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Get Started Free →Use this skill alongside figma-use when the task involves translating an application page, view, or multi-section layout into Figma. Triggers: 'write to Figma', 'create in Figma from code', 'push page to Figma', 'take this app/page and build it in Figma', 'create a screen', 'build a landing page in Figma', 'update the Figma screen to match code', 'convert this modal/dialog/drawer/panel to Figma'. This is the preferred workflow skill whenever the user wants to build or update a full page, modal,
SkillScore67/100
Test results 67
3/3 safety checks passed
25%67%of test cases passed +42 pts
Passed 16 of 24 test cases with the skill, and 6 of 24 without it.
11 fixed5 passed either way7 still failing1 broke
One test taskWe are invoking `use_figma` inside a script to build a modal dialog view for skill `devin-axis/figma-generate-design` loaded via an MCP resource. What parameter must be passed to `use_figma`?
Adds every file the skill has, pinned so you can update it. No account needed.
pip install "decimalai>=0.15"
decimalai skills add devin-axis-figma-generate-design
claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"The same install from Python, for a notebook or a setup script.
pip install "decimalai>=0.15"from decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("devin-axis-figma-generate-design")
if result.exit_code:
raise SystemExit(result.problems)
print("Added. Start Claude Code with the command below.")claude --plugin-dir "$(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.
/plugin marketplace add decimal-labs/decimalai-claude-plugins
/plugin install decimalai@decimalai/decimalai:install devin-axis-figma-generate-designThe plugin needs python3 and curl on your machine.
No Python or account. Claude Code picks it up in its next session.
curl -fsSL --create-dirs -o .claude/skills/devin-axis-figma-generate-design/SKILL.md https://app.decimal.ai/s/devin-axis-figma-generate-design/SKILL.md.claude/skills/devin-axis-figma-generate-design/SKILL.mdThis gets SKILL.md only. The skill's 2 other files are not included.
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.
pip install "decimalai[claude-agent-sdk]"
decimalai skills add devin-axis-figma-generate-designimport 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.
pip install "decimalai[claude-agent-sdk]"from decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("devin-axis-figma-generate-design")
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.
.agents/skills/devin-axis-figma-generate-design/SKILL.mdimport asyncio
from pathlib import Path
from claude_agent_sdk import ClaudeAgentOptions, query
skill = Path(".agents/skills/devin-axis-figma-generate-design/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 2 other files are not included.
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.
pip install "decimalai[openai-agents]"
decimalai skills add devin-axis-figma-generate-designfrom 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.
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.
pip install "decimalai[openai-agents]"from decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("devin-axis-figma-generate-design")
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.
.agents/skills/devin-axis-figma-generate-design/SKILL.mdfrom pathlib import Path
from agents import Agent, Runner
skill = Path(".agents/skills/devin-axis-figma-generate-design/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 2 other files are not included.
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.
pip install "decimalai[openai]"
decimalai skills add devin-axis-figma-generate-designimport 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.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.
pip install "decimalai[openai]"from decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("devin-axis-figma-generate-design")
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.
.agents/skills/devin-axis-figma-generate-design/SKILL.mdfrom pathlib import Path
from openai import OpenAI
client = OpenAI()
skill = Path(".agents/skills/devin-axis-figma-generate-design/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].messageThis gets SKILL.md only. The skill's 2 other files are not included.
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.
pip install "decimalai[anthropic]"
decimalai skills add devin-axis-figma-generate-designimport 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()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.
pip install "decimalai[anthropic]"from decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("devin-axis-figma-generate-design")
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.
.agents/skills/devin-axis-figma-generate-design/SKILL.mdfrom pathlib import Path
import anthropic
client = anthropic.Anthropic()
skill = Path(".agents/skills/devin-axis-figma-generate-design/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 2 other files are not included.
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.
pip install "decimalai[google-genai]"
decimalai skills add devin-axis-figma-generate-designfrom 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)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.
pip install "decimalai[google-genai]"from decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("devin-axis-figma-generate-design")
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.
.agents/skills/devin-axis-figma-generate-design/SKILL.mdfrom pathlib import Path
from google import genai
from google.genai import types
client = genai.Client()
skill = Path(".agents/skills/devin-axis-figma-generate-design/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 2 other files are not included.
