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Context Scope Discipline — 只把你要改的文件放进工作集 · agentsope | DecimalAI

Agent Toolingby agentsope

Context Scope Discipline — 只把你要改的文件放进工作集

Coder-agent working-file budget discipline: keep the editable working set (files you /add into writable context) under ~25k tokens, separate "read" from "edit", delegate breadth to a read-only repo-map, and drop files once edited.

Never made a case worseMaintained

SkillScore

SkillScore100/100

Test results 100

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

65%100%of test cases passed +35 pts

Without skill15 of 23

With skill23 of 23

8 fixed15 passed either way0 still failing0 broke

Measured on gemini-3.6-flash8/24/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWhen categorizing candidate files before beginning a coding session, what three context classification roles can be assigned, and which role grants write permissions in the session?

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 agentsope-agentsop-context-scope-discipline
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("agentsope-agentsop-context-scope-discipline")
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 agentsope-agentsop-context-scope-discipline

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/agentsope-agentsop-context-scope-discipline/SKILL.md https://app.decimal.ai/s/agentsope-agentsop-context-scope-discipline/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/agentsope-agentsop-context-scope-discipline/SKILL.md

This gets SKILL.md only. The skill's 1 other file is 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 agentsope-agentsop-context-scope-discipline

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("agentsope-agentsop-context-scope-discipline")
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/agentsope-agentsop-context-scope-discipline/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/agentsope-agentsop-context-scope-discipline/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 1 other file is 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 agentsope-agentsop-context-scope-discipline

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("agentsope-agentsop-context-scope-discipline")
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/agentsope-agentsop-context-scope-discipline/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/agentsope-agentsop-context-scope-discipline/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 1 other file is 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 agentsope-agentsop-context-scope-discipline

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("agentsope-agentsop-context-scope-discipline")
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/agentsope-agentsop-context-scope-discipline/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/agentsope-agentsop-context-scope-discipline/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 1 other file is 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 agentsope-agentsop-context-scope-discipline

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("agentsope-agentsop-context-scope-discipline")
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/agentsope-agentsop-context-scope-discipline/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/agentsope-agentsop-context-scope-discipline/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 1 other file is 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 agentsope-agentsop-context-scope-discipline

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("agentsope-agentsop-context-scope-discipline")
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/agentsope-agentsop-context-scope-discipline/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/agentsope-agentsop-context-scope-discipline/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 1 other file is 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 agentsope-agentsop-context-scope-discipline

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("agentsope-agentsop-context-scope-discipline")
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/agentsope-agentsop-context-scope-discipline/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/agentsope-agentsop-context-scope-discipline/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 1 other file is 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 agentsope-agentsop-context-scope-discipline

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("agentsope-agentsop-context-scope-discipline")
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/agentsope-agentsop-context-scope-discipline/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 1 other file is 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 agentsope-agentsop-context-scope-discipline

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("agentsope-agentsop-context-scope-discipline")
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/agentsope-agentsop-context-scope-discipline/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/agentsope-agentsop-context-scope-discipline/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 1 other file is 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 agentsope-agentsop-context-scope-discipline

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("agentsope-agentsop-context-scope-discipline")
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/agentsope-agentsop-context-scope-discipline/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/agentsope-agentsop-context-scope-discipline/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 1 other file is 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 agentsope-agentsop-context-scope-discipline

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("agentsope-agentsop-context-scope-discipline")
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/agentsope-agentsop-context-scope-discipline/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/agentsope-agentsop-context-scope-discipline/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 1 other file is 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 agentsope-agentsop-context-scope-discipline

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("agentsope-agentsop-context-scope-discipline")
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/agentsope-agentsop-context-scope-discipline/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/agentsope-agentsop-context-scope-discipline/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 1 other file is 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 agentsope-agentsop-context-scope-discipline

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("agentsope-agentsop-context-scope-discipline")
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/agentsope-agentsop-context-scope-discipline/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/agentsope-agentsop-context-scope-discipline/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 1 other file is 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 agentsope-agentsop-context-scope-discipline

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/agentsope-agentsop-context-scope-discipline/SKILL.md https://app.decimal.ai/s/agentsope-agentsop-context-scope-discipline/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/agentsope-agentsop-context-scope-discipline/SKILL.md

This gets SKILL.md only. The skill's 1 other file is 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/agentsope-agentsop-context-scope-discipline/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/agentsope-agentsop-context-scope-discipline.json

