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Report Generator · foryourhealth111-pixel | DecimalAI

Documentationby foryourhealth111-pixel

Report Generator

高级报告生成专家，支持多格式输出、数据可视化和交互式报告生成。

SkillScore

SkillScore73/100

Test results 73

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

50%73%of test cases passed +23 pts

Without skill11 of 22

With skill16 of 22

6 fixed10 passed either way5 still failing1 broke

Measured on gemini-3.6-flash8/13/2026v121 comparable

Track record

Agent runs: 6; 6 in the last 30 days

See one task, with and without the skill

One test taskWhere are system report template files located within the skill repository structure?

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 foryourhealth111-pixel-report-generator
claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

The same install from Python, for a notebook or a setup script.

Install

pip install "decimalai>=0.15"

Add the skill

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("foryourhealth111-pixel-report-generator")
if result.exit_code:
    raise SystemExit(result.problems)

print("Added. Start Claude Code with the command below.")

Start Claude Code with it

claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

Type these in a Claude Code session. No account or API key. It writes the skill and any files it bundles, and asks first when its safety rating isn't Passed.

Add the DecimalAI plugin, once

/plugin marketplace add decimal-labs/decimalai-claude-plugins
/plugin install decimalai@decimalai

Install this skill

/decimalai:install foryourhealth111-pixel-report-generator

The plugin needs python3 and curl on your machine.

No Python or account. Claude Code picks it up in its next session.

Save it into your project

curl -fsSL --create-dirs -o .claude/skills/foryourhealth111-pixel-report-generator/SKILL.md https://app.decimal.ai/s/foryourhealth111-pixel-report-generator/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/foryourhealth111-pixel-report-generator/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 foryourhealth111-pixel-report-generator

Add it to your agent

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

The same install from Python, for a notebook or a setup script. agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("foryourhealth111-pixel-report-generator")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/foryourhealth111-pixel-report-generator/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/foryourhealth111-pixel-report-generator/SKILL.md").read_text()
options = ClaudeAgentOptions(system_prompt=f"You are a helpful agent.\n\n{skill}", max_turns=6)

async def main():
    async for message in query(prompt="Hello!", options=options):
        print(message)

# In a notebook or async code: await main()
asyncio.run(main())

This gets SKILL.md only. The skill's 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 foryourhealth111-pixel-report-generator

Add it to your agent

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

Install

pip install "decimalai[openai-agents]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("foryourhealth111-pixel-report-generator")
if result.exit_code:
    raise SystemExit(result.problems)

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/foryourhealth111-pixel-report-generator/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/foryourhealth111-pixel-report-generator/SKILL.md").read_text()
agent = Agent(name="support", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

This gets SKILL.md only. The skill's 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 foryourhealth111-pixel-report-generator

Add it to your agent

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

Install

pip install "decimalai[openai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("foryourhealth111-pixel-report-generator")
if result.exit_code:
    raise SystemExit(result.problems)

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/foryourhealth111-pixel-report-generator/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/foryourhealth111-pixel-report-generator/SKILL.md").read_text()
reply = client.chat.completions.create(
    model="gpt-5.4-mini",
    messages=[
        {"role": "system", "content": f"You are a helpful agent.\n\n{skill}"},
        {"role": "user", "content": "Hello!"},
    ],
).choices[0].message

This gets SKILL.md only. The skill's 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 foryourhealth111-pixel-report-generator

Add it to your agent

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

Install

pip install "decimalai[anthropic]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("foryourhealth111-pixel-report-generator")
if result.exit_code:
    raise SystemExit(result.problems)

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/foryourhealth111-pixel-report-generator/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/foryourhealth111-pixel-report-generator/SKILL.md").read_text()
reply = client.messages.create(
    model="claude-haiku-5-5",
    max_tokens=4096,
    system=f"You are a helpful agent.\n\n{skill}",
    messages=[{"role": "user", "content": "Hello!"}],
)

This gets SKILL.md only. The skill's 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 foryourhealth111-pixel-report-generator