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.
pip install "decimalai[langchain]" langchain-openai
decimalai skills add devin-axis-figma-generate-designfrom 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!"}]})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.
pip install "decimalai[langchain]" langchain-openaifrom decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("devin-axis-figma-generate-design")
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.
.agents/skills/devin-axis-figma-generate-design/SKILL.mdfrom pathlib import Path
from langchain.agents import create_agent
skill = Path(".agents/skills/devin-axis-figma-generate-design/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 2 other files are not included.
CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.
pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add devin-axis-figma-generate-designimport 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.
pip install "decimalai>=0.16" "crewai>=1.15.27"from decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("devin-axis-figma-generate-design")
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.
.agents/skills/devin-axis-figma-generate-design/SKILL.mdfrom 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 2 other files are not included.
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.
pip install "decimalai[adk]"
decimalai skills add devin-axis-figma-generate-designfrom 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.
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.
pip install "decimalai[adk]"from decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("devin-axis-figma-generate-design")
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.
.agents/skills/devin-axis-figma-generate-design/SKILL.mdfrom pathlib import Path
from google.adk.agents import LlmAgent
skill = Path(".agents/skills/devin-axis-figma-generate-design/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 2 other files are not included.
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.
pip install "decimalai[pydantic-ai]"
decimalai skills add devin-axis-figma-generate-designfrom 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.
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.
pip install "decimalai[pydantic-ai]"from decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("devin-axis-figma-generate-design")
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.
.agents/skills/devin-axis-figma-generate-design/SKILL.mdfrom pathlib import Path
from pydantic_ai import Agent
skill = Path(".agents/skills/devin-axis-figma-generate-design/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 2 other files are not included.
Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.
pip install "decimalai>=0.16" deepagents
decimalai skills add devin-axis-figma-generate-designimport 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.
pip install "decimalai>=0.16" deepagentsfrom decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("devin-axis-figma-generate-design")
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.
.agents/skills/devin-axis-figma-generate-design/SKILL.mdfrom pathlib import Path
from deepagents import create_deep_agent
skill = Path(".agents/skills/devin-axis-figma-generate-design/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 2 other files are not included.
Its own skill loader reads the installed folder. No DecimalAI account needed.
pip install "decimalai>=0.16" agent-framework
decimalai skills add devin-axis-figma-generate-designimport 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.
pip install "decimalai>=0.16" agent-frameworkfrom decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("devin-axis-figma-generate-design")
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.
.agents/skills/devin-axis-figma-generate-design/SKILL.mdimport asyncio
from pathlib import Path
from agent_framework import Agent
skill = Path(".agents/skills/devin-axis-figma-generate-design/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 2 other files are not included.
Strands' own skill loader reads the installed folder. No DecimalAI account needed.
pip install "decimalai>=0.16" strands-agents
decimalai skills add devin-axis-figma-generate-designfrom 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.
pip install "decimalai>=0.16" strands-agentsfrom decimalai import skillpkg
# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("devin-axis-figma-generate-design")
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.
.agents/skills/devin-axis-figma-generate-design/SKILL.mdfrom pathlib import Path
from strands import Agent
skill = Path(".agents/skills/devin-axis-figma-generate-design/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 2 other files are not included.
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 devin-axis-figma-generate-designNo Python or account. Cursor, Copilot, Cline and Warp read this folder by themselves.
curl -fsSL --create-dirs -o .agents/skills/devin-axis-figma-generate-design/SKILL.md https://app.decimal.ai/s/devin-axis-figma-generate-design/SKILL.md.agents/skills/devin-axis-figma-generate-design/SKILL.mdThis gets SKILL.md only. The skill's 2 other files are not included.
Give its text to your model as instructions. No account or package needed.
This gets SKILL.md only. The skill's 2 other files are not included.
Raw files: /s/devin-axis-figma-generate-design/SKILL.md · /s/devin-axis-figma-generate-design.json
Fetch the file and give its text to your model as instructions. No account or package needed.
curl -fsSL https://app.decimal.ai/s/devin-axis-figma-generate-design/SKILL.mdcurl -fsSL https://app.decimal.ai/s/devin-axis-figma-generate-design.jsonThis gets SKILL.md only. The skill's 2 other files are not included.