This gets SKILL.md only. The skill's 1 other file is not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/24/2026
Source: agentsope/skillalchemy ↗
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

Context Scope Discipline — 只把你要改的文件放进工作集

一句话：编辑代码时，工作文件预算（你 /add-ed 进可写上下文的文件）要压在 ~25k tokens 以内。超过这个量，"more context ≠ better edits"——模型注意力被稀释，开始改错文件、漏看你刚加进去的目标。广度交给 [[agentsop-repo-map]]（只读签名地图），深度只留给"这次真要编辑"的那几个文件。

这是一个增强叠加技能（enhance overlay）。它不替代任何"通用 token 预算"建议，而是把那条泛泛的"少塞上下文"打磨成一条 coder-agent 专属的硬规则：区分"读"与"改"，只把"改"的文件加进工作集。借用 Aider 的实测阈值——

"Above about 25k tokens of context, most models start to become distracted." [aider.chat/docs/troubleshooting/edit-errors.html]

1. 何时激活本技能

下列任一情形成立时，把"工作文件预算纪律"作为该编辑会话的标准约束：

任务是多文件编辑：rename、抽函数、改 API 签名、加 hook 点——你需要理解 N 个文件，但只会真正修改其中一小部分。
agent 正在改错文件：给出的 diff 落在你没想改的文件上，或编造了不存在的路径。这几乎总是"工作集不对"——目标没加进去，或加了太多无关文件把模型呛晕。
上下文窗口在涨：/tokens（或等价物）逼近 25k；响应被截断；长会话里模型"记住了错的东西"。
你在大仓库里工作，凭"为了保险全加进去"的本能正在把整个目录、整个 repo 灌进可写上下文。
你在写自建 coder harness，需要一条明确的"可编辑文件白名单何时收/何时放"的规则。

不应激活的反面信号：单文件已知的小改动（工作集天然就是 1）；纯讨论/架构问答（用只读上下文 + [[agentsop-repo-map]] 即可，不进工作集）；非编辑任务。

2. 核心心智模型

2.1 一句话铁律

more context ≠ better edits. 过了约 ~25k tokens 的文件量，模型就开始失焦——只把你这一轮真要编辑的文件加进工作集，其余的靠 [[agentsop-repo-map]] 顶上。

2.2 "读" vs "改"是两种不同的上下文，需要两种不同的预算

LLM 看到的编辑上下文分三层，优先级与写权限递减：

层	内容	写权限	预算策略
系统提示 + 编辑格式	harness 固化	harness	不可控
只读上下文	[[agentsop-repo-map]] 签名地图 + `/read` 的参考文件 + CONVENTIONS.md	人/agent 配置	给"广度"——用地图覆盖全仓，但只放签名不放函数体
工作集（写集合）	`/add`-ed 的文件	LLM 唯一能编辑的	给"深度"——只放这次真要改的，压在 ~25k 以内

核心区分：repo-map 给"哪儿"（breadth，签名级，便宜），工作集给"怎么改"（depth，全文级，贵）。把这两种需求混进同一个篮子（"全 /add 进来再说"）是本技能要根除的反模式。

2.3 25k 是稀释阈，不是上限

25k 不是"塞到 25k 就崩"，而是"过了 25k 编辑准确率开始断崖式下降"。它是个信号阈：

工作集本身 + 对话历史 + repo-map 都算进这一份预算。
文件越多、越大，留给"模型对当前编辑点的注意力"越少。
模型越弱，对 25k 越敏感（弱模型"更容易违背系统提示" [aider.chat/docs/troubleshooting/edit-errors.html]）。

2.4 "全加进去保险"是错觉——repo-map 已经替你覆盖了广度

凭直觉，"我要理解这 10 个文件才能改对，那就全 /add"。实测相反：

Aider 只靠 repo-map（不把文件加进工作集）在 SWE-Bench Lite 上仍 70.3% 命中正确文件 [aider.chat/2024/05/22/swe-bench-lite.html]。

即"找文件"这件事不需要把文件灌进工作集——只读地图就够了。工作集只为"编辑"存在。把这两件事拆开，是省预算的关键。详见 [[agentsop-repo-map]]。

2.5 动态预算：工作集涨，地图就该缩

预算是一份蛋糕，不是各自独立的盘子。/add 了正确文件后，[[agentsop-repo-map]] 应自动缩小（"adjusts ... based on the state of the chat" [aider.chat/docs/repomap.html]），把 token 让给真代码。如果你的 harness 不会自动缩地图，编辑期就手动 --map-tokens 调小或归零。

