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Test Data Generation · openqa-cn | DecimalAI

Testingby openqa-cn

Test Data Generation

Constructs test data against real backends and writes it back into test cases as executable preconditions.

Never made a case worseMaintained

SkillScore

SkillScore86/100

Test results 86

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

18%86%of test cases passed +68 pts

Without skill4 of 22

With skill19 of 22

15 fixed4 passed either way3 still failing0 broke

Measured on gemini-3.8-flash11/2/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskStep 1 returned no matching skills for creating a test return merchandise authorization (RMA). We are moving to Step 2 to query the tool registry. Provide the CLI command to search for an RMA creation tool.

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 openqa-cn-codexqa-testdata-generator
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("openqa-cn-codexqa-testdata-generator")
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 openqa-cn-codexqa-testdata-generator

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/openqa-cn-codexqa-testdata-generator/SKILL.md https://app.decimal.ai/s/openqa-cn-codexqa-testdata-generator/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/openqa-cn-codexqa-testdata-generator/SKILL.md

This gets SKILL.md only. The skill's 14 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 openqa-cn-codexqa-testdata-generator

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("openqa-cn-codexqa-testdata-generator")
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/openqa-cn-codexqa-testdata-generator/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/openqa-cn-codexqa-testdata-generator/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 14 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 openqa-cn-codexqa-testdata-generator

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("openqa-cn-codexqa-testdata-generator")
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/openqa-cn-codexqa-testdata-generator/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/openqa-cn-codexqa-testdata-generator/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 14 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 openqa-cn-codexqa-testdata-generator

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("openqa-cn-codexqa-testdata-generator")
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/openqa-cn-codexqa-testdata-generator/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/openqa-cn-codexqa-testdata-generator/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 14 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 openqa-cn-codexqa-testdata-generator

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("openqa-cn-codexqa-testdata-generator")
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/openqa-cn-codexqa-testdata-generator/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/openqa-cn-codexqa-testdata-generator/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 14 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 openqa-cn-codexqa-testdata-generator

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("openqa-cn-codexqa-testdata-generator")
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/openqa-cn-codexqa-testdata-generator/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/openqa-cn-codexqa-testdata-generator/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 14 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 openqa-cn-codexqa-testdata-generator

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("openqa-cn-codexqa-testdata-generator")
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/openqa-cn-codexqa-testdata-generator/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/openqa-cn-codexqa-testdata-generator/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 14 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 openqa-cn-codexqa-testdata-generator

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("openqa-cn-codexqa-testdata-generator")
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/openqa-cn-codexqa-testdata-generator/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 14 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 openqa-cn-codexqa-testdata-generator

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("openqa-cn-codexqa-testdata-generator")
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/openqa-cn-codexqa-testdata-generator/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/openqa-cn-codexqa-testdata-generator/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 14 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 openqa-cn-codexqa-testdata-generator

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("openqa-cn-codexqa-testdata-generator")
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/openqa-cn-codexqa-testdata-generator/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/openqa-cn-codexqa-testdata-generator/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 14 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 openqa-cn-codexqa-testdata-generator

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("openqa-cn-codexqa-testdata-generator")
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/openqa-cn-codexqa-testdata-generator/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/openqa-cn-codexqa-testdata-generator/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 14 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 openqa-cn-codexqa-testdata-generator

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("openqa-cn-codexqa-testdata-generator")
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/openqa-cn-codexqa-testdata-generator/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/openqa-cn-codexqa-testdata-generator/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 14 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 openqa-cn-codexqa-testdata-generator

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("openqa-cn-codexqa-testdata-generator")
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/openqa-cn-codexqa-testdata-generator/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/openqa-cn-codexqa-testdata-generator/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 14 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 openqa-cn-codexqa-testdata-generator

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/openqa-cn-codexqa-testdata-generator/SKILL.md https://app.decimal.ai/s/openqa-cn-codexqa-testdata-generator/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/openqa-cn-codexqa-testdata-generator/SKILL.md

This gets SKILL.md only. The skill's 14 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/openqa-cn-codexqa-testdata-generator/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/openqa-cn-codexqa-testdata-generator.json

This gets SKILL.md only. The skill's 14 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 11/2/2026
Source: openqa-cn/codexqa ↗
License: Apache-2.0

Manage it in DecimalAI

Saved prompts and skills, pinned versions, and traces of this skill in your runs. DecimalAI does not run your agent.

