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API Design Framework · aiskillstore | DecimalAI

Codingby aiskillstore

API Design Framework

Use this skill when designing REST, GraphQL, or gRPC APIs. Provides comprehensive API design patterns, versioning strategies, error handling conventions, authentication approaches, and OpenAPI/AsyncAPI templates. Ensures consistent, well-documented, and developer-friendly APIs across all backend services.

Never made a case worseMaintained

SkillScore

SkillScore91/100

Test results 91

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

87%91%of test cases passed +4 pts

Without skill20 of 23

With skill21 of 23

1 fixed20 passed either way2 still failing0 broke

Measured on gemini-3.6-flash9/2/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWe are defining a standard HTTP 422 error response structure for validation failures across our REST services. What top-level keys and structure should be used for error responses?

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 aiskillstore-api-design-framework
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("aiskillstore-api-design-framework")
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 aiskillstore-api-design-framework

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/aiskillstore-api-design-framework/SKILL.md https://app.decimal.ai/s/aiskillstore-api-design-framework/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/aiskillstore-api-design-framework/SKILL.md

This gets SKILL.md only. The skill's 2 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 aiskillstore-api-design-framework

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("aiskillstore-api-design-framework")
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/aiskillstore-api-design-framework/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/aiskillstore-api-design-framework/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.

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 aiskillstore-api-design-framework

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("aiskillstore-api-design-framework")
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/aiskillstore-api-design-framework/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/aiskillstore-api-design-framework/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.

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 aiskillstore-api-design-framework

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("aiskillstore-api-design-framework")
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/aiskillstore-api-design-framework/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/aiskillstore-api-design-framework/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 2 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 aiskillstore-api-design-framework

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("aiskillstore-api-design-framework")
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/aiskillstore-api-design-framework/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/aiskillstore-api-design-framework/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.

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 aiskillstore-api-design-framework

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("aiskillstore-api-design-framework")
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/aiskillstore-api-design-framework/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/aiskillstore-api-design-framework/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.

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 aiskillstore-api-design-framework

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("aiskillstore-api-design-framework")
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/aiskillstore-api-design-framework/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/aiskillstore-api-design-framework/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.

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 aiskillstore-api-design-framework

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("aiskillstore-api-design-framework")
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/aiskillstore-api-design-framework/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 2 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 aiskillstore-api-design-framework

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("aiskillstore-api-design-framework")
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/aiskillstore-api-design-framework/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/aiskillstore-api-design-framework/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.

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 aiskillstore-api-design-framework

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("aiskillstore-api-design-framework")
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/aiskillstore-api-design-framework/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/aiskillstore-api-design-framework/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.

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 aiskillstore-api-design-framework

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("aiskillstore-api-design-framework")
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/aiskillstore-api-design-framework/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/aiskillstore-api-design-framework/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.

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 aiskillstore-api-design-framework

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("aiskillstore-api-design-framework")
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/aiskillstore-api-design-framework/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/aiskillstore-api-design-framework/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.

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 aiskillstore-api-design-framework

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("aiskillstore-api-design-framework")
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/aiskillstore-api-design-framework/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/aiskillstore-api-design-framework/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.

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 aiskillstore-api-design-framework

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/aiskillstore-api-design-framework/SKILL.md https://app.decimal.ai/s/aiskillstore-api-design-framework/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/aiskillstore-api-design-framework/SKILL.md

This gets SKILL.md only. The skill's 2 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/aiskillstore-api-design-framework/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/aiskillstore-api-design-framework.json

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 9/2/2026
Source: aiskillstore/marketplace ↗
License: Not recorded

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

API Design Framework

Overview

This skill provides comprehensive guidance for designing robust, scalable, and developer-friendly APIs. Whether building REST, GraphQL, or gRPC services, this framework ensures consistency, usability, and maintainability.

When to use this skill:

Designing new API endpoints or services
Establishing API conventions for a team or organization
Reviewing API designs for consistency and best practices
Migrating or versioning existing APIs
Creating API documentation (OpenAPI, AsyncAPI)
Choosing between REST, GraphQL, or gRPC

API Design Principles

1. Developer Experience First

APIs should be intuitive and self-documenting:

Clear, consistent naming conventions
Predictable behavior and responses
Comprehensive documentation
Helpful error messages

2. Consistency Over Cleverness

Follow established patterns rather than inventing new ones:

Standard HTTP methods and status codes (REST)
Conventional query structures (GraphQL)
Idiomatic proto definitions (gRPC)

3. Evolution Without Breaking Changes

Design for change from day one:

API versioning strategy
Backward compatibility considerations
Deprecation policies
Migration paths

4. Performance by Design

Consider performance implications:

Pagination for large datasets
Filtering and partial responses
Caching strategies
Rate limiting