2.6 与 [[agentsop-session-state-hygiene]] 的分工

本技能管文件维度（工作集里有哪些文件）；[[agentsop-session-state-hygiene]] 管历史维度（对话历史是否污染当前任务）。二者共用同一份 25k 预算：

预算超了，先 /drop 不再需要的文件（本技能）；
仍然超 / 话题已切换，再 /clear 清历史（[[agentsop-session-state-hygiene]]）。
/drop 保历史去文件；/clear 保文件去历史；/reset 两者都丢。

3. SOP 工作流

Phase 1 — 区分"要编辑"和"只要读懂"

任务进来，第一步不是 /add，而是分类。对每个相关文件问一句："这一轮我会修改它的字节吗？"

会改它的字节        → 候选写集合（稍后 /add）
只需理解它的契约     → 只读：/read，或干脆只靠 repo-map 的签名
不确定改哪些        → 先不加任何文件，进 Phase 2 让 repo-map 帮你定位

经验法则：写集合目标 ≤ 5 个文件。超过，多半是任务没拆够。

Phase 2 — 不知道改哪个？让 repo-map 定位，而不是全加进来

> /ask which files implement <feature>?
< [模型基于只读 repo-map 回答候选文件]

模型命名出目标后，你再决定把哪些加进工作集（Op locate-then-add）。"找文件"和"改文件"永远两步走——这是 [[agentsop-repo-map]] 与本技能共享的设计哲学。

Phase 3 — 只 `/add` 你会编辑的，参考文件用 `/read`

/add  src/auth.py tests/test_auth.py     # 这两个会改 → 进写集合
/read src/config.py docs/auth.md         # 只参考，不改 → 只读

铁律重申：少 /add，敢 /drop。"为了保险全加"恰恰是让模型改错文件的主因。

Phase 4 — 编辑过程中持续盯预算

/tokens                  # 看当前占用；接近 25k 是黄灯

信号	动作
`/tokens` 逼近 25k	`/drop` 已经改完、不再相关的文件
repo-map 占比偏大	调小 `--map-tokens`（目标文件已定，地图可缩）
模型反复改错文件	`/ls` 检查工作集；`/drop` 多余的，`/add` 缺的
历史漂移（不是文件问题）	转交 [[agentsop-session-state-hygiene]]：`/clear`

Phase 5 — 一个文件改完就 `/drop` 它

工作集不是"会话期一直累积"的。某文件这一轮的修改告一段落、后续子任务不再碰它——立即 /drop。把腾出的预算还给下一批要改的文件。这是把工作集当滑动窗口用，而不是当垃圾堆。

Phase 6 — 任务太大撑不住时，拆，而不是塞

地图也缩了、能 /drop 的都 /drop 了，预算还是破 25k？这是任务太宽的信号，不是预算的问题：

1. 进子目录 + --subtree-only（缩小 repo-map 范围，见 [[agentsop-repo-map]] §3）
2. 拆任务：大需求拆成多个收敛子目标，每个子目标一个会话
3. 每个新会话只带它真正要改的那 ≤5 个文件

4. 操作模型

每条给 Trigger / Action / Output / Evidence。命令名以 Aider 为参考，行为框架无关。

Op 1 — `classify(file)` 区分"读"与"改"

Trigger：任何相关文件进入视野。
Action：问"这一轮会修改它的字节吗？"。会改 → 写集合候选；只读懂 → 只读层（/read 或仅 repo-map 签名）。
Output：每个文件被标记为 EDIT / READ-ONLY / NAVIGATE-ONLY 三类之一。
Evidence：LLM 只能编辑工作集里的文件 [aider.chat/docs/more/edit-formats.html]；只读 vs 读写是 Aider 的安全边界。

Op 2 — `locate-then-add(task)` 先定位再加

Trigger：写集合未定，不知道改哪个文件。
Action：把 task + repo-map 喂给 LLM，让它只命名候选文件（不直接编辑）；人/agent 再 /add 命名出的目标。
Output：≤5 个写集合文件 + ≤3 个只读参考。
Evidence：repo-map 只读即可达 70.3% 文件命中 [aider.chat/2024/05/22/swe-bench-lite.html]——定位不需要进工作集。

Op 3 — `add(files)` 加入工作集（克制）

Trigger：某文件确定这一轮会被编辑。
Action：/add 只加该文件。不加"可能会用到"的、不加整目录。
Output：工作集 +1。
Evidence：模型改错文件"几乎总是因为该文件没 /add 或你 /add 了太多无关文件" [aider.chat/docs/usage].