Claude Code reads files from your project, so DecimalAI config does not apply there.

On Claude Agent SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI Agents: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Anthropic: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Gemini API: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On LangChain: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On CrewAI: set DECIMAL_API_KEY and add decimalai.init(crewai=True) to record runs and skill use. The skill version stays in your repo.

On Google ADK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Pydantic AI: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Deep Agents: set DECIMAL_API_KEY and add decimalai.init(langchain=True) to record runs and skill use. The skill version stays in your repo.

On Microsoft Agent Framework: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

On Strands: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

Editors read files from your project, so DecimalAI config does not apply there.

Anywhere else: record a use with decimalai.log_skill_activation() inside a trace.

Use this skill

SKILL.md

Test Data Generation

This directory is the skill. It has no baked-in company knowledge. Platforms are adapters; local files work with zero extra infrastructure.

This skill never invents business data. It decides which path to take and extracts parameters the user already supplied; an executor, a published tool, or a generated script is what actually calls the backend. "Construct succeeded" means the backend returned a business ID, not that an ID appeared in the reply.

SKILL_DIR is the folder that contains this SKILL.md. Every command below uses it, so resolve it once:

SKILL_DIR="<directory of this SKILL.md>"

Config lookup: $DATA_BUILD_CONFIG → ./testdata/config.yaml → ~/.testdata/config.yaml.

Four fallback paths, in this order, after the case-material check below:

Domain skill — search for a matching data-build skill, install or load it, and delegate
Existing tool — reuse a published tool from the tool registry
API catalog — discover the APIs that can build the data
Generated script — write a construction script from those APIs and run it

Documents (load on demand)

Read this file first; it is the routing contract. Load anything else only when the row below applies, so a single-step construct does not drag the whole tree into context.

File	Load when
`SKILL.md` (this file)	always: routing, priorities, guardrails
references/case-data-material-planner/SKILL.md	the request is a case-material job (see the next section)
references/workflow.md	you need the full command syntax, experience-report payloads, or the FAQ for steps 1–4
references/adapters.md	wiring an enterprise platform, or an adapter behaves unexpectedly
references/script-template.ts	writing a step-4 construction script
slot-scaffolder/SKILL.md	building a reusable domain pack from OpenAPI
slots/SLOT_SPEC.md	authoring or reviewing a slot contract by hand
`README.md` / `README.zh-CN.md`, HOW_IT_WORKS.md, INSTALL.md, KNOWN_LIMITATIONS.md (and `.zh-CN.md`)	human-facing: install, operations, method, and known gaps. Not needed by the agent

Case-material route (check first)

After receiving a data-construction request, first decide whether it is a case-material job.

Trigger phrases (any match enters the sub-skill and stops the generic flow):

English: case data, case materials, test-data preparation, write back test data, data requirements, data inventory, prepare test data, test material list
Chinese: 用例数据, 用例物料, 用例数据准备, 为用例准备数据, 用例前置数据, 测试数据回写, 用例数据构造, 测试数据分析, 测试物料, 数据需求分析, 测试数据准备, 数据物料, 需求测试数据, 准备测试数据, 测试物料清单, 数据诉求

On hit: load and follow references/case-data-material-planner/SKILL.md. Run references/case-data-material-planner/scripts/pipeline.ts as the orchestrator (paths below are relative to that sub-skill root) — construction, binding, and writeback are stages inside that script, so do not spawn Agents to do them.

The sub-skill root is <this SKILL.md directory>/references/case-data-material-planner/. All of its scripts/, agents/, templates/ paths are relative to that root.