You don't need a DecimalAI account.
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 to create or update screens, views, and multi-section UI containers in Figma by reusing the published design system — components, variables, and styles — rather than drawing primitives with hardcoded values. This includes full pages, modals, dialogs, drawers, sidebars, panels, and any composed view with multiple sections. The key insight: the Figma file likely has a published design system with components, color/spacing variables, and text/effect styles that correspond to the codebase's UI components and tokens. Find and use those instead of drawing boxes with hex colors.
MANDATORY: You MUST also load figma-use before any use_figma call. That skill contains critical rules (color ranges, font loading, etc.) that apply to every script you write.
Always include figma-generate-design in the comma-separated skillNames parameter when calling use_figma as part of this skill. If this skill was loaded via an MCP resource, you MUST prefix the name with resource: (e.g. resource:figma-generate-design). This is a logging parameter — it does not affect execution.
fileKey). If they don't have one yet, invoke /figma-create-new-file (or call create_new_file) first and reuse the returned file_key. Both use_figma and generate_figma_design require an existing fileKey.When building a screen from a web app that can be rendered in a browser, the best results come from running both approaches in parallel:
fileKey).generate_figma_design against the same fileKey to capture a pixel-perfect screenshot of the running web app into that file. generate_figma_design always requires fileKey — if the user does not yet have a Figma file, first invoke /figma-create-new-file (or call the create_new_file MCP tool) to get one, and reuse that file_key for both this skill and the capture.generate_figma_design capture. The capture provides the exact spacing, sizing, and visual treatment to aim for, while your use_figma output has proper component instances linked to the design system. If the capture contains images, transfer them to your use_figma output by copying imageHash values from the capture's image fills (see Step 5 for details).generate_figma_design output — it was only used as a visual reference.This combines the best of both: generate_figma_design gives pixel-perfect layout accuracy, while use_figma gives proper design system component instances that stay linked and updatable.
This parallel workflow is MANDATORY when the source contains images. The use_figma Plugin API cannot fetch external image URLs — it can only set image fills by copying imageHash values from nodes already in the file. generate_figma_design rasterizes all visible images into Figma, providing the hashes you need. If you skip the capture when images are present, image frames will be left blank.
For non-web apps (iOS, Android, etc.) or when updating existing screens, use the standard workflow below.
Follow these steps in order. Do not skip steps.
Hard gates — forbidden shortcuts:
- Forbidden:
search_design_systemfor component keys until 2a-i is complete and 2a-ii is attempted or logged N/A (e.g. "empty file, no existing screens").- Forbidden: Any
use_figmacall that mutates the canvas (Step 3+) until all Step 2 rows in the checklist below are filled in.
Before touching Figma, understand what you're building:
<img>, <Image>, background images, product photos, avatars, icons loaded from URLs). If it does and this is a web app, you must run the parallel generate_figma_design capture workflow — start it immediately alongside Step 2 so the capture runs while you discover components. See "Parallel Workflow with generate_figma_design" above.You need three things from the design system: components (buttons, cards, etc.), variables (colors, spacing, radii), and styles (text styles, effect styles like shadows). Don't hardcode hex colors or pixel values when design system tokens exist.
2a-i — REQUIRED: Check Code Connect for needed components. Starting from the component list you built in Step 1, check whether each component has a Code Connect file in the codebase. Code Connect files live next to the component source and are named by platform:
*.figma.ts, *.figma.js*.figma.tsx.kt files containing @FigmaConnect.swift files containing FigmaConnectFor each component you need (e.g., Button, Card, Input), search for its Code Connect file — glob or grep by component name (e.g., **/Button.figma.tsx, **/Card.figma.ts). Only read files that match components you actually need.
From each matching Code Connect file, extract the Figma component URL. Parse fileKey and nodeId from the URL (convert hyphens to colons: 123-456 → 123:456). Then resolve component keys via use_figma:
Example: Code Connect file contains // url=https://figma.com/design/ABC123/File?node-id=609-35535. Parse fileKey = ABC123, nodeId = 609:35535. Run use_figma against the library file (fileKey ABC123, not the target file) to resolve the key:
const node = await figma.getNodeByIdAsync("609:35535");
const set = node?.parent?.type === "COMPONENT_SET" ? node.parent : node;
return { componentKey: set.key };
Batch multiple lookups in a single call. Use the returned keys with importComponentSetByKeyAsync() in Step 4.
