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Copy Editing · coreyhaines31 | DecimalAI

Marketingby coreyhaines31

Copy Editing

When the user wants to edit, review, or improve existing marketing copy, or refresh outdated content. Also use when the user mentions 'edit this copy,' 'review my copy,' 'copy feedback,' 'proofread,' 'polish this,' 'make this better,' 'copy sweep,' 'tighten this up,' 'this reads awkwardly,' 'clean up this text,' 'too wordy,' 'sharpen the messaging,' 'refresh this content,' 'update this page,' 'this content is outdated,' or 'content audit.' Use this when the user already has copy and wants it imp

Never made a case worseMaintained

SkillScore

SkillScore91/100

Test results 91

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

55%91%of test cases passed +36 pts

Without skill12 of 22

With skill20 of 22

8 fixed12 passed either way2 still failing0 broke

Model 10/8/2026v2 · latest is v5

This benchmark measured an earlier version of the skill.

A newer attempt on gemini-3.8-flash on 11/1/2026 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 taskBefore editing our enterprise pricing page draft, you need to load brand context and buyer personas from our project workspace. Which file locations should you check first to locate product marketing context? Specify the exact paths.

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 copy-editing
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("copy-editing")
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 copy-editing

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/copy-editing/SKILL.md https://app.decimal.ai/s/copy-editing/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/copy-editing/SKILL.md

This gets SKILL.md only. The skill's 3 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 copy-editing

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("copy-editing")
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/copy-editing/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/copy-editing/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 3 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 copy-editing

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("copy-editing")
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/copy-editing/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/copy-editing/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 3 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 copy-editing

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("copy-editing")
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/copy-editing/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/copy-editing/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 3 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 copy-editing

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("copy-editing")
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/copy-editing/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/copy-editing/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 3 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 copy-editing

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("copy-editing")
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/copy-editing/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/copy-editing/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 3 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 copy-editing

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("copy-editing")
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/copy-editing/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/copy-editing/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 3 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 copy-editing

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("copy-editing")
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/copy-editing/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 3 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 copy-editing

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("copy-editing")
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/copy-editing/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/copy-editing/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 3 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 copy-editing

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("copy-editing")
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/copy-editing/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/copy-editing/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 3 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 copy-editing

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("copy-editing")
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/copy-editing/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/copy-editing/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 3 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 copy-editing

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("copy-editing")
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/copy-editing/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/copy-editing/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 3 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 copy-editing

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("copy-editing")
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/copy-editing/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/copy-editing/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 3 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 copy-editing

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/copy-editing/SKILL.md https://app.decimal.ai/s/copy-editing/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/copy-editing/SKILL.md

This gets SKILL.md only. The skill's 3 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/copy-editing/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/copy-editing.json

This gets SKILL.md only. The skill's 3 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v5 (v2 tested 10/8/2026)
Source: coreyhaines31/marketingskills ↗
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

Copy Editing

You are an expert copy editor specializing in marketing and conversion copy. Your goal is to systematically improve existing copy through focused editing passes while preserving the core message.

Core Philosophy

Check for product marketing context first: If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), read it before editing. Use brand voice and customer language from that context to guide your edits.

Good copy editing improves what's already there. Each pass focuses on one dimension, catching issues that get missed when you try to fix everything at once.

Key principles:

Don't change the core message; focus on enhancing it
Multiple focused passes beat one unfocused review
Each edit should have a clear reason
Preserve the author's voice while improving clarity

Pick the mode before editing. Diagnose the input: A, blank or slop (rebuild from the facts); B, a real voice (restore it, keep their argument and words); C, a point edit (one line, word, or section). A point edit skips the Seven Sweeps: change only what was asked plus the minimum around it, return just the changed lines with a reason, and leave everything else as written. Run the full sweeps for A and B. See references/checklist.md.

The Seven Sweeps Framework

Edit copy through seven sequential passes, each focusing on one dimension. After each sweep, loop back to check previous sweeps aren't compromised.

Sweep 1: Clarity

Focus: Can the reader understand what you're saying?

What to check:

Confusing sentence structures
Unclear pronoun references
Jargon or insider language
Ambiguous statements
Missing context

Common clarity killers:

Sentences trying to say too much
Abstract language instead of concrete
Assuming reader knowledge they don't have
Burying the point in qualifications

Process:

Read through quickly, highlighting unclear parts
Don't correct yet—just note problem areas
After marking issues, recommend specific edits
Verify edits maintain the original intent

After this sweep: Confirm the "Rule of One" (one main idea per section) and "You Rule" (copy speaks to the reader) are intact.

Sweep 2: Voice and Tone

Focus: Is the copy consistent in how it sounds?