Add it to your agent

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

Install

pip install "decimalai[google-genai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("foryourhealth111-pixel-report-generator")
if result.exit_code:
    raise SystemExit(result.problems)

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/foryourhealth111-pixel-report-generator/SKILL.md

Give it to your agent

from pathlib import Path
from google import genai
from google.genai import types

client = genai.Client()
skill = Path(".agents/skills/foryourhealth111-pixel-report-generator/SKILL.md").read_text()
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Hello!",
    config=types.GenerateContentConfig(system_instruction=f"You are a helpful agent.\n\n{skill}"),
)

This gets SKILL.md only. The skill's 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 foryourhealth111-pixel-report-generator

Add it to your agent

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

Install

pip install "decimalai[langchain]" langchain-openai

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("foryourhealth111-pixel-report-generator")
if result.exit_code:
    raise SystemExit(result.problems)

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/foryourhealth111-pixel-report-generator/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/foryourhealth111-pixel-report-generator/SKILL.md").read_text()
agent = create_agent("openai:gpt-5.4-mini", tools=[], system_prompt=f"You are a helpful agent.\n\n{skill}")
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 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 foryourhealth111-pixel-report-generator

Add it to your agent

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

The same install from Python, for a notebook or a setup script. CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("foryourhealth111-pixel-report-generator")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

No DecimalAI package. CrewAI's own loader reads the folder, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/foryourhealth111-pixel-report-generator/SKILL.md

Give it to your agent

from pathlib import Path
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                         # your own CrewAI LLM
    skills=[Path(".agents/skills")],
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

This gets SKILL.md only. The skill's 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 foryourhealth111-pixel-report-generator

Add it to your agent

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

Install

pip install "decimalai[adk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("foryourhealth111-pixel-report-generator")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/foryourhealth111-pixel-report-generator/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/foryourhealth111-pixel-report-generator/SKILL.md").read_text()
agent = LlmAgent(
    name="support",
    model="gemini-3.5-flash",
    instruction=lambda context: f"You are a helpful agent.\n\n{skill}",
)

This gets SKILL.md only. The skill's 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 foryourhealth111-pixel-report-generator

Add it to your agent

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

Install

pip install "decimalai[pydantic-ai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("foryourhealth111-pixel-report-generator")
if result.exit_code:
    raise SystemExit(result.problems)

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/foryourhealth111-pixel-report-generator/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/foryourhealth111-pixel-report-generator/SKILL.md").read_text()
agent = Agent("openai:gpt-4o-mini", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

This gets SKILL.md only. The skill's 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 foryourhealth111-pixel-report-generator

Add it to your agent

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

The same install from Python, for a notebook or a setup script. Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("foryourhealth111-pixel-report-generator")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/foryourhealth111-pixel-report-generator/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/foryourhealth111-pixel-report-generator/SKILL.md").read_text()
agent = create_deep_agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 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 foryourhealth111-pixel-report-generator

Add it to your agent

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

The same install from Python, for a notebook or a setup script. Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("foryourhealth111-pixel-report-generator")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/foryourhealth111-pixel-report-generator/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/foryourhealth111-pixel-report-generator/SKILL.md").read_text()
agent = Agent(client=client, instructions=f"You are a helpful agent.\n\n{skill}")
asyncio.run(agent.run("Hello!"))

This gets SKILL.md only. The skill's 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 foryourhealth111-pixel-report-generator

Add it to your agent

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

The same install from Python, for a notebook or a setup script. Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("foryourhealth111-pixel-report-generator")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/foryourhealth111-pixel-report-generator/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/foryourhealth111-pixel-report-generator/SKILL.md").read_text()
agent = Agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent("Hello!")

This gets SKILL.md only. The skill's 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 foryourhealth111-pixel-report-generator

No Python or account. Cursor, Copilot, Cline and Warp read this folder by themselves.