Mark resolved components. If all components are resolved, skip 2a-ii and 2a-iii. If none of the needed components have Code Connect files, proceed to 2a-ii.
2a-ii — REQUIRED if unresolved components remain: Inspect existing screens. Check if the target file already contains screens using the same design system. A single use_figma call that walks an existing frame's instances gives you an exact, authoritative component map:
const frame = figma.currentPage.findOne(n => n.name === "Existing Screen");
const uniqueSets = new Map();
frame.findAllWithCriteria({ types: ["INSTANCE"] }).forEach(inst => {
const mc = inst.mainComponent;
const cs = mc?.parent?.type === "COMPONENT_SET" ? mc.parent : null;
const key = cs ? cs.key : mc?.key;
const name = cs ? cs.name : mc?.name;
if (key && !uniqueSets.has(key)) {
uniqueSets.set(key, { name, key, isSet: !!cs, sampleVariant: mc.name });
}
});
return [...uniqueSets.values()];
Match results against your unresolved components. Mark any newly resolved. If all components are resolved, skip 2a-iii.
2a-iii — LAST RESORT: search_design_system. Only if components remain unresolved after completing both 2a-i and 2a-ii.
Before searching, call get_libraries to discover which libraries are available for the file. This returns two lists: libraries already added to the file and libraries available to add (community UI kits and org libraries). Each entry includes a libraryKey you can pass to search_design_system via the includeLibraryKeys param to scope your search to specific libraries instead of searching across everything.
// Step 1: Discover available libraries
get_libraries({ fileKey })
// Returns: {
// libraries_added_to_file: [...],
// libraries_available_to_add: [...],
// libraries_available_to_add_next_offset: number | null
// }
// Step 2: Search within a specific library using its libraryKey
search_design_system({ query: "button", fileKey, includeLibraryKeys: ["lk-abc123..."] })
Org libraries in libraries_available_to_add are paginated (20 per page). When libraries_available_to_add_next_offset is non-null, more org libraries are available — call get_libraries again with offset set to that value to fetch the next page. Community UI kits only appear on the first page. If the user names a specific library you don't see in the current page, page further before giving up.
This is especially useful when the file has many libraries and you want targeted results (e.g. searching only within "iOS 26" or "Material 3" instead of getting matches from every library).
Search broadly — try multiple terms and synonyms (e.g., "button", "input", "nav", "card", "accordion", "header", "footer", "tag", "avatar", "toggle", "icon", etc.). Use includeComponents: true to focus on components.
Include component properties in your map — you need to know which TEXT properties each component exposes for text overrides. Create a temporary instance, read its componentProperties (and those of nested instances), then remove the temp instance.
Example component map with property info:
Component Map:
- Button → key: "abc123", type: COMPONENT_SET
Properties: { "Label#2:0": TEXT, "Has Icon#4:64": BOOLEAN }
- PricingCard → key: "ghi789", type: COMPONENT_SET
Properties: { "Device": VARIANT, "Variant": VARIANT }
Nested "Text Heading" has: { "Text#2104:5": TEXT }
Nested "Button" has: { "Label#2:0": TEXT }
Inspect existing screens first (same as components). Or use search_design_system with includeVariables: true.
WARNING: Two different variable discovery methods — do not confuse them.
use_figmawithfigma.variables.getLocalVariableCollectionsAsync()— returns only local variables defined in the current file. If this returns empty, it does not mean no variables exist. Remote/published library variables are invisible to this API.search_design_systemwithincludeVariables: true— searches across all linked libraries, including remote and published ones. This is the correct tool for discovering design system variables.Never conclude "no variables exist" based solely on
getLocalVariableCollectionsAsync()returning empty. Always also runsearch_design_systemwithincludeVariables: trueto check for library variables before deciding to create your own.
Query strategy: search_design_system matches against variable names (e.g., "Gray/gray-9", "core/gray/100", "space/400"), not categories. Run multiple short, simple queries in parallel rather than one compound query:
If initial searches return empty, try shorter fragments or different naming conventions — libraries vary widely ("grey" vs "gray", "spacing" vs "space", "color/bg" vs "background").
