is fresh
    → Exit criteria: variant count correct, all bindings verified, screenshot looks right
    ✋ USER CHECKPOINT per component: show screenshot, await approval before next component

Phase 4: INTEGRATION + QA (final pass)
  4a. Finalize all Code Connect mappings
  4b. Accessibility audit (contrast, min touch targets, focus visibility)
  4c. Naming audit (no duplicates, no unnamed nodes, consistent casing)
  4d. Unresolved bindings audit (no hardcoded fills/strokes remaining)
  4e. Final review screenshots of every page
  ✋ USER CHECKPOINT: complete sign-off

3. Critical Rules

Plugin API basics (from use_figma skill — enforced here too):

Use return to send data back (auto-serialized). Do NOT wrap in IIFE or call closePlugin.
Return ALL created/mutated node IDs in every return value
Page context resets each call — always await figma.setCurrentPageAsync(page) at start
figma.notify() throws — never use it
Colors are 0–1 range, not 0–255
Font MUST be loaded before any text write: await figma.loadFontAsync({family, style})

Design system rules:

Variables BEFORE components — components bind to variables. No token = no component.
Inspect before creating — run read-only use_figma to discover existing conventions. Match them.
One page per component (default) — exception: tightly related families (e.g., Input + helpers) may share a page with clear section separation.
Bind visual properties to variables (default) — fills, strokes, padding, radius, gap. Exceptions: intentionally fixed geometry (icon pixel-grid sizes, static dividers).
Scopes on every variable — NEVER leave as ALL_SCOPES. Background: FRAME_FILL, SHAPE_FILL. Text: TEXT_FILL. Border: STROKE_COLOR. Spacing: GAP. Radii: CORNER_RADIUS. Primitives: [] (hidden).
Code syntax on every variable — WEB syntax MUST use the var() wrapper: var(--color-bg-primary), not --color-bg-primary. Use the actual CSS variable name from the codebase. ANDROID/iOS do NOT use a wrapper.
Alias semantics to primitives — { type: 'VARIABLE_ALIAS', id: primitiveVar.id }. Never duplicate raw values in semantic layer.
Position variants after combineAsVariants — they stack at (0,0). Manually grid-layout + resize.
INSTANCE_SWAP for icons — never create a variant per icon. Cap variant matrices: if Size × Style × State > 30 combinations, split into sub-component.
Deterministic naming — use consistent, unique node names for idempotent cleanup and resumability. Track created node IDs via return values and the state ledger.
No destructive cleanup — cleanup scripts identify nodes by name convention or returned IDs, not by guessing.
Validate before proceeding — never build on unvalidated work. get_metadata after every create, get_screenshot after each component.
NEVER parallelize use_figma calls — Figma state mutations must be strictly sequential. Even if your tool supports parallel calls, never run two use_figma calls simultaneously.
Never hallucinate Node IDs — always read IDs from the state ledger returned by previous calls. Never reconstruct or guess an ID from memory.
Use the helper scripts — embed scripts from scripts/ into your use_figma calls. Don't write 200-line inline scripts from scratch.
Explicit phase approval — at each checkpoint, name the next phase explicitly. "looks good" is not approval to proceed to Phase 3 if you asked about Phase 1.

4. State Management (Required for Long Workflows)

getPluginData() / setPluginData() are NOT supported in use_figma. Use getSharedPluginData() / setSharedPluginData() instead (these ARE supported), or use name-based lookups and the state ledger (returned IDs).

Entity type	Idempotency key	How to check existence
Scene nodes (pages, frames, components)	`setSharedPluginData('dsb', 'key', value)` or unique name	`node.getSharedPluginData('dsb', 'key')` or `page.findOne(n => n.name === 'Button')`
Variables	Name within collection	`(await figma.variables.getLocalVariablesAsync()).find(v => v.name === name && v.variableCollectionId === collId)`
Styles	Name	`getLocalTextStyles().find(s => s.name === name)`

Tag every created scene node immediately after creation:

node.setSharedPluginData('dsb', 'run_id', RUN_ID);        // identifies this build run
node.setSharedPluginData('dsb', 'phase', 'phase3');        // which phase created it
node.setSharedPluginData('dsb', 'key', 'component/button');// unique logical key

State persistence: Do NOT rely solely on conversation context for the state ledger. Write it to disk:

/tmp/dsb-state-{RUN_ID}.json

Re-read this file at the start of every turn. In long workflows, conversation context will be truncated — the file is the source of truth.