REST API Design

Resource Naming Conventions

Use plural nouns for resources:

✅ GET /users
✅ GET /users/123
✅ GET /users/123/orders

❌ GET /user
❌ GET /getUser
❌ GET /user/123

Use hierarchical relationships:

✅ GET /users/123/orders          # Orders for specific user
✅ GET /teams/5/members           # Members of specific team
✅ POST /projects/10/tasks        # Create task in project 10

❌ GET /userOrders/123            # Flat structure
❌ GET /orders?userId=123         # Query param for relationship

Use kebab-case for multi-word resources:

✅ /shopping-carts
✅ /order-items
✅ /user-preferences

❌ /shoppingCarts    (camelCase)
❌ /shopping_carts   (snake_case)
❌ /ShoppingCarts    (PascalCase)

HTTP Methods (Verbs)

Method	Purpose	Idempotent	Safe	Example
GET	Retrieve resource(s)	Yes	Yes	`GET /users/123`
POST	Create resource	No	No	`POST /users`
PUT	Replace entire resource	Yes	No	`PUT /users/123`
PATCH	Partial update	No*	No	`PATCH /users/123`
DELETE	Remove resource	Yes	No	`DELETE /users/123`
HEAD	Metadata only (no body)	Yes	Yes	`HEAD /users/123`
OPTIONS	Allowed methods	Yes	Yes	`OPTIONS /users`

*PATCH can be designed to be idempotent

Status Codes

Success (2xx)

200 OK: Successful GET, PUT, PATCH, or DELETE
201 Created: Successful POST (include Location header)
202 Accepted: Request accepted, processing async
204 No Content: Successful DELETE or PUT with no response body

Client Errors (4xx)

400 Bad Request: Invalid request body or parameters
401 Unauthorized: Missing or invalid authentication
403 Forbidden: Authenticated but not authorized
404 Not Found: Resource doesn't exist
405 Method Not Allowed: HTTP method not supported for resource
409 Conflict: Resource conflict (e.g., duplicate)
422 Unprocessable Entity: Validation failed
429 Too Many Requests: Rate limit exceeded

Server Errors (5xx)

500 Internal Server Error: Generic server error
502 Bad Gateway: Upstream service error
503 Service Unavailable: Temporary unavailability
504 Gateway Timeout: Upstream timeout

Request/Response Formats

Request Body (POST/PUT/PATCH):

POST /users
Content-Type: application/json

{
  "email": "[email protected]",
  "name": "Jane Smith",
  "role": "developer"
}

Success Response:

HTTP/1.1 201 Created
Location: /users/123
Content-Type: application/json

{
  "id": 123,
  "email": "[email protected]",
  "name": "Jane Smith",
  "role": "developer",
  "created_at": "2025-10-31T10:30:00Z",
  "updated_at": "2025-10-31T10:30:00Z"
}

Error Response (Standard Format):

HTTP/1.1 422 Unprocessable Entity
Content-Type: application/json

{
  "error": {
    "code": "VALIDATION_ERROR",
    "message": "Request validation failed",
    "details": [
      {
        "field": "email",
        "message": "Email is already registered",
        "code": "DUPLICATE_EMAIL"
      },
      {
        "field": "name",
        "message": "Name must be at least 2 characters",
        "code": "NAME_TOO_SHORT"
      }
    ],
    "timestamp": "2025-10-31T10:30:00Z",
    "request_id": "req_abc123"
  }
}

Pagination

Cursor-Based Pagination (Recommended):

GET /users?cursor=eyJpZCI6MTIzfQ&limit=20

Response:
{
  "data": [...],
  "pagination": {
    "next_cursor": "eyJpZCI6MTQzfQ",
    "has_more": true
  }
}

Pros: Consistent results even as data changes Use for: Large datasets, real-time data, infinite scroll

Offset-Based Pagination:

GET /users?page=2&per_page=20

Response:
{
  "data": [...],
  "pagination": {
    "page": 2,
    "per_page": 20,
    "total": 487,
    "total_pages": 25
  }
}

Pros: Easy to understand, supports "jump to page N" Use for: Small datasets, admin panels, known bounds

Filtering and Sorting

Filtering:

GET /users?status=active&role=developer&created_after=2025-01-01
GET /products?price_min=10&price_max=100&category=electronics

Sorting:

GET /users?sort=created_at:desc
GET /users?sort=-created_at              # Minus prefix for descending
GET /users?sort=name:asc,created_at:desc # Multiple fields

Field Selection (Partial Response):

GET /users?fields=id,name,email          # Only specified fields
GET /users/123?exclude=password_hash     # All except specified

API Versioning

Strategy 1: URI Versioning (Recommended)