Op 4 — `read(files)` 加为只读参考

Trigger：文件需要被理解但不会被改（schema、config、CONVENTIONS）。
Action：/read，进只读层，LLM 不能编辑。
Output：只读上下文 +1，写集合不变。
Evidence：Aider --read / /read；只读层与读写层分离 [aider.chat/docs/usage/conventions.html]。

Op 5 — `budget_watch()` 监控预算

Trigger：每次编辑回合开始，或感觉模型变笨时。
Action：/tokens 看占用分布（工作集 / 历史 / 地图各占多少）。
Output：是否越过 25k 黄灯的判断。
Evidence：25k 稀释阈 [aider.chat/docs/troubleshooting/edit-errors.html]。

Op 6 — `drop(files)` 改完即释放

Trigger：某文件这一轮修改完成、后续不再碰；或预算逼近 25k。
Action：/drop 该文件，腾出预算。
Output：工作集 -1，地图自动回涨补位。
Evidence：/drop 是常态操作而非应急；动态预算 [aider.chat/docs/repomap.html]。

Op 7 — `lean-on-map()` 把广度还给地图

Trigger：你想"全加进来才安心"的冲动；或工作集 >5。
Action：把"只为理解、不为编辑"的文件从写集合移到只读地图（/drop + 信任 repo-map）。必要时 /map 审计地图已覆盖什么。
Output：更瘦的工作集，广度由签名地图承担。
Evidence：见 [[agentsop-repo-map]]；breadth 用签名、depth 用全文是两种预算。

Op 8 — `split-task()` 拆任务而非塞预算

Trigger：地图缩了、能 drop 的都 drop 了，预算仍破 25k。
Action：把需求拆成多个收敛子目标，每个新会话只带它要改的 ≤5 个文件。
Output：每会话工作集都在预算内。
Evidence：monorepo 上下文溢出的标准缓解 [aider.chat/docs/troubleshooting/token-limits.html]。

5. 困境决策案例 (Examples / Scenarios)

案例 1 — "我要理解 10 个文件才能改对那 2 个：全加，还是用地图？"

触发：一个改动横跨 10 个文件的调用链，但你实际只会修改 2 个（比如改一个 API 签名 + 它的一处实现）。本能是把 10 个全 /add 进来"看全"。

诊断：你把"理解广度"误当成"编辑深度"。10 个里有 8 个你只需要看签名/契约，不会动它们的字节。

决策规则：

文件角色	数量	放哪
真要改字节	2	`/add`（写集合）
需看完整契约/会被这次改动影响、要核对	1–2	`/read`（只读全文）
只需知道"它在哪、签名是什么"	6–7	不加，靠 [[agentsop-repo-map]] 签名

为什么有效：repo-map 只读即 70.3% 命中正确文件 [aider.chat/2024/05/22/swe-bench-lite.html]——广度不需要进工作集。把 2 个进写集合、地图覆盖其余 8 个，预算从"10 个全文"降到"2 全文 + 8 签名"，编辑注意力集中在真正要改的两处。

反模式：10 个全 /add → 破 25k → 模型在 8 个无关文件里挑了错的位置改 → 回滚 → 重来。

案例 2 — "任务做到一半预算满了，怎么办？"

触发：多文件重构进行中，/tokens 显示已过 25k，模型开始截断响应、漏看你刚加的文件。

决策树（按代价递增，能停就停）：

步	动作	何时停
1	`/tokens` 看占用分布：工作集 / 历史 / 地图谁是大头	找到主要占用者
2	已改完的文件 `/drop`（Op 6）	工作集回到 ≤5、预算降到 25k 下
3	地图占比大 → `--map-tokens` 调小或归零（目标已定，地图可让位）	预算回落
4	历史是大头、且话题已切 → 转 [[agentsop-session-state-hygiene]]：`/clear`（保文件去历史）	历史清掉
5	仍破 25k → 任务太宽：`split-task()`，余下子任务新开会话	单会话扛得住