Maintain a state ledger tracking:

{
  "runId": "ds-build-2024-001",
  "phase": "phase3",
  "step": "component-button",
  "entities": {
    "collections": { "primitives": "id:...", "color": "id:..." },
    "variables": { "color/bg/primary": "id:...", "spacing/sm": "id:..." },
    "pages": { "Cover": "id:...", "Button": "id:..." },
    "components": { "Button": "id:..." }
  },
  "pendingValidations": ["Button:screenshot"],
  "completedSteps": ["phase0", "phase1", "phase2", "component-avatar"]
}

Idempotency check before every create: query by name + state ledger ID. If exists, skip or update — never duplicate.

Resume protocol: at session start or after context truncation, run a read-only use_figma to scan all pages, components, variables, and styles by name to reconstruct the {key → id} map. Then re-read the state file from disk if available.

Continuation prompt (give this to the user when resuming in a new chat):

"I'm continuing a design system build. Run ID: {RUN_ID}. Load the figma-generate-library skill and resume from the last completed step."

5. search_design_system — Reuse Decision Matrix

Search FIRST in Phase 0, then again immediately before each component creation.

search_design_system({ query, fileKey, includeComponents: true, includeVariables: true, includeStyles: true })

Reuse if all of these are true:

Component property API matches your needs (same variant axes, compatible types)
Token binding model is compatible (uses same or aliasable variables)
Naming conventions match the target file
Component is editable (not locked in a remote library you don't own)

Rebuild if any of these:

API incompatibility (different property names, wrong variant model)
Token model incompatible (hardcoded values, different variable schema)
Ownership issue (can't modify the library)

Wrap if visual match but API incompatible:

Import the library component as a nested instance inside a new wrapper component
Expose a clean API on the wrapper

Three-way priority: local existing → subscribed library import → create new.

6. User Checkpoints

Mandatory. Design decisions require human judgment.

After	Required artifacts	Ask
Discovery + scope lock	Token list, component list, gap analysis	"Here's my plan. Approve before I create anything?"
Foundations	Variable summary (N collections, M vars, K modes), style list	"All tokens created. Review before file structure?"
File structure	Page list + screenshot	"Pages set up. Review before components?"
Each component	get_screenshot of component page	"Here's [Component] with N variants. Correct?"
Each conflict (code ≠ Figma)	Show both versions	"Code says X, Figma has Y. Which wins?"
Final QA	Per-page screenshots + audit report	"Complete. Sign off?"

If user rejects: fix before moving on. Never build on rejected work.

7. Naming Conventions

Match existing file conventions. If starting fresh:

Variables (slash-separated):

color/bg/primary     color/text/secondary    color/border/default
spacing/xs  spacing/sm  spacing/md  spacing/lg  spacing/xl  spacing/2xl
radius/none  radius/sm  radius/md  radius/lg  radius/full
typography/body/font-size    typography/heading/line-height

Primitives: blue/50 → blue/900, gray/50 → gray/900

Component names: Button, Input, Card, Avatar, Badge, Checkbox, Toggle

Variant names: Property=Value, Property=Value — e.g., Size=Medium, Style=Primary, State=Default

Page separators: --- (most common) or ——— COMPONENTS ———

Full naming reference: naming-conventions.md

8. Token Architecture

Complexity	Pattern
< 50 tokens	Single collection, 2 modes (Light/Dark)
50–200 tokens	Standard: Primitives (1 mode) + Color semantic (Light/Dark) + Spacing (1 mode) + Typography (1 mode)
200+ tokens	Advanced: Multiple semantic collections, 4–8 modes (Light/Dark × Contrast × Brand). See M3 pattern in token-creation.md

Standard pattern (recommended starting point):

Collection: "Primitives"    modes: ["Value"]
  blue/500 = #3B82F6, gray/900 = #111827, ...

Collection: "Color"         modes: ["Light", "Dark"]
  color/bg/primary → Light: alias Primitives/white, Dark: alias Primitives/gray-900
  color/text/primary → Light: alias Primitives/gray-900, Dark: alias Primitives/white

Collection: "Spacing"       modes: ["Value"]
  spacing/xs = 4, spacing/sm = 8, spacing/md = 16, ...