✅ /api/v1/users
✅ /api/v2/users

Pros: Clear, easy to test, cache-friendly
Cons: Verbose URLs

Strategy 2: Header Versioning

GET /api/users
Accept: application/vnd.company.v2+json

Pros: Clean URLs
Cons: Harder to test, not visible in URL

Strategy 3: Query Parameter

GET /api/users?version=2

Pros: Simple
Cons: Can be forgotten, mixes with business logic params

Best Practice: URI versioning for public APIs, header versioning for internal services

Rate Limiting

Response Headers:

HTTP/1.1 200 OK
X-RateLimit-Limit: 1000
X-RateLimit-Remaining: 987
X-RateLimit-Reset: 1635724800

Response when exceeded:
HTTP/1.1 429 Too Many Requests
Retry-After: 3600

{
  "error": {
    "code": "RATE_LIMIT_EXCEEDED",
    "message": "API rate limit exceeded",
    "retry_after": 3600
  }
}

Authentication & Authorization

Bearer Token (JWT):

GET /users/me
Authorization: Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...

API Key:

GET /users
X-API-Key: sk_live_abc123...

Basic Auth (avoid for production):

GET /users
Authorization: Basic dXNlcm5hbWU6cGFzc3dvcmQ=

GraphQL API Design

Schema Design Principles

1. Nullable by Default

type User {
  id: ID!              # Non-null (required)
  email: String!       # Non-null
  name: String         # Nullable (optional)
  avatar: String       # Nullable
}

2. Use Connections for Lists

type Query {
  users(first: Int, after: String): UserConnection!
}

type UserConnection {
  edges: [UserEdge!]!
  pageInfo: PageInfo!
  totalCount: Int!
}

type UserEdge {
  node: User!
  cursor: String!
}

type PageInfo {
  hasNextPage: Boolean!
  hasPreviousPage: Boolean!
  startCursor: String
  endCursor: String
}

3. Input Types for Mutations

input CreateUserInput {
  email: String!
  name: String!
  role: UserRole!
}

type Mutation {
  createUser(input: CreateUserInput!): CreateUserPayload!
}

type CreateUserPayload {
  user: User!
  errors: [UserError!]
}

type UserError {
  field: String!
  message: String!
  code: String!
}

Query Design

Fetch single resource:

query GetUser {
  user(id: "123") {
    id
    name
    email
    posts {
      id
      title
    }
  }
}

Fetch list with filters:

query GetUsers {
  users(
    first: 10
    after: "cursor123"
    filter: { role: DEVELOPER, status: ACTIVE }
  ) {
    edges {
      node {
        id
        name
        email
      }
    }
    pageInfo {
      hasNextPage
      endCursor
    }
  }
}

Error Handling

Field-Level Errors:

type Mutation {
  createUser(input: CreateUserInput!): CreateUserPayload!
}

type CreateUserPayload {
  user: User
  errors: [UserError!]
}

Response:

{
  "data": {
    "createUser": {
      "user": null,
      "errors": [
        {
          "field": "email",
          "message": "Email is already taken",
          "code": "DUPLICATE_EMAIL"
        }
      ]
    }
  }
}

gRPC API Design

Proto File Structure

user.proto:

syntax = "proto3";

package company.user.v1;

import "google/protobuf/timestamp.proto";
import "google/protobuf/empty.proto";

// User service definition
service UserService {
  // Get user by ID
  rpc GetUser(GetUserRequest) returns (GetUserResponse);

  // List users with pagination
  rpc ListUsers(ListUsersRequest) returns (ListUsersResponse);

  // Create new user
  rpc CreateUser(CreateUserRequest) returns (CreateUserResponse);

  // Update user
  rpc UpdateUser(UpdateUserRequest) returns (UpdateUserResponse);

  // Delete user
  rpc DeleteUser(DeleteUserRequest) returns (google.protobuf.Empty);

  // Stream updates (server streaming)
  rpc WatchUsers(WatchUsersRequest) returns (stream UserEvent);
}

// Messages
message User {
  string id = 1;
  string email = 2;
  string name = 3;
  UserRole role = 4;
  google.protobuf.Timestamp created_at = 5;
  google.protobuf.Timestamp updated_at = 6;
}

enum UserRole {
  USER_ROLE_UNSPECIFIED = 0;
  USER_ROLE_ADMIN = 1;
  USER_ROLE_DEVELOPER = 2;
  USER_ROLE_VIEWER = 3;
}

message GetUserRequest {
  string id = 1;
}

message GetUserResponse {
  User user = 1;
}

message ListUsersRequest {
  int32 page_size = 1;
  string page_token = 2;
  string filter = 3;  // e.g., "role=DEVELOPER AND status=ACTIVE"
}

message ListUsersResponse {
  repeated User users = 1;
  string next_page_token = 2;
  int32 total_size = 3;
}

message CreateUserRequest {
  string email = 1;
  string name = 2;
  UserRole role = 3;
}

message CreateUserResponse {
  User user = 1;
}

Error Handling

Use gRPC status codes:

// OK: Success
// CANCELLED: Client cancelled
// INVALID_ARGUMENT: Invalid request (400 equivalent)
// NOT_FOUND: Resource not found (404 equivalent)
// ALREADY_EXISTS: Duplicate (409 equivalent)
// PERMISSION_DENIED: Forbidden (403 equivalent)
// UNAUTHENTICATED: Auth required (401 equivalent)
// RESOURCE_EXHAUSTED: Rate limit (429 equivalent)
// INTERNAL: Server error (500 equivalent)

API Documentation

OpenAPI 3.1 Structure

See /templates/openapi-template.yaml for complete example.

Key sections:

info: API metadata (title, version, description)
servers: Base URLs for different environments
paths: Endpoints with operations
components: Reusable schemas, responses, parameters
security: Authentication schemes

AsyncAPI 3.0 (Event-Driven)

For documenting message-based APIs (Kafka, RabbitMQ, WebSockets).

See /templates/asyncapi-template.yaml for complete example.

Best Practices

1. Use Standard Media Types

Content-Type: application/json          # JSON
Content-Type: application/xml           # XML
Content-Type: application/protobuf      # Protocol Buffers
Content-Type: application/octet-stream  # Binary data

2. HATEOAS (Optional for REST)

Include links for related resources:

{
  "id": 123,
  "name": "Jane Smith",
  "_links": {
    "self": { "href": "/users/123" },
    "orders": { "href": "/users/123/orders" },
    "avatar": { "href": "/users/123/avatar" }
  }
}

3. Idempotency Keys

For preventing duplicate operations:

POST /payments
Idempotency-Key: unique-request-id-123

4. Bulk Operations

POST /users/bulk-create
POST /users/bulk-update
POST /users/bulk-delete

5. Webhooks

Document webhook payloads and retry logic:

POST https://client.example.com/webhook
X-Webhook-Signature: sha256=abc123...

{
  "event": "user.created",
  "data": { ... },
  "timestamp": "2025-10-31T10:30:00Z"
}

Common Pitfalls

❌ Using verbs in URLs

Bad:  POST /createUser
Good: POST /users

❌ Inconsistent naming

Bad:  /users, /userOrders, /user_preferences
Good: /users, /orders, /preferences

❌ Ignoring HTTP methods

Bad:  POST /users/123/delete
Good: DELETE /users/123

❌ Exposing implementation details

Bad:  /users-table, /get-user-from-db
Good: /users, /users/123

❌ Generic error messages

Bad:  { "error": "Something went wrong" }
Good: { "error": { "code": "DUPLICATE_EMAIL", "message": "Email already exists" }}

Integration with Agents

Backend System Architect

Uses this framework when designing new APIs
References patterns for consistency across services
Creates OpenAPI specifications from templates

Frontend UI Developer

Reviews API contracts before implementation
Provides feedback on developer experience
Integrates with APIs following documented patterns

Code Quality Reviewer

Validates API designs against this framework
Ensures OpenAPI docs are accurate and complete
Checks for REST/GraphQL/gRPC best practices

Skill Version: 1.0.0 Last Updated: 2025-10-31 Maintained by: AI Agent Hub Team

About this skill

Automatic

Coding packapirestgraphql

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LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

aiskillstore / marketplace

skills/ariegoldkin/api-design-framework/SKILL.md

View on GitHub ↗

Imported 8/10/2026Version v1Bundled 2 files

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What it changes

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Use this skill

API Design Framework

Overview

API Design Principles

1. Developer Experience First

2. Consistency Over Cleverness

3. Evolution Without Breaking Changes

4. Performance by Design

REST API Design

Resource Naming Conventions

HTTP Methods (Verbs)

Status Codes

Success (2xx)

Client Errors (4xx)

Server Errors (5xx)

Request/Response Formats

Pagination

Filtering and Sorting

API Versioning

Strategy 1: URI Versioning (Recommended)

Strategy 2: Header Versioning

Strategy 3: Query Parameter

Rate Limiting

Authentication & Authorization

GraphQL API Design

Schema Design Principles

Query Design

Error Handling

gRPC API Design

Proto File Structure

Error Handling

API Documentation

OpenAPI 3.1 Structure

AsyncAPI 3.0 (Event-Driven)

Best Practices

1. Use Standard Media Types

2. HATEOAS (Optional for REST)

3. Idempotency Keys

4. Bulk Operations

5. Webhooks

Common Pitfalls

Integration with Agents

Backend System Architect

Frontend UI Developer

Code Quality Reviewer

SkillSafety

Source

Other skills for the same job

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