关键认知：先动文件维度（drop / 缩地图），再动历史维度（clear），最后才拆任务。/drop 与 /clear 是互补而非二选一——前者是本技能，后者是 [[agentsop-session-state-hygiene]]。

反模式：预算满了第一反应是"换更大上下文窗口的模型"。窗口更大不改变 25k 稀释阈——大窗口模型塞到 25k+ 一样失焦。先收工作集，别先换模型。

案例 3 — "模型一直改错文件，我该再多加几个文件让它看清吗？"

触发：连续几轮，模型的 diff 落在错误文件上。直觉是"它没看够，再 /add 几个"。

诊断：方向反了。改错文件的两种根因都不靠"加更多文件"解决：

现象	根因	修复
改的文件根本没在工作集里	目标没 `/add`	`/add` 目标文件（Op 3）
工作集里文件太多、它挑错了	`/add` 过量稀释	`/drop` 无关的，收到 ≤5（Op 6）

决策规则：/ls 看现状 → 缺目标就加目标、多余就 drop → 不知道哪个是目标就 locate-then-add（Op 2）让地图替你找。几乎不会是"加更多文件"能解的。

6. 反模式与边界

常见反模式

/add 整个目录——"这个 feature 在 src/payments/，全加"。目录里 90% 的文件你不会改，纯稀释。只 /add 那 2–3 个目标文件。
从不 /drop——把工作集当只进不出的垃圾堆。改完的文件不释放，预算单调上涨直到破 25k。/drop 是常态操作。
dump entire repo——"上下文越多越好"，把整仓灌进去。这是 25k 阈值的反面教材；repo-map 的全部意义就是让你不必这么做。
用工作集做广度——把"只为读懂"的文件 /add 进可写上下文。读懂用 /read 或 repo-map 签名，编辑才用 /add。
预算满了先换模型不先收工作集——更大窗口不改变稀释阈。
混淆 /drop 与 /clear——文件多就 /drop（本技能）；历史脏就 /clear（[[agentsop-session-state-hygiene]]）。用错维度解决不了问题。

硬边界

本技能不替代 [[agentsop-repo-map]]：广度（找文件）是 repo-map 的活，本技能管深度（哪些进工作集）。两者配套使用。
25k 是经验阈不是物理上限：具体数字随模型变；强模型耐受更高，弱模型更低。把它当"该警觉"的信号，不是"卡死"的红线。
本技能只管文件维度：对话历史污染交给 [[agentsop-session-state-hygiene]]；二者共享同一份 25k 预算。
单文件已知任务无需本技能：工作集天然是 1，地图可关。
本技能不保证 100% 不改错：收紧工作集大幅降低改错率，但定位本身仍有 ~30% 残余误差（repo-map 70.3% 命中的另一面），需人工兜底。

7. 跨框架对照

同一条"只把要改的文件放进工作集"纪律，四个 harness 各自的接口与默认行为：

	Aider	Claude Code	Cursor	Cline
加入工作集	`/add <files>`（显式，仅这些可编辑）	`Read` 工具按需读文件入上下文	`@file` / `@folder` mention	按需 `read_file` tool-call
移出/释放	`/drop <files>`	上下文压缩 / `/clear`	移除 mention	tool-call 历史自然滚出
只读参考	`/read <file>`（不可编辑）	读了即在上下文（无读/写区分）	`@file` 同样方式	同上，无显式只读层
广度来源	tree-sitter repo-map（签名）	Grep/Glob/Read 按需探索	全仓 codebase index	文件树 + 主动读
预算监控	`/tokens`（25k 显式建议）	上下文窗口指示 + 自动压缩	闭源	上下文长度可见
写权限边界	硬：仅 `/add` 的可编辑	软：能读即能改（用工具白名单约束）	软：可改任意打开文件	软：可改任意读过的文件

关键差异：

Aider 把"读/写"做成硬边界（/read vs /add），最贴合本技能——工作集就是写白名单。其 25k 阈值是这条纪律的实测来源。
Claude Code 没有显式"工作集"概念：Read 进来的文件既可读也可被 Edit。本技能在这里表现为自律——不要为了"看全"而 Read 整个目录；用 Grep/Glob 定位（相当于 repo-map 的广度），只 Read 你要 Edit 的文件。/clear 与自动压缩对应 /drop 的预算回收。
Cursor 用 @-mention 选上下文；mention 越多预算越紧。纪律是"@ 你要改的，别 @ 整个 folder 求保险"。
Cline 靠 tool-call 现场读文件，工作集隐式等于"读过的文件集"。纪律是不要在 plan 阶段把一堆文件读进来当背景——读过即占预算。