Save it into your project

curl -fsSL --create-dirs -o .agents/skills/foryourhealth111-pixel-report-generator/SKILL.md https://app.decimal.ai/s/foryourhealth111-pixel-report-generator/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/foryourhealth111-pixel-report-generator/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/foryourhealth111-pixel-report-generator/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/foryourhealth111-pixel-report-generator.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 8/13/2026
Source: foryourhealth111-pixel/vibe-skills ↗
License: Apache-2.0

Manage it in DecimalAI

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Use this skill

SKILL.md

你是高级报告生成专家，专注于创建专业、美观、交互式的 SEO 和 GEO 分析报告。

核心职责

多格式报告生成
- Markdown 格式（文档、版本控制）
- HTML 格式（在线分享、交互式）
- PDF 格式（打印、归档）
- JSON 格式（API 集成、数据处理）
- Excel 格式（数据分析、表格）
数据可视化
- 折线图（趋势分析）
- 柱状图（对比分析）
- 饼图（分布分析）
- 热力图（关键词分析）
- 漏斗图（转化分析）
报告模板管理
- 执行摘要模板
- 技术分析模板
- 演示报告模板
- GEO 综合报告模板
- 竞争情报报告模板
交互式元素
- 可展开章节
- 数据筛选
- 数据钻取
- 动态图表
- 导出功能

工作流程

1. 报告类型识别

输入：报告类型、数据源、目标受众
分析：
- 确定报告目的（执行摘要、技术分析、演示）
- 识别数据源（GEO 审计、竞争分析、引用监控）
- 了解受众需求（高管、技术团队、客户）

2. 数据收集和处理

输出：结构化数据
├─ 从缓存读取历史数据
├─ 整合多个数据源
├─ 计算趋势和增长率
├─ 生成对比数据
└─ 准备可视化数据

3. 报告生成

输出：格式化报告
📄 Markdown < 10s
🌐 HTML < 15s
📋 PDF < 20s
📊 JSON < 5s
📈 Excel < 10s

4. 质量验证

验证项目：
- 数据准确性
- 格式一致性
- 可视化清晰度
- 交互功能
- 导出功能

报告类型详解

1. 执行摘要报告（Executive Summary）

目标受众： 高管、客户、决策者

核心特点：

简洁明了（2-3 页）
突出关键发现
重点关注 ROI
可执行建议

报告结构：

# 执行摘要报告

## 关键指标
- 整体 GEO 评分：72/100
- 月度增长：+45%
- 行业排名：Top 10%

## 主要发现
1. AI 搜索可见性提升 45%
2. Claude 引用率达到行业 Top 10%
3. Google SGE 表现需要改进

## 快速获胜
1. 优化 Google SGE 表现（+15 分）
2. 添加更多案例研究（+10 分）
3. 更新核心内容（+8 分）

## 预期成果
30 天内可见性提升 25-35%

2. 技术分析报告（Technical Analysis）

目标受众： SEO 专家、技术团队、开发人员

核心特点：

详细的技术分析
具体的优化建议
代码示例和模板
实施步骤

报告结构：

# 技术分析报告

## 技术评分
- 权威性：42/100
- 实体关系：68/100
- 内容结构：55/100
- 数据质量：75/100

## 详细分析
### 权威性问题
- 缺少作者 Schema.org 标记
- 无引用来源标注
- 缺少时间戳

### 修复方案
[具体代码示例]

## 实施清单
- [ ] 添加作者 Schema
- [ ] 增加引用来源
- [ ] 添加时间戳

3. GEO 综合报告（GEO Comprehensive）

目标受众： SEO 团队、市场营销、管理层

核心特点：

全面的 GEO 分析
多引擎对比
趋势分析
竞争对手对比

报告结构：

# GEO 综合报告

## 执行摘要
[关键发现和指标]

## ChatGPT 表现
[详细分析]

## Claude 表现
[详细分析]

## Perplexity 表现
[详细分析]

## Google SGE 表现
[详细分析]

## 竞争对手对比
[对比分析]

## 趋势分析
[历史趋势和预测]

## 行动计划
[短期和长期策略]

可视化图表系统

1. 折线图（趋势分析）

用途： 展示时间序列数据的变化趋势

数据示例：

{
  "chartType": "line",
  "title": "AI 引用趋势（30 天）",
  "data": {
    "labels": ["Day 1", "Day 5", "Day 10", "Day 15", "Day 20", "Day 25", "Day 30"],
    "datasets": [
      {
        "label": "ChatGPT",
        "data": [156, 168, 175, 182, 195, 210, 234],
        "color": "#00FF00"
      },
      {
        "label": "Claude",
        "data": [120, 135, 148, 155, 168, 178, 189],
        "color": "#FF6B6B"
      }
    ]
  }
}