Inspect an existing screen's bound variables for the most authoritative results:
const frame = figma.currentPage.findOne(n => n.name === "Existing Screen");
// boundVariables can live on any scene node — enumerating every scene type
// just to feed findAllWithCriteria is roughly the same as findAll(() => true)
// and is much noisier in script output.
const uniqueIds = new Set(
frame.findAll(() => true).flatMap(n =>
Object.values(n.boundVariables ?? {})
.flatMap(b => Array.isArray(b) ? b : [b])
.map(b => b?.id)
.filter(Boolean)
)
);
const variables = await Promise.all(
[...uniqueIds].map(id => figma.variables.getVariableByIdAsync(id))
);
return variables
.filter(Boolean)
.map(v => ({ name: v.name, id: v.id, key: v.key, type: v.resolvedType, remote: v.remote }));
For library variables (remote = true), import them by key with figma.variables.importVariableByKeyAsync(key). For local variables, use figma.variables.getVariableByIdAsync(id) directly.
See variable-patterns.md for binding patterns.
Search for styles using search_design_system with includeStyles: true and terms like "heading", "body", "shadow", "elevation". Or inspect what an existing screen uses:
const frame = figma.currentPage.findOne(n => n.name === "Existing Screen");
const styles = { text: new Map(), effect: new Map() };
for (const node of frame.findAll(() => true)) {
// textStyleId is on TEXT and TEXT_PATH; effectStyleId is on most scene
// shape/container types. Use `in` guards to handle both without an
// exhaustive type list.
if ('textStyleId' in node && node.textStyleId) {
const s = figma.getStyleById(node.textStyleId);
if (s) styles.text.set(s.id, { name: s.name, id: s.id, key: s.key });
}
if ('effectStyleId' in node && node.effectStyleId) {
const s = figma.getStyleById(node.effectStyleId);
if (s) styles.effect.set(s.id, { name: s.name, id: s.id, key: s.key });
}
}
return {
textStyles: [...styles.text.values()],
effectStyles: [...styles.effect.values()]
};
Import library styles with figma.importStyleByKeyAsync(key), then apply with node.textStyleId = style.id or node.effectStyleId = style.id.
See text-style-patterns.md and effect-style-patterns.md for details.
Do NOT build sections as top-level page children and reparent them later — moving nodes across use_figma calls with appendChild() silently fails and produces orphaned frames. Instead, create the wrapper first, then build each section directly inside it.
Create the wrapper in its own use_figma call. Position it away from existing content and return its ID:
// Find clear space
let maxX = 0;
for (const child of figma.currentPage.children) {
maxX = Math.max(maxX, child.x + child.width);
}
const wrapper = figma.createAutoLayout("VERTICAL");
// --- Size the wrapper based on container type ---
// Full page: wrapper.resize(1440, 100); wrapper.name = "Homepage";
// Modal/dialog: wrapper.resize(640, 100); wrapper.name = "Settings Modal";
// Drawer/sidebar: wrapper.resize(360, 100); wrapper.name = "Navigation Drawer";
// Panel: wrapper.resize(400, 100); wrapper.name = "Details Panel";
// Adapt width to match the source code's actual dimensions.
wrapper.name = "VIEW_NAME";
wrapper.primaryAxisAlignItems = "CENTER";
wrapper.counterAxisAlignItems = "CENTER";
wrapper.resize(WIDTH, 100);
wrapper.layoutSizingHorizontal = "FIXED";
wrapper.x = maxX + 200;
wrapper.y = 0;
return { success: true, wrapperId: wrapper.id };
This is the most important step. Build one section at a time, each in its own use_figma call. At the start of each script, fetch the wrapper by ID and append new content directly to it.
const createdNodeIds = [];
// Resolve the wrapper and import every design system dependency in parallel.