Decision tree

Handle every other request in this exact order:

user data-construction request
    │
    ▼
[route] case-material scene? (phrases above)
    │
    ├─ yes → load references/case-data-material-planner/SKILL.md  ✓ stop
    │
    └─ no → generic flow
            │
            ▼
        [step 1] search_data_build.ts
                 (semantic proven methods + skill marketplace, in parallel)
            │
            ├─ pinned_matches present AND Agent judges them relevant
            │       → load skillPath (or install) → delegate → report  ✓ stop
            │
            ├─ proven_matches present AND Agent judges them applicable
            │       → run proven_invocation → feedback only  ✓ stop
            │
            ├─ skill_matches: one local clear match
            │       → load → delegate → report  ✓ stop
            │     several / weak / needs install → ask, then same
            │
            └─ none match
                    │
                    ▼
                [step 2] tool_registry.query (two query angles)
                    │
                    ├─ one clear tool (or user already gave an id)
                    │       → query_input_list → fill params → execute → report  ✓ stop
                    │     several / weak → ask, then same
                    │
                    └─ no matching tool
                            │
                            ▼
                        [step 3] api_catalog (in parallel when useful)
                            │
                            ├─ method A: plan change APIs (if planId exists)
                            └─ method B: keyword / service search
                            │
                            ▼
                        [step 4] write script from template
                            │
                            ├─ generate → run locally → report
                            └─ after a successful run, ask whether to publish
                                    ├─ yes → tool_registry.publish  ✓ stop
                                    └─ no  → return the result     ✓ stop

Step 1 — Search matching data-build options

Build the search input

Extract two kinds of input from the user request:

Keywords (skill-name match):

Take 1–2 nouns that name the domain slot, such as the name in slot.yaml
Never use verb phrases such as "create a" or "help me construct"
If keywords miss, the script falls back to a full scan. Do not retry by hand.

Structured query (semantic match):

registry-key: {entity}::{action}, e.g. catalog-order::create
query: one natural-language sentence
entry-type: entity or action
domain: from workspace context business_line, or infer from the request

Examples and the full command: references/workflow.md.

node "$SKILL_DIR/scripts/search_data_build.ts" \
  --keywords <nouns> \
  --query "<sentence>" \
  --registry-key "<entity>::<action>" \
  --entry-type entity \
  --domain "<domain>" \
  --json

The script runs proven-method search and skill-marketplace search in parallel, then injects pinned favorites. JSON shape:

{
  "pinned_matches": [...],
  "proven_matches": [...],
  "skill_matches": [...]
}

Result priority

Priority 0 — pinned_matches (highest)

Pinned skills are returned unconditionally so retrieval noise cannot drop them.

The Agent judges relevance from each item's description (same idea as proven applicability):

Relevant → use it first. Prefer skillPath when SKILL.md exists. If available=false or skillPath is missing, install then load.
Not relevant → ignore and continue to proven / skill matches.

The script does no keyword filter on pinned items. A relevant pinned hit ends step 1; do not evaluate proven/skill after that.

Priority 1 — proven_matches

Reuse only when all three hold:

Semantic alignment: registry_key describes the same operation
Tool reachable: the bound skill is installed or the bound tool can execute
Params available: required proven_invocation.paramMapping values exist in context

All three → execute proven_invocation and keep experience_id for feedback. Any miss → skip that row and continue to skill_matches.

Priority 2 — skill_matches

Asking is for ambiguity, not ceremony. The user already asked to construct data. Drop rows whose description does not cover the request (a catalog-product construct is not a distributor slot, even if both appear in skill_matches).

One remaining local match (skillPath already has SKILL.md) → load it, say which skill you used, and stop. Do not ask first.
Needs install (available=false or no skillPath) → ask, because install is a side effect the user did not request.
Two or more remaining rows that could both be right, or only a weak/partial match → show them and ask: "Which of these skills matches your request, if any?"

None match → go to step 2.

If search is empty and the user wants a reusable domain pack (not a one-off construct), read $SKILL_DIR/slot-scaffolder/SKILL.md and follow it. That is a bundled folder in this skill, not a separately installed skill — do not go looking for it in the host's skill list. One-off work continues at step 2.

Install and load a confirmed skill

SKILLS_DIR="$(dirname "$SKILL_DIR")"
node "$SKILL_DIR/scripts/adapters/cli.ts" skill_marketplace.install '<name>' '$SKILLS_DIR'

If skillPath already points at a directory that contains SKILL.md, load it directly and skip install. After load, follow that skill and stop.

Pin favorites (personal cheat-sheet)

Pinned skills always enter the candidate set first. Relevance is Agent-judged.