统一心智：无论接口是 /add、@file、Read 还是 read_file，规则不变——进工作集的应当是"这一轮会编辑的文件"，广度交给地图/搜索，预算盯住 ~25k。

引用源

主要：

[aider.chat/docs/troubleshooting/edit-errors.html] — "Above about 25k tokens of context, most models start to become distracted." 本技能的核心阈值。
[aider.chat/docs/troubleshooting/token-limits.html] — 上下文溢出缓解。
[aider.chat/docs/repomap.html] — 动态预算："adjusts the size of the repo map dynamically based on the state of the chat."
[aider.chat/2024/05/22/swe-bench-lite.html] — repo-map 只读即 70.3% 文件命中（广度无需进工作集）。
[aider.chat/docs/more/edit-formats.html] — 只读 vs 读写上下文边界。
[aider.chat/docs/usage/commands.html] [aider.chat/docs/usage/conventions.html] — /add /read /drop /tokens 命令面。

派生：

references/R1-source-evidence.md — 来源逐条 quote。
intermediate/operation_candidates.json — 操作抽取过程。

关联技能：

[[agentsop-repo-map]] — 广度伙伴：签名地图给"哪儿"，本技能给"哪些进工作集"。
[[agentsop-session-state-hygiene]] — 历史维度伙伴：/drop（文件）与 /clear（历史）共享同一份 25k 预算。
上游：本技能是"通用 token 预算"建议的 coder-agent 专属增强叠加。

跨工具（一般认知）：Aider / Claude Code / Cursor / Cline 公开文档与博客。

When it activates

Automatically, when a task matches. Use when an LLM coder-agent edits multiple files, when the working set must stay focused, or when the model starts editing the wrong file / missing targets because too much context dilutes attention. Search keywords: context window full, agent edits wrong file, too muc

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skills/agentsop-context-scope-discipline/SKILL.md

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License MITImported 8/3/2026Version v1Bundled 1 file

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Context Scope Discipline — 只把你要改的文件放进工作集

1. 何时激活本技能

2. 核心心智模型

2.1 一句话铁律

2.2 "读" vs "改"是两种不同的上下文，需要两种不同的预算

2.3 25k 是稀释阈，不是上限

2.4 "全加进去保险"是错觉——repo-map 已经替你覆盖了广度

2.5 动态预算：工作集涨，地图就该缩

2.6 与 [[agentsop-session-state-hygiene]] 的分工

3. SOP 工作流

Phase 1 — 区分"要编辑"和"只要读懂"

Phase 2 — 不知道改哪个？让 repo-map 定位，而不是全加进来

Phase 3 — 只 /add 你会编辑的，参考文件用 /read

Phase 4 — 编辑过程中持续盯预算

Phase 5 — 一个文件改完就 /drop 它

Phase 6 — 任务太大撑不住时，拆，而不是塞

4. 操作模型

Op 1 — classify(file) 区分"读"与"改"

Op 2 — locate-then-add(task) 先定位再加

Op 3 — add(files) 加入工作集（克制）

Op 4 — read(files) 加为只读参考

Op 5 — budget_watch() 监控预算

Op 6 — drop(files) 改完即释放

Op 7 — lean-on-map() 把广度还给地图

Op 8 — split-task() 拆任务而非塞预算

5. 困境决策案例 (Examples / Scenarios)

案例 1 — "我要理解 10 个文件才能改对那 2 个：全加，还是用地图？"

案例 2 — "任务做到一半预算满了，怎么办？"

案例 3 — "模型一直改错文件，我该再多加几个文件让它看清吗？"

6. 反模式与边界

常见反模式

硬边界

7. 跨框架对照

引用源

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Phase 3 — 只 `/add` 你会编辑的，参考文件用 `/read`

Phase 5 — 一个文件改完就 `/drop` 它

Op 1 — `classify(file)` 区分"读"与"改"

Op 2 — `locate-then-add(task)` 先定位再加

Op 3 — `add(files)` 加入工作集（克制）

Op 4 — `read(files)` 加为只读参考

Op 5 — `budget_watch()` 监控预算

Op 6 — `drop(files)` 改完即释放

Op 7 — `lean-on-map()` 把广度还给地图

Op 8 — `split-task()` 拆任务而非塞预算