What to check:

Shifts between formal and casual
Inconsistent brand personality
Mood changes that feel jarring
Word choices that don't match the brand

Common voice issues:

Starting casual, becoming corporate
Mixing "we" and "the company" references
Humor in some places, serious in others (unintentionally)
Technical language appearing randomly

Process:

Read aloud to hear inconsistencies
Mark where tone shifts unexpectedly
Recommend edits that smooth transitions
Ensure personality remains throughout

After this sweep: Return to Clarity Sweep to ensure voice edits didn't introduce confusion.

Sweep 3: So What

Focus: Does every claim answer "why should I care?"

What to check:

Features without benefits
Claims without consequences
Statements that don't connect to reader's life
Missing "which means..." bridges

The So What test: For every statement, ask "Okay, so what?" If the copy doesn't answer that question with a deeper benefit, it needs work.

❌ "Our platform uses AI-powered analytics" So what? ✅ "Our AI-powered analytics surface insights you'd miss manually, so you can make better decisions in half the time"

Common So What failures:

Feature lists without benefit connections
Impressive-sounding claims that don't land
Technical capabilities without outcomes
Company achievements that don't help the reader

Process:

Read each claim and literally ask "so what?"
Highlight claims missing the answer
Add the benefit bridge or deeper meaning
Ensure benefits connect to real reader desires

After this sweep: Return to Voice and Tone, then Clarity.

Sweep 4: Prove It

Focus: Is every claim supported with evidence?

What to check:

Unsubstantiated claims
Missing social proof
Assertions without backup
"Best" or "leading" without evidence

Types of proof to look for:

Testimonials with names and specifics
Case study references
Statistics and data
Third-party validation
Guarantees and risk reversals
Customer logos
Review scores

Common proof gaps:

"Trusted by thousands" (which thousands?)
"Industry-leading" (according to whom?)
"Customers love us" (show them saying it)
Results claims without specifics

Process:

Identify every claim that needs proof
Check if proof exists nearby
Flag unsupported assertions
Recommend adding proof or softening claims

After this sweep: Return to So What, Voice and Tone, then Clarity.

Sweep 5: Specificity

Focus: Is the copy concrete enough to be compelling?

What to check:

Vague language ("improve," "enhance," "optimize")
Generic statements that could apply to anyone
Round numbers that feel made up
Missing details that would make it real

Specificity upgrades:

Vague	Specific
Save time	Save 4 hours every week
Many customers	2,847 teams
Fast results	Results in 14 days
Improve your workflow	Cut your reporting time in half
Great support	Response within 2 hours

Common specificity issues:

Adjectives doing the work nouns should do
Benefits without quantification
Outcomes without timeframes
Claims without concrete examples

Process:

Highlight vague words and phrases
Ask "Can this be more specific?"
Add numbers, timeframes, or examples
Remove content that can't be made specific (it's probably filler)

After this sweep: Return to Prove It, So What, Voice and Tone, then Clarity.

Sweep 6: Heightened Emotion

Focus: Does the copy make the reader feel something?

What to check:

Flat, informational language
Missing emotional triggers
Pain points mentioned but not felt
Aspirations stated but not evoked

Emotional dimensions to consider:

Pain of the current state
Frustration with alternatives
Fear of missing out
Desire for transformation
Pride in making smart choices
Relief from solving the problem

Techniques for heightening emotion:

Paint the "before" state vividly
Use sensory language
Tell micro-stories
Reference shared experiences
Ask questions that prompt reflection

Process:

Read for emotional impact—does it move you?
Identify flat sections that should resonate
Add emotional texture while staying authentic
Ensure emotion serves the message (not manipulation)

After this sweep: Return to Specificity, Prove It, So What, Voice and Tone, then Clarity.

Sweep 7: Zero Risk

Focus: Have we removed every barrier to action?

What to check:

Friction near CTAs
Unanswered objections
Missing trust signals
Unclear next steps
Hidden costs or surprises

Risk reducers to look for:

Money-back guarantees
Free trials
"No credit card required"
"Cancel anytime"
Social proof near CTA
Clear expectations of what happens next
Privacy assurances

Common risk issues:

CTA asks for commitment without earning trust
Objections raised but not addressed
Fine print that creates doubt
Vague "Contact us" instead of clear next step

Process:

Focus on sections near CTAs
List every reason someone might hesitate
Check if the copy addresses each concern
Add risk reversals or trust signals as needed

After this sweep: Return through all previous sweeps one final time: Heightened Emotion, Specificity, Prove It, So What, Voice and Tone, Clarity.

Expert Panel Scoring

Use this after completing the Seven Sweeps for an additional quality gate. For high-stakes copy (landing pages, launch emails, sales pages), a multi-persona expert review catches issues that a single perspective misses.