User says	Command
pin this skill	`node "$SKILL_DIR/scripts/favorites.ts" add --name "<name>" --desc "<when to use>" --path "<dir with SKILL.md>"`
show pinned	`node "$SKILL_DIR/scripts/favorites.ts" list`
unpin	`node "$SKILL_DIR/scripts/favorites.ts" rm --name "<name>"` (or `--uuid` / `--id`)
verify pinned	`node "$SKILL_DIR/scripts/favorites.ts" verify`

Storage: project ./testdata/data-build-favorites.json, user ~/.testdata/favorites.json. add writes the user file unless --scope project. $DATA_BUILD_FAVORITES_PATH overrides both.

Step 2 — Tool registry (only when no skill matched)

Query with natural-language sentences, not space-separated keywords.

Write at least two angles:

Angle	Intent	Example
A	construct / create	`create a catalog test product`
B	business flow	`customer books a standard product`

node "$SKILL_DIR/scripts/adapters/cli.ts" tool_registry.query "create a catalog test product"

If the user already has a tool id, look it up exactly:

node "$SKILL_DIR/scripts/adapters/cli.ts" tool_registry.get "<resource-id>"

User already named a tool id, or exactly one result clearly matches → use it. Tell the user which tool you picked.
Several results or only a weak match → show them and ask: "Is there a tool that matches your request?"

Then:

tool_registry.query_input_list(resource_id)
Fill params. Order IDs, user IDs, and other core business fields must be confirmed with the user. Do not invent them. Optional fields may use sane defaults.
tool_registry.execute(resource_id, params)
On success → report (see below)

Step 3 — API catalog

Method A — plan change APIs (preferred when a plan exists)

Use when the request sits inside a test-plan / change scope.

Resolve planId from workspace context testPlan.id or planId. If missing, ask the user.

node "$SKILL_DIR/scripts/adapters/cli.ts" api_catalog.search_plan_changes "<planId>"

Then api_catalog.detail(<operationId>) for key operations.

Method B — keyword / service search (fallback)

Use when there is no planId, or plan APIs are weakly related.

First: read workspace context targetRepositories[].serviceId or relatedJobs[].serviceId. If a service id is already known, list its APIs directly:

node "$SKILL_DIR/scripts/adapters/cli.ts" api_catalog.list_by_service "<serviceId>" "<optional-name-filter>"

Second: keyword search. Use one precise token, not a long Chinese/English phrase.

node "$SKILL_DIR/scripts/adapters/cli.ts" api_catalog.search "create catalog product"

Then api_catalog.detail(<operationId>).

If the workspace has no API source at all (no OpenAPI files, no planId, no serviceId), stop and ask for a plan, a service id, or an OpenAPI directory. That is a material gate, not a field-by-field param review. Do not invent endpoints.

Combination

Situation	Strategy
Has `planId`, request matches the change	A first: plan APIs → detail key ops
Has `planId`, change is weakly related	A + B: inspect the change, then search by request
No `planId`	B: service id if known, else keyword search
User already named a service / API	B: `list_by_service` or `search` + `detail`

Step 4 — Write and run a construction script

Write ./testdata/<name>.ts from references/script-template.ts, then run it.

Required shape: shebang + docstring, top-level constants, main(params) -> {success, data, error}, CLI entry via import.meta.url. Use adapter helpers only: callHttp, callSql, getConfig, callFeatureFlag.

Rules:

Child-process calls use argument lists, never shell: true
Timeouts on every network call
Coerce object / array / bool / number with typeof / Array.isArray
Do not invent order IDs, user IDs, or amounts

Feature flags (only when the user asks for experiments, drafts, or whitelists and feature_flags.type is not noop): confirm the environment before writes; production writes need a second confirmation; one subject per call. toolType for a successful flag script is feature_flag.

After a successful local run, ask whether to publish to the tool registry. Do not publish before a successful run.

node "$SKILL_DIR/scripts/adapters/cli.ts" tool_registry.publish '<name>' '<what the script does>' './testdata/<name>.ts' '[{"name":"<param>","type":"string","required":true}]'

What to tell the user

Report the business outcome, because that is the only part the user can act on. Lead with the identifiers the backend returned, then how they were produced, then anything still needed:

Constructed <what>: <field>=<value>, <field>=<value>
Path: <domain skill | registry tool | generated script> (<name>)
Next: <what the user can do with it, or what is still missing>

Keep infrastructure out of it — skillRoot, absolute paths, mock ports, node … invoke lines, and adapter names are noise to the person who asked for data, and in a case document they are actively wrong (see the writeback rules). Also say which environment produced the IDs when it was the local mock, since a mock ID looks identical to a real one but nothing landed in a real system.