// Sequential awaits here serialize N independent IPC round-trips at the top
// of every section build; one Promise.all is dramatically faster.
const [wrapper, buttonSet, bgColorVar, spacingVar, shadowStyle] = await Promise.all([
figma.getNodeByIdAsync("WRAPPER_ID_FROM_STEP_3"),
figma.importComponentSetByKeyAsync("BUTTON_SET_KEY"),
figma.variables.importVariableByKeyAsync("BG_COLOR_VAR_KEY"),
figma.variables.importVariableByKeyAsync("SPACING_VAR_KEY"),
figma.importStyleByKeyAsync("SHADOW_STYLE_KEY"),
]);
const primaryButton = buttonSet.children.find(c =>
c.type === "COMPONENT" && c.name.includes("variant=primary")
) || buttonSet.defaultVariant;
// Build section frame with variable bindings (not hardcoded values)
const section = figma.createAutoLayout();
section.name = "Header";
section.setBoundVariable("paddingLeft", spacingVar);
section.setBoundVariable("paddingRight", spacingVar);
const bgPaint = figma.variables.setBoundVariableForPaint(
{ type: 'SOLID', color: { r: 0, g: 0, b: 0 } }, 'color', bgColorVar
);
section.fills = [bgPaint];
// Apply the effect style imported above
section.effectStyleId = shadowStyle.id;
// Create component instances inside the section
const btnInstance = primaryButton.createInstance();
section.appendChild(btnInstance);
createdNodeIds.push(btnInstance.id);
// Append section to wrapper
wrapper.appendChild(section);
section.layoutSizingHorizontal = "FILL"; // AFTER appending
createdNodeIds.push(section.id);
return { success: true, createdNodeIds };
After each section, validate with get_screenshot before moving on. Look closely for cropped/clipped text (line heights cutting off content) and overlapping elements — these are the most common issues and easy to miss at a glance.
Component instances ship with placeholder text ("Title", "Heading", "Button"). Use the component property keys you discovered in Step 2 to override them with setProperties() — this is more reliable than direct node.characters manipulation. See component-patterns.md for the full pattern.
For nested instances that expose their own TEXT properties, call setProperties() on the nested instance:
// Use the type-indexed criteria for the type filter, then narrow by name.
const nestedHeading = cardInstance
.findAllWithCriteria({ types: ["INSTANCE"] })
.find(n => n.name === "Text Heading");
if (nestedHeading) {
nestedHeading.setProperties({ "Text#2104:5": "Actual heading from source code" });
}
Only fall back to direct node.characters for text that is NOT managed by any component property.
When translating code components to Figma instances, check the component's default prop values in the source code, not just what's explicitly passed. For example, <Button size="small">Register</Button> with no variant prop — check the component definition to find variant = "primary" as the default. Selecting the wrong variant (e.g., Neutral instead of Primary) produces a visually incorrect result that's easy to miss.
| Build manually | Import from design system |
|---|---|
| Wrapper frame | Components: buttons, cards, inputs, nav, etc. |
| Section container frames | Variables: colors (fills, strokes), spacing (padding, gap), radii |
| Layout grids (rows, columns) | Text styles: heading, body, caption, etc. |
| Effect styles: shadows, blurs, etc. |
Never hardcode hex colors or pixel spacing when a design system variable exists. Use setBoundVariable for spacing/radii and setBoundVariableForPaint for colors. Apply text styles with node.textStyleId and effect styles with node.effectStyleId.
Componentization is part of the default workflow, not an optional follow-up. Produce a componentized structure on the first pass; do not emit a flat tree of one-off frames and wait for a second "now make it componentized" prompt.
figma.createComponent() and place instances, instead of hand-building N near-identical frames. One source component maps to one Figma main component.See references/componentization.md for the build-once-place-instances pattern and code.
Icons are the main exception to the build-manually-vs-import split above. If the design system exposes an icon as a component, instance it (a single INSTANCE_SWAP property, not a variant per icon). Otherwise — most commonly when grabbing an icon from the codebase to place or replace it in Figma — import the icon's SVG source directly as a vector node. This is the primary, default path for icons; do not redraw them.