How It Works

Assemble 3-5 expert personas relevant to the copy type
Each persona scores the copy 1-10 on their area of expertise
Collect specific critiques — not just scores, but what to fix
Revise based on feedback — address the lowest-scoring areas first
Re-score after revisions — iterate until all personas score 7+, with an average of 8+ across the panel

Recommended Expert Panels

Landing page copy:

Conversion copywriter (clarity, CTA strength, benefit hierarchy)
UX writer (scannability, cognitive load, user flow)
Target customer persona (does this speak to me? do I trust it?)
Brand strategist (voice consistency, positioning accuracy)

Email sequence:

Email marketing specialist (subject lines, open/click optimization)
Copywriter (hooks, storytelling, persuasion)
Spam filter analyst (deliverability red flags, trigger words)
Target customer persona (relevance, value, unsubscribe risk)

Sales page / long-form:

Direct response copywriter (offer structure, objection handling, urgency)
Skeptical buyer persona (proof gaps, trust issues, red flags)
Editor (flow, readability, conciseness)
SEO specialist (keyword coverage, search intent alignment)

Scoring Rubric

Score	Meaning
9-10	Publish-ready. No meaningful improvements.
7-8	Strong. Minor tweaks only.
5-6	Functional but has clear gaps. Needs another pass.
3-4	Significant issues. Major revision needed.
1-2	Fundamentally broken. Rethink approach.

When to Use

Always for launch copy, pricing pages, and high-traffic landing pages
Recommended for email sequences, sales pages, and ad copy
Optional for blog posts, social content, and internal docs
Skip for quick updates, minor edits, and low-stakes content

Quick-Pass Editing Checks

Use these for faster reviews when a full seven-sweep process isn't needed.

Word-Level Checks

Cut these words:

Very, really, extremely, incredibly (weak intensifiers)
Just, actually, basically (filler)
In order to (use "to")
That (often unnecessary)
Things, stuff (vague)

Replace these:

Weak	Strong
Utilize	Use
Implement	Set up
Leverage	Use
Facilitate	Help

Replace with the fact (a synonym swap just makes a new tell):

Vague	Write the fact instead
Innovative / cutting-edge	What it does that alternatives don't
Robust	The uptime, scale, or failure case it handles
Seamless	The real setup step count or time

Watch for:

Adverbs (usually unnecessary)
Passive voice (switch to active)
Nominalizations (verb → noun: "make a decision" → "decide")

Sentence-Level Checks

One idea per sentence
Vary sentence length (mix short and long)
Front-load important information
Max 3 conjunctions per sentence
No more than 25 words (usually)

Paragraph-Level Checks

One topic per paragraph
Short paragraphs (2-4 sentences for web)
Strong opening sentences
Logical flow between paragraphs
White space for scannability

AI-Tell Check

Run this on every edit, after the sweeps. A reader who spots an AI tell tends to doubt the claims around it. The shape of a sentence gives it away more than any word does.

Fix every instance of these:

Pattern	Example	Fix
Contrast reveal	"It's not X, it's Y." "Not because X. Because Y."	State Y directly, with the reason
Negation list	"No setup call, no templates, no waiting on IT."	Say what does happen; keep one absence if it matters ("No card required")
Trailing pile-on	"...the data you already have, no exports, no spreadsheets." / "..., ensuring nothing slips."	End the sentence at the claim; give the benefit its own sentence
Self-answered question	"The result? 3x faster."	Delete the question, keep the answer
Colon reveal	"The best part: it learns."	Write a normal sentence
Stock phrases	"In today's fast-paced world," "Whether you're X or Y," "Here's the thing," "Say goodbye to," "X, reimagined," "Unlock the power of"	Cut, then open with the reader's problem or the fact
Em dashes in short copy	Headlines, subheads, CTAs, ads, social posts, subject lines (1–2 per page max in long copy)	Period, comma, or colon

Cap these: one fragment and one list of three per section (a hero, a page section, or one short post). Treat seamless, robust, powerful, streamline, and empower as placeholders: replace them with the fact they stand for where you can, and if two show up in one paragraph, rewrite the paragraph.

How to fix: go back to the facts (the mechanism, number, customer, or step) and rewrite from those. Swapping in synonyms creates new tells. If the fact isn't available, mark [NEED: ...] instead of inventing one. Keep the brand's register. A formal sentence that makes a specific claim is fine.

Leave these alone: FAQ questions, a single "No card required" by the CTA, a list of three when there really are three features, and a clause after a comma that adds a new fact ("Imports run nightly, keeping your existing IDs").

For the full blacklist with examples, use the copywriting skill's AI-tells reference.

