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Spring Batch · rrezartprebreza | DecimalAI

Codingby rrezartprebreza

Spring Batch

Use when building batch jobs, ETL pipelines, scheduled imports/exports, or any chunk-oriented bulk processing with Spring Batch. Covers the Spring Batch 5 / Boot 3 builder API, restartability and idempotent job parameters, reader/writer thread-safety, fault tolerance, and chunk transaction boundaries.

Never made a case worseMaintained

SkillScore

SkillScore100/100

Test results 100

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

75%100%of test cases passed +25 pts

Without skill18 of 24

With skill24 of 24

6 fixed18 passed either way0 still failing0 broke

Model 9/2/2026v2 · latest is v3

This benchmark measured an earlier version of the skill.

A newer attempt on gemini-3.8-flash on 10/21/2026 did not produce a publishable comparison (graded differently). The snapshot above is the previously measured run.

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskOur team is configuring a new Spring Boot 4 application for nightly batch data processing. We require job execution history and restartability after crashes to be stored in PostgreSQL audit tables. Which Spring Boot starter dependency should we add to our pom.xml file?

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 rrezartprebreza-spring-batch
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("rrezartprebreza-spring-batch")
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 rrezartprebreza-spring-batch

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/rrezartprebreza-spring-batch/SKILL.md https://app.decimal.ai/s/rrezartprebreza-spring-batch/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/rrezartprebreza-spring-batch/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 rrezartprebreza-spring-batch

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("rrezartprebreza-spring-batch")
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/rrezartprebreza-spring-batch/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/rrezartprebreza-spring-batch/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 rrezartprebreza-spring-batch

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("rrezartprebreza-spring-batch")
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/rrezartprebreza-spring-batch/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/rrezartprebreza-spring-batch/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 rrezartprebreza-spring-batch

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("rrezartprebreza-spring-batch")
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/rrezartprebreza-spring-batch/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/rrezartprebreza-spring-batch/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 rrezartprebreza-spring-batch

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("rrezartprebreza-spring-batch")
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/rrezartprebreza-spring-batch/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/rrezartprebreza-spring-batch/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 rrezartprebreza-spring-batch

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("rrezartprebreza-spring-batch")
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/rrezartprebreza-spring-batch/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/rrezartprebreza-spring-batch/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 rrezartprebreza-spring-batch

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("rrezartprebreza-spring-batch")
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/rrezartprebreza-spring-batch/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/rrezartprebreza-spring-batch/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 rrezartprebreza-spring-batch

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("rrezartprebreza-spring-batch")
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/rrezartprebreza-spring-batch/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 rrezartprebreza-spring-batch

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("rrezartprebreza-spring-batch")
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/rrezartprebreza-spring-batch/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/rrezartprebreza-spring-batch/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 rrezartprebreza-spring-batch

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("rrezartprebreza-spring-batch")
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/rrezartprebreza-spring-batch/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/rrezartprebreza-spring-batch/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 rrezartprebreza-spring-batch

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("rrezartprebreza-spring-batch")
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/rrezartprebreza-spring-batch/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/rrezartprebreza-spring-batch/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 rrezartprebreza-spring-batch

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("rrezartprebreza-spring-batch")
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/rrezartprebreza-spring-batch/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/rrezartprebreza-spring-batch/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 rrezartprebreza-spring-batch

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("rrezartprebreza-spring-batch")
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/rrezartprebreza-spring-batch/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/rrezartprebreza-spring-batch/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 rrezartprebreza-spring-batch

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/rrezartprebreza-spring-batch/SKILL.md https://app.decimal.ai/s/rrezartprebreza-spring-batch/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/rrezartprebreza-spring-batch/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/rrezartprebreza-spring-batch/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/rrezartprebreza-spring-batch.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: v3 (v2 tested 9/2/2026)
Source: rrezartprebreza/spring-boot-skills ↗
License: MIT

Manage it in DecimalAI

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Use this skill

SKILL.md

Spring Batch

Spring Boot 4.x ships Spring Batch 6. The API changed significantly from 5.x (and drastically from 4.x) — most online examples are wrong. The rules that break the most agent-generated code:

Do NOT add @EnableBatchProcessing. Boot auto-configures the JobRepository, JobOperator, and transaction manager. Adding @EnableBatchProcessing disables that auto-configuration and you lose all the wired beans.
Metadata is in-memory by default. Batch 6's JobRepository is resourceless — nothing is persisted. Restartability and the BATCH_* audit tables require the spring-boot-starter-batch-jdbc starter (plain spring-boot-starter-batch = no restart after a crash).
JobLauncher is folded into JobOperator (which extends it), and JobExplorer is deprecated: JobRepository now extends it — launch through JobOperator, query executions/instances through JobRepository. Inject JobOperator and call start(job, params).
JobBuilderFactory/StepBuilderFactory are long gone, and chunk(500, txManager) is the old Batch 5 style — Batch 6 takes the size alone, with an optional .transactionManager(...).

Dependencies

<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-batch-jdbc</artifactId> <!-- persistent BATCH_* metadata -->
</dependency>
<!-- spring-boot-starter-batch alone = resourceless in-memory repository:
     fine for run-and-forget jobs, but no restart-on-failure, no audit trail -->

Job & Step (Spring Batch 6 API)

@Configuration
@RequiredArgsConstructor
public class OrderExportJobConfig {

    @Bean
    public Job orderExportJob(JobRepository jobRepository, Step exportStep) {
        return new JobBuilder("orderExportJob", jobRepository)
            .incrementer(new RunIdIncrementer()) // lets the same job be re-run; see "Idempotency"
            .start(exportStep)
            .build();
    }

    @Bean
    public Step exportStep(JobRepository jobRepository,
                           PlatformTransactionManager txManager, // Boot's, injected — do NOT new one up
                           ItemReader<Order> reader,
                           ItemProcessor<Order, OrderRow> processor,
                           ItemWriter<OrderRow> writer) {
        return new StepBuilder("exportStep", jobRepository)
            .<Order, OrderRow>chunk(500)        // chunk size is the commit interval — and a TX boundary
            .transactionManager(txManager)      // optional in Batch 6 — but set it for JDBC-backed steps
            .reader(reader)
            .processor(processor)
            .writer(writer)
            .faultTolerant()
            .skip(FlatFileParseException.class)
            .skipLimit(50)
            .build();
    }
}

chunk(500) means: read 500 items, process each, hand the list of 500 to the writer, commit one transaction, repeat. The chunk is the unit of restart and the unit of rollback. The Batch 5 form chunk(500, txManager) is deprecated — size and transaction manager are now separate builder calls.

Idempotency & Restartability — the #1 operational gotcha

A JobInstance is identified by its identifying JobParameters. Launch the same job with the same identifying parameters twice and you get:

JobInstanceAlreadyCompleteException: A job instance already exists and is complete

This is by design — Batch refuses to re-run completed work. Two ways to handle it:

// Option A — RunIdIncrementer on the job (above) + JobLauncherApplicationRunner bumps run.id each launch.
// Option B — add a unique identifying parameter yourself when launching:
JobParameters params = new JobParametersBuilder()
    .addString("status", "COMPLETED")              // identifying — part of the instance key
    .addLong("run.id", System.currentTimeMillis()) // identifying & unique — makes each run a new instance
    .toJobParameters();

Mark a parameter non-identifying with the false flag when it's metadata that shouldn't change the instance identity (e.g. a request id you log but don't key on):

.addString("requestId", requestId, false) // non-identifying — excluded from the instance key

(In Batch 6 JobParameter is an immutable record that carries its own name — JobParameters holds a Set<JobParameter> — but the builder above is unchanged.)

A failed job, by contrast, is resumed when relaunched with the same parameters — it skips completed steps and restarts the failed step from the last committed chunk. That is the point of the metadata tables — and it only works with the JDBC job repository; the default resourceless repository forgets everything when the JVM exits. Don't defeat it by always passing a unique parameter if you want resume-on-failure.