<svg>, the imported .svg asset, or the icon-library entry — and pass that exact SVG string. Prefer the codebase's own SVG over hand-authoring one.figma.createNodeFromSvg(svgString), which returns a FrameNode of editable vector paths. The SVG string must include a viewBox plus explicit width/height (e.g. <svg width="24" height="24" viewBox="0 0 24 24" ...>). Without width/height it falls back to the viewBox size, which is often smaller than the slot — the usual cause of "the icon didn't size properly."createNodeFromSvg frames scale their contents on resize, so icon.resize(size, size) fits the whole icon (stroke weight included) to the target box. Equivalently author width/height equal to the target. Match the source's icon size — commonly 16/20/24px.use_figma context and produces broken, mis-rotated icons (a chevron collapses into a blob, an arrowhead detaches from its shaft). Importing the SVG is both more reliable and more editable.// Place / replace an icon from a codebase SVG into a 24px slot
const icon = figma.createNodeFromSvg(
'<svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">' +
'<path d="m9 18 6-6-6-6" stroke="#1A1A1A" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/></svg>'
);
icon.name = "icon/chevron-right";
icon.resize(24, 24); // scales the whole icon to the slot
slotFrame.appendChild(icon);
Codebase SVGs usually use currentColor (e.g. stroke="currentColor" / fill="currentColor"), which createNodeFromSvg imports as black — it does not inherit the parent's color. Set the intended color after import: substitute the literal color into the SVG string before importing, or bind the imported vector fills/strokes to a design-system color variable with setBoundVariableForPaint (same as any paint). To turn an imported SVG into a reusable icon component (for INSTANCE_SWAP), see figma-generate-library → Creating Icon Components and the INSTANCE_SWAP pattern.
After composing all sections, call get_screenshot on the wrapper frame and compare against the source. Fix any issues with targeted use_figma calls — don't rebuild the entire view.
Screenshot individual sections, not just the full view. A full-view screenshot at reduced resolution hides text truncation, wrong colors, and placeholder text that hasn't been overridden. Take a screenshot of each section by node ID to catch:
generate_figma_design capture (see below)You MUST explicitly assert that rendered text uses the product font(s) identified in Step 1, and treat any mismatch as a failed validation. Do not assume a successfully loaded font is the correct font: loading Inter when the product uses SF Pro is a failure even if no errors occur. If you have a source reference (the running web app, a design mock, or the generate_figma_design capture), you MUST also compare rendered screenshots — a near-miss style within the right family can pass a family check but still look wrong.
See references/discover-product-font.md for the read-back script (which separates free-standing text you fix from design-system-governed text you flag) and the screenshot-comparison steps.
If you ran generate_figma_design in parallel (mandatory when the source contains images), transfer the captured images into your design system output:
type === "IMAGE":
const capture = await figma.getNodeByIdAsync("CAPTURE_NODE_ID");
const imageNodes = capture.findAll(() => true).flatMap(n => {
if (!Array.isArray(n.fills)) return [];
const imageFill = n.fills.find(f => f.type === "IMAGE");
return imageFill ? [{ name: n.name, id: n.id, imageHash: imageFill.imageHash }] : [];
});
return imageNodes;
targetFrame.fills = [{ type: "IMAGE", imageHash: "hash_from_capture", scaleMode: "FILL" }];
generate_figma_design capture output after all images are transferred.When updating rather than creating from scratch:
get_metadata to inspect the existing screen structure.get_screenshot after each modification.// Example: Swap a button variant in an existing screen.
// Batch the node lookup and component-set import in parallel — they are
// independent and awaiting them sequentially serializes two IPC round-trips.
const [existingButton, buttonSet] = await Promise.all([
figma.getNodeByIdAsync("EXISTING_BUTTON_INSTANCE_ID"),
figma.importComponentSetByKeyAsync("BUTTON_SET_KEY"),
]);
if (existingButton && existingButton.type === "INSTANCE") {
const newVariant = buttonSet.children.find(c =>
c.name.includes("variant=primary") && c.name.includes("size=lg")
) || buttonSet.defaultVariant;
existingButton.swapComponent(newVariant);
}
return { success: true, mutatedNodeIds: [existingButton.id] };
For detailed API patterns and gotchas, load these from the figma-use references as needed:
Follow the error recovery process from figma-use:
get_metadata or get_screenshot to inspect the current file state.Because this skill works incrementally (one section per call), errors are naturally scoped to a single section. Previous sections from successful calls remain intact.
use_figma call.get_screenshot to catch issues early.Published skills whose descriptions closely match this one, with their headline benchmark lift.
Skills that do similar work, with their headline benchmark lift.
From the same source and nearby in the catalogue.