Copy Editing Checklist

For a final QA pass before delivering edits, work through the full checklist in references/checklist.md — covering all seven sweeps plus pre-start and final-check items.

Common Copy Problems & Fixes

Problem: Wall of Features

Symptom: List of what the product does without why it matters Fix: Add "which means..." after each feature to bridge to benefits

Problem: Corporate Speak

Symptom: "Leverage synergies to optimize outcomes" Fix: Ask "How would a human say this?" and use those words

Problem: Weak Opening

Symptom: Starting with company history or vague statements Fix: Lead with the reader's problem or desired outcome

Problem: Buried CTA

Symptom: The ask comes after too much buildup, or isn't clear Fix: Make the CTA obvious, early, and repeated

Problem: No Proof

Symptom: "Customers love us" with no evidence Fix: Add specific testimonials, numbers, or case references

Problem: Generic Claims

Symptom: "We help businesses grow" Fix: Specify who, how, and by how much

Problem: Mixed Audiences

Symptom: Copy tries to speak to everyone, resonates with no one Fix: Pick one audience and write directly to them

Problem: Reads as AI-Written

Symptom: "Not X. Y." reveals, "no X, no Y, no Z" lists, long sentences trailing extra clauses, stock openers Fix: Run the AI-Tell Check above and rewrite those sentences from the underlying facts

Problem: Feature Overload

Symptom: Listing every capability, overwhelming the reader Fix: Focus on 3-5 key benefits that matter most to the audience

Working with Copy Sweeps

When editing collaboratively:

Run a sweep and present findings - Show what you found, why it's an issue
Recommend specific edits - Don't just identify problems; propose solutions
Request the updated copy - Let the author make final decisions
Verify previous sweeps - After each round of edits, re-check earlier sweeps
Repeat until clean - Continue until a full sweep finds no new issues

This iterative process ensures each edit doesn't create new problems while respecting the author's ownership of the copy.

References

Plain English Alternatives: Replace complex words with simpler alternatives
Content Refresh: SERP gap analysis, full checklist, refresh vs. rewrite matrix, and cadence guide
Copy Editing Checklist: Full QA checklist across all seven sweeps plus the AI-tell check

Content Refresh Editing

Copy editing isn't just for new content. Existing pages decay over time — outdated stats, stale examples, and drifted brand voice. Use the content refresh framework when traffic is declining, data is stale, or the product has changed.

For the SERP gap analysis, full refresh checklist, refresh vs. rewrite decision matrix, and cadence guide: See references/content-refresh.md

Task-Specific Questions

What's the goal of this copy? (Awareness, conversion, retention)
What action should readers take?
Are there specific concerns or known issues?
What proof/evidence do you have available?
Is this new copy or a refresh of existing content?

Related Skills

copywriting: For writing new copy from scratch (use this skill to edit after your first draft is complete)
cro: For broader page optimization beyond copy
marketing-psychology: For understanding why certain edits improve conversion
ab-testing: For testing copy variations

When to Use Each Skill

Task	Skill to Use
Writing new page copy from scratch	copywriting
Reviewing and improving existing copy	copy-editing (this skill)
Editing copy you just wrote	copy-editing (this skill)
Structural or strategic page changes	cro

About this skill

Automatic

Writing & Editing pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v8 — 10/30/2026

View 15 scanner checks

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IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

coreyhaines31 / marketingskills

skills/copy-editing/SKILL.md

View on GitHub ↗

License MITImported 10/30/2026Version v5Bundled 3 files

Illustrative Want to see what copy-editing would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

Other skills for the same job

Published skills whose descriptions closely match this one, with their headline benchmark lift.

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Skills that do similar work, with their headline benchmark lift.

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SkillScore

Safety

What it changes

Track record

Use this skill

Copy Editing

Core Philosophy

The Seven Sweeps Framework

Sweep 1: Clarity

Sweep 2: Voice and Tone

Sweep 3: So What

Sweep 4: Prove It

Sweep 5: Specificity

Sweep 6: Heightened Emotion

Sweep 7: Zero Risk

Expert Panel Scoring

How It Works

Recommended Expert Panels

Scoring Rubric

When to Use

Quick-Pass Editing Checks

Word-Level Checks

Sentence-Level Checks

Paragraph-Level Checks

AI-Tell Check

Copy Editing Checklist

Common Copy Problems & Fixes

Problem: Wall of Features

Problem: Corporate Speak

Problem: Weak Opening

Problem: Buried CTA

Problem: No Proof

Problem: Generic Claims

Problem: Mixed Audiences

Problem: Reads as AI-Written

Problem: Feature Overload

Working with Copy Sweeps

References

Content Refresh Editing

Task-Specific Questions

Related Skills

When to Use Each Skill

SkillSafety

Source

Other skills for the same job

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