ItemReader — sort key and thread-safety

@Bean
@StepScope // required: late-binds jobParameters at step execution, not context startup
public JpaPagingItemReader<Order> orderReader(
        EntityManagerFactory emf,
        @Value("#{jobParameters['status']}") String status) {
    return new JpaPagingItemReaderBuilder<Order>()
        .name("orderReader")
        .entityManagerFactory(emf)
        .queryString("SELECT o FROM Order o WHERE o.status = :status ORDER BY o.id") // ORDER BY is MANDATORY
        .parameterValues(Map.of("status", OrderStatus.valueOf(status)))
        .pageSize(500) // keep pageSize == chunk size
        .build();
}

Paging readers require a deterministic ORDER BY on a unique column. Without it the DB returns rows in arbitrary order across pages → rows get skipped or processed twice. This is silent data corruption, not an error.
JdbcCursorItemReader is NOT thread-safe. JdbcPagingItemReader / JpaPagingItemReader are safe for multi-threaded steps. For a non-thread-safe reader in a multi-threaded step, wrap it in SynchronizedItemStreamReader.
Don't mutate the column you page on inside the same job. If the writer flips status from PENDING to DONE while the reader pages WHERE status = 'PENDING' ORDER BY id, the result set shifts under you and pages are missed. Read into a stable snapshot, page by immutable id, or use a cursor reader.

ItemProcessor — returning null filters

@Component
public class OrderProcessor implements ItemProcessor<Order, OrderRow> {
    @Override
    public OrderRow process(Order order) {
        if (order.getTotal().isZero()) {
            return null; // ⚠️ null = FILTER this item; it is NOT written and NOT an error
        }
        return OrderRow.from(order);
    }
}

Returning null silently drops the item from the chunk. That's a feature (filtering) but a footgun if you returned null by accident expecting it to pass through.

ItemWriter — Chunk, not List

Since Batch 5 the writer receives a Chunk<? extends T>, not List<? extends T>:

@Override
public void write(Chunk<? extends OrderRow> chunk) { // was List<? extends T> in 4.x
    repository.saveAll(chunk.getItems());
}

For SQL writes, prefer the batched JDBC writer over per-row saves — it uses one addBatch():

@Bean
public JdbcBatchItemWriter<OrderRow> orderWriter(DataSource dataSource) {
    return new JdbcBatchItemWriterBuilder<OrderRow>()
        .dataSource(dataSource)
        .sql("INSERT INTO order_export (id, total) VALUES (:id, :total)")
        .beanMapped()
        .build();
}

The writer runs inside the chunk transaction. Never fire emails, publish to Kafka, or call webhooks from a writer — if the chunk rolls back you've already sent it. Bind side effects to the job completion instead (see [[transactional-patterns]] and the listener below).

Launching jobs

Boot runs every Job bean on startup by default. For scheduled or on-demand jobs, turn that off and launch explicitly:

spring:
  batch:
    job:
      enabled: false           # don't run jobs on app startup; we trigger them ourselves
    jdbc:
      initialize-schema: never # (batch-jdbc starter) manage BATCH_* tables with Flyway in prod

@Component
@RequiredArgsConstructor
public class OrderExportScheduler {

    private final JobOperator jobOperator; // Batch 6: JobOperator extends JobLauncher
    private final Job orderExportJob;

    @Scheduled(cron = "0 0 2 * * *")
    public void runNightly() throws JobExecutionException {
        JobParameters params = new JobParametersBuilder()
            .addString("status", "COMPLETED")
            .addLong("run.id", System.currentTimeMillis())
            .toJobParameters();
        jobOperator.start(orderExportJob, params);
    }
}

Use start(Job, JobParameters) — the old start(String jobName, Properties) overload is deprecated for removal. The default JobOperator is synchronous — start(...) blocks the @Scheduled thread until the whole job finishes. For fire-and-forget, configure it with an async TaskExecutor (or annotate a @Bean method with @BatchTaskExecutor), or trigger from a request thread only if you accept the block.

Metadata schema in production

With the JDBC repository, Spring Batch needs its BATCH_JOB_INSTANCE, BATCH_JOB_EXECUTION, BATCH_STEP_EXECUTION, … tables. initialize-schema: always is fine for dev/embedded DBs but don't let Batch DDL your production database on startup. Set initialize-schema: never and ship the schema as a versioned [[flyway-migrations]] migration (the canonical DDL lives in org/springframework/batch/core/schema-*.sql inside spring-batch-core). Upgrading an existing Boot 3 database? Batch 6 renamed the BATCH_JOB_SEQ sequence to BATCH_JOB_INSTANCE_SEQ — the project ships migration scripts; add one to your Flyway history.