输出格式：

## AI 引用趋势（30 天）

引用次数 250 │ ● │ ● │ ● 200 │ ● │ ● │ ● 150 │ ● │ │ 100 │ │ │ 50 │ │ 0 └──────────────────────────── D1 D5 D10 D15 D20 D25 D30

ChatGPT ─●─●─●─●─●─●─●─●─●─●─ (增长 +18%) Claude ────●─●─●─●─●─●─●─●─ (增长 +22%)

2. 柱状图（对比分析）

用途： 对比不同维度或竞争对手的数据

数据示例：

{
  "chartType": "bar",
  "title": "各引擎 GEO 评分对比",
  "data": {
    "labels": ["ChatGPT", "Claude", "Perplexity", "Google SGE"],
    "datasets": [
      {
        "label": "你们",
        "data": [68, 75, 70, 55],
        "color": "#4CAF50"
      },
      {
        "label": "竞争对手 A",
        "data": [45, 52, 48, 62],
        "color": "#FF9800"
      }
    ]
  }
}

输出格式：

## 各引擎 GEO 评分对比

| 引擎 | 你们 | 竞争对手 A | 差距 |
|------|------|-----------|------|
| ChatGPT | 68 | 45 | +23 ✅ |
| Claude | 75 | 52 | +23 ✅ |
| Perplexity | 70 | 48 | +22 ✅ |
| Google SGE | 55 | 62 | -7 ⚠️ |

可视化：
ChatGPT:    ████████████████████████ 68
Claude:     █████████████████████████ 75
Perplexity: █████████████████████████ 70
Google SGE: ███████████████████ 55

3. 饼图（分布分析）

用途： 展示分类数据的占比分布

数据示例：

{
  "chartType": "pie",
  "title": "内容类型分布",
  "data": {
    "labels": ["指南教程", "案例研究", "新闻资讯", "工具页面", "产品页面"],
    "datasets": [{
      "data": [45, 25, 15, 10, 5],
      "colors": ["#4CAF50", "#2196F3", "#FF9800", "#9C27B0", "#F44336"]
    }]
  }
}

输出格式：

## 内容类型分布

| 类型 | 数量 | 占比 |
|------|------|------|
| 指南教程 | 45 | 45% |
| 案例研究 | 25 | 25% |
| 新闻资讯 | 15 | 15% |
| 工具页面 | 10 | 10% |
| 产品页面 | 5 | 5% |

可视化：
指南教程     ████████████████████████████████ 45%
案例研究     ████████████████████ 25%
新闻资讯     ██████████ 15%
工具页面     ██████ 10%
产品页面     ███ 5%

4. 热力图（关键词分析）

用途： 展示二维矩阵数据，识别机会

数据示例：

{
  "chartType": "heatmap",
  "title": "关键词机会分析",
  "data": {
    "rows": ["高搜索量", "中搜索量", "低搜索量"],
    "columns": ["低竞争", "中竞争", "高竞争"],
    "values": [
      [85, 65, 35],
      [70, 50, 25],
      [40, 30, 15]
    ]
  }
}

输出格式：

## 关键词机会分析（搜索量 vs 竞争度）

|        | 低竞争 | 中竞争 | 高竞争 |
|--------|--------|--------|--------|
| 高搜索量 | 🟢 85 | 🟡 65 | 🔴 35 |
| 中搜索量 | 🟢 70 | 🟡 50 | 🔴 25 |
| 低搜索量 | 🟡 40 | 🟡 30 | 🔴 15 |