9. Per-Phase Anti-Patterns

Phase 0 anti-patterns:

❌ Starting to create anything before scope is locked with user
❌ Ignoring existing file conventions and imposing new ones
❌ Skipping search_design_system before planning component creation

Phase 1 anti-patterns:

❌ Using ALL_SCOPES on any variable
❌ Duplicating raw values in semantic layer instead of aliasing
❌ Not setting code syntax (breaks Dev Mode and round-tripping)
❌ Creating component tokens before agreeing on token taxonomy

Phase 2 anti-patterns:

❌ Skipping the cover page or foundations docs
❌ Putting multiple unrelated components on one page

Phase 3 anti-patterns:

❌ Creating components before foundations exist
❌ Hardcoding any fill/stroke/spacing/radius value in a component
❌ Creating a variant per icon (use INSTANCE_SWAP instead)
❌ Not positioning variants after combineAsVariants (they all stack at 0,0)
❌ Building variant matrix > 30 without splitting (variant explosion)
❌ Importing remote components then immediately detaching them

General anti-patterns:

❌ Retrying a failed script without understanding the error first
❌ Using name-prefix matching for cleanup (deletes user-owned nodes)
❌ Building on unvalidated work from the previous step
❌ Skipping user checkpoints to "save time"
❌ Parallelizing use_figma calls (always sequential)
❌ Guessing/hallucinating node IDs from memory (always read from state ledger)
❌ Writing massive inline scripts instead of using the provided helper scripts
❌ Starting Phase 3 because the user said "build the button" without completing Phases 0-2

10. Reference Docs

Load on demand — each reference is authoritative for its phase:

Use your file reading tool to read these docs when needed. Do not assume their contents from the filename.

Doc	Phase	Required / Optional	Load when
discovery-phase.md	0	Required	Starting any build — codebase analysis + Figma inspection
token-creation.md	1	Required	Creating variables, collections, modes, styles
documentation-creation.md	2	Required	Creating cover page, foundations docs, swatches
component-creation.md	3	Required	Creating any component or variant
code-connect-setup.md	3–4	Required	Setting up Code Connect or variable code syntax
naming-conventions.md	Any	Optional	Naming anything — variables, pages, variants, styles
error-recovery.md	Any	Required on error	Script fails, multi-step workflow recovery, cleanup of abandoned workflow state

11. Scripts

Reusable Plugin API helper functions. Embed in use_figma calls:

Script	Purpose
inspectFileStructure.js	Discover all pages, components, variables, styles; returns full inventory
createVariableCollection.js	Create a named collection with modes; returns `{collectionId, modeIds}`
createSemanticTokens.js	Create aliased semantic variables from a token map
createComponentWithVariants.js	Build a component set from a variant matrix; handles grid layout
bindVariablesToComponent.js	Bind design tokens to all component visual properties
createDocumentationPage.js	Create a page with title + description + section structure
validateCreation.js	Verify created nodes match expected counts, names, structure
cleanupOrphans.js	Remove orphaned nodes by name convention or state ledger IDs
rehydrateState.js	Scan file for all pages, components, variables by name; returns full `{key → nodeId}` map for state reconstruction

When it activates

Automatically, when a task matches. Use when the user wants to create variables/tokens, build component libraries, set up theming (light/dark modes), document foundations, or reconcile gaps between code and Figma. This skill teaches WHAT to build and in WHAT ORDER — it complements the `figma-use` skill which teaches HOW to call the Plugin API. Both skills should be loaded together.

About this skill

Automatic

Design & UX Review pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v5 — 8/31/2026

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

warpdotdev / warp

resources/bundled/mcp_skills/figma/figma-generate-library/SKILL.md

View on GitHub ↗

License AGPL-3.0Imported 8/31/2026Version v1Bundled 16 files · scripts

Illustrative Want to see what warpdotdev/figma-generate-library 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

Design System Builder — Figma MCP Skill

1. The One Rule That Matters Most

2. Mandatory Workflow

3. Critical Rules

4. State Management (Required for Long Workflows)

5. search_design_system — Reuse Decision Matrix

6. User Checkpoints

7. Naming Conventions

8. Token Architecture

9. Per-Phase Anti-Patterns

10. Reference Docs

11. Scripts

When it activates

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