If a path failed and you fell through to the next one, say so in one line rather than narrating every attempt.

Success follow-up (after any successful path)

Non-blocking. A report/feedback failure must not hide a successful construct.

When to report vs feedback:

Step 1 proven hit that ran successfully → feedback only, do not report again
Step 1 skill delegate succeeded → report
Step 2 tool execute succeeded → report
Step 4 script succeeded → report
Step 4 published after a successful run → report with the published resource id

Payloads and toolType mapping: references/workflow.md.

Never put tokens, cookies, personal identifiers, or local absolute skillRoot paths in the experience store. Consumers locate a skill by resourceId (skill name).

Pin ask (only after a step-1 skill success)

If a skill (not a tool or script) completed the request and is not already pinned, ask: "Pin <name> so later similar requests stay at the top?"

On confirm:

node "$SKILL_DIR/scripts/favorites.ts" add --uuid <uuid> --name "<name>" \
  --desc "<what it does and when to use it>" --path "<local skill dir>"

Pinning and experience reporting are independent. A declined pin does not undo the construct.

Material gates (high-level only)

Ask the user only when a prerequisite material is missing — a case pack, an API/OpenAPI source, a cross-domain product, or a slot spec. Do not stop to collect executor fields (fulfillOn, quantity, credits, rate, …). Those follow the original rules: reuse user-supplied IDs, fill optional params with defaults, do not invent core IDs.

Scenario	Prerequisite material	If missing
1. One-shot construct	None extra. Run the original 4-step fallback	—
2. Multi-step scene	Upstream domain artifact the scene cannot create (e.g. `ready-to-sell` needs a catalog product)	Ask for that material or construct it first, then finish remaining scene steps
3. Write a script from APIs	An API source: OpenAPI files, `planId`, or `serviceId`	Ask for one of those; then write/run the script with original param rules
4. Case materials	At least one case source (file, paste, `planId`, or URL)	Ask; do not parse or construct without cases. Path A waits for cases after knowledge-build
5. New domain slot	A scene skill under `slots/` (or `workspace.slot_roots`)	Ask for the skill folder or `domain` + OpenAPI to scaffold. Drop-in discover; do not invent operations

After the user supplies the material, resume and complete the remaining workflow. Do not skip later high-level steps.

Guardrails

Do not invent business identifiers.
Do not call organization-specific CLIs or private registries from this skill.
SQL is SELECT-only.
Feature-flag writes require an explicit environment and user confirmation.
Keep this file under 500 lines; load references/workflow.md on demand.

When it activates

Automatically, when a task matches. Use whenever the user wants test data built, case materials or preconditions prepared, a data-build skill or tool reused, an API discovered, a construction script written, or a proven method recorded — including Chinese phrasings such as 构造测试数据, 准备测试数据, 造数据, 用例数据, 用例物料, 用例数据准备, 用例前置数据, 测试数据回写, 测试物料清单, 数据需求分析. Also use it for concrete requests that never say "test data", like "create this ac

About this skill

Automatic

QA & Test Engineering pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v6 — 10/19/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

openqa-cn / codexqa

skills/codexqa-testdata-generator/SKILL.md

View on GitHub ↗

License Apache-2.0Imported 10/19/2026Version v1Bundled 14 files · scripts

Illustrative Want to see what openqa-cn/codexqa-testdata-generator would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

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SkillScore

Safety

What it changes

Track record

Use this skill

Test Data Generation

Documents (load on demand)

Case-material route (check first)

Decision tree

Step 1 — Search matching data-build options

Build the search input

Result priority

Install and load a confirmed skill

Pin favorites (personal cheat-sheet)

Step 2 — Tool registry (only when no skill matched)

Step 3 — API catalog

Method A — plan change APIs (preferred when a plan exists)

Method B — keyword / service search (fallback)

Combination

Step 4 — Write and run a construction script

What to tell the user

Success follow-up (after any successful path)

Pin ask (only after a step-1 skill success)

Material gates (high-level only)

Guardrails

When it activates

SkillSafety

Source

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