Listeners — work that must run after the job, not per chunk

@Bean
public Job orderExportJob(JobRepository jobRepository, Step exportStep) {
    return new JobBuilder("orderExportJob", jobRepository)
        .incrementer(new RunIdIncrementer())
        .listener(new JobExecutionListener() {
            @Override public void afterJob(JobExecution exec) {
                if (exec.getStatus() == BatchStatus.COMPLETED) {
                    notifier.notifyExportReady(exec.getJobParameters()); // safe: all chunks committed
                }
            }
        })
        .start(exportStep)
        .build();
}

Don't reach for Batch when you don't need it

Spring Batch earns its complexity (metadata tables, restart, chunking) on large, restartable, auditable bulk jobs. For a quick one-off async task, @Async or a @Scheduled loop is lighter. For durable background jobs with retry, a job queue is a better fit. Match the tool to the scale.

Gotchas

Agent adds @EnableBatchProcessing — on Boot it disables auto-config; remove it, just inject JobRepository
Agent uses plain spring-boot-starter-batch and expects restart/audit — Batch 6's default repository is resourceless (in-memory); use spring-boot-starter-batch-jdbc for the BATCH_* tables
Agent uses JobBuilderFactory / StepBuilderFactory — removed in Batch 5; use new JobBuilder(name, repo) / new StepBuilder(name, repo)
Agent calls .chunk(500, txManager) — Batch 5 style, deprecated in 6; use .chunk(500) + .transactionManager(txManager)
Agent injects JobLauncher or JobExplorer — in Batch 6 launch with JobOperator.start(job, params) and query with JobRepository (it extends the deprecated JobExplorer); JobOperator's own query methods are deprecated for removal
Agent calls jobOperator.start("jobName", properties) — deprecated for removal; use start(Job, JobParameters)
Agent writes manual config @EnableBatchProcessing(dataSourceRef = ...) — split in Batch 6: @EnableBatchProcessing(taskExecutorRef = ...) + @EnableJdbcJobRepository(dataSourceRef = ...)
Agent reuses a Boot 3 Flyway baseline for BATCH_* — Batch 6 renamed BATCH_JOB_SEQ to BATCH_JOB_INSTANCE_SEQ; add the migration script
Agent writes write(List<? extends T> items) — the signature is write(Chunk<? extends T> chunk) since Batch 5
Agent expects batch metrics to just appear — Batch 6 dropped Micrometer's global static registry; declare an ObservationRegistry bean wired to your MeterRegistry
Agent re-runs a job with identical parameters and hits JobInstanceAlreadyCompleteException — add RunIdIncrementer or a unique identifying param
Agent adds a unique param every run on a job that should resume-on-failure — kills restartability; only add it when you want a fresh instance
Agent writes a paging reader query with no ORDER BY (or a non-unique one) — pages skip/duplicate rows silently; order by a unique column
Agent uses JdbcCursorItemReader in a multi-threaded step — not thread-safe; use a paging reader or SynchronizedItemStreamReader
Agent pages on a column the writer mutates in the same job — result set shifts; page on an immutable id
Agent returns null from a processor expecting pass-through — null filters (drops) the item
Agent sends email / publishes events from the ItemWriter — runs inside the chunk TX; do it in an afterJob listener
Agent forgets @StepScope on a reader that reads jobParameters — @Value("#{jobParameters[...]}") only binds in step scope
Agent leaves jobs running on startup in a web app — set spring.batch.job.enabled=false and launch explicitly
Agent lets initialize-schema: always DDL the prod DB — use never + a Flyway migration for the BATCH_* tables

About this skill

Automatic

Coding pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v6 — 10/18/2026

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

rrezartprebreza / spring-boot-skills

skills/spring-boot-3/spring-batch/SKILL.md

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License MITImported 10/18/2026Version v3Bundled 2 files

Illustrative Want to see what rrezartprebreza/spring-batch 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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Spring Batch

Dependencies

Job & Step (Spring Batch 6 API)

Idempotency & Restartability — the #1 operational gotcha

ItemReader — sort key and thread-safety

ItemProcessor — returning null filters

ItemWriter — Chunk, not List

Launching jobs

Metadata schema in production

Listeners — work that must run after the job, not per chunk

Don't reach for Batch when you don't need it

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