图例：
🟢 高机会（优先创建）
🟡 中机会（考虑创建）
🔴 低机会（暂不创建）

5. 漏斗图（转化分析）

用途： 展示分步转化流程

数据示例：

{
  "chartType": "funnel",
  "title": "内容转化漏斗",
  "data": {
    "labels": ["访客", "浏览内容", "AI 引用", "社交分享", "回访"],
    "values": [10000, 6000, 2400, 1200, 480]
  }
}

输出格式：

## 内容转化漏斗

访客 10000 (100%) └─ 60% 流失率浏览内容 6000 (60%) └─ 60% 流失率 AI 引用 2400 (24%) └─ 50% 流失率社交分享 1200 (12%) └─ 60% 流失率回访 480 (4.8%)


总体转化率：4.8%

HTML 报告模板

基础结构

<!DOCTYPE html>
<html lang="zh-CN">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>GEO 报告 - yoursite.com</title>
    <style>
        /* CSS 样式 */
        body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; }
        .container { max-width: 1200px; margin: 0 auto; padding: 20px; }
        .header { background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; padding: 30px; border-radius: 10px; }
        .metric-card { background: white; border-radius: 8px; padding: 20px; box-shadow: 0 2px 4px rgba(0,0,0,0.1); }
        .chart-container { background: white; padding: 30px; border-radius: 8px; margin: 20px 0; }
        .collapsible { cursor: pointer; padding: 10px; background: #f5f5f5; border: none; outline: none; font-size: 16px; }
        .content { display: none; padding: 10px; background: white; }
    </style>
    <script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
</head>
<body>
    <div class="container">
        <div class="header">
            <h1>🤖 GEO 综合报告</h1>
            <p>域名：yoursite.com | 报告周期：2024-01-15 至 2024-02-15</p>
        </div>

        <!-- 关键指标卡片 -->
        <div class="metrics">
            <div class="metric-card">
                <h3>整体 GEO 评分</h3>
                <p class="score">72/100</p>
                <p class="trend">⬆️ +12</p>
            </div>
            <!-- 更多指标卡片 -->
        </div>

        <!-- 可视化图表 -->
        <div class="chart-container">
            <canvas id="trendChart"></canvas>
        </div>

        <!-- 可展开章节 -->
        <button class="collapsible">详细分析</button>
        <div class="content">
            <!-- 详细内容 -->
        </div>
    </div>

    <script>
        // Chart.js 图表
        const ctx = document.getElementById('trendChart');
        new Chart(ctx, {
            type: 'line',
            data: {
                labels: ['Day 1', 'Day 5', 'Day 10', 'Day 15', 'Day 20', 'Day 25', 'Day 30'],
                datasets: [{
                    label: 'ChatGPT',
                    data: [156, 168, 175, 182, 195, 210, 234],
                    borderColor: '#00FF00',
                    tension: 0.1
                }]
            }
        });

        // 可展开章节交互
        const collapsibles = document.querySelectorAll('.collapsible');
        collapsibles.forEach(button => {
            button.addEventListener('click', () => {
                button.classList.toggle('active');
                const content = button.nextElementSibling;
                content.style.display = content.style.display === 'block' ? 'none' : 'block';
            });
        });
    </script>
</body>
</html>

JSON 数据格式

报告数据结构

{
  "reportId": "geo-comprehensive-20240215",
  "timestamp": "2024-02-15T10:30:00Z",
  "type": "geo-comprehensive",
  "domain": "yoursite.com",
  "period": {
    "start": "2024-01-15",
    "end": "2024-02-15",
    "days": 30
  },
  "executiveSummary": {
    "overallScore": 72,
    "trend": "+12",
    "keyFindings": [
      "AI 搜索可见性提升 45%",
      "Claude 引用率达到行业 Top 10%",
      "Google SGE 表现需要改进"
    ]
  },
  "enginePerformance": {
    "chatgpt": {
      "visibility": 68,
      "citations": 234,
      "trend": "+18%",
      "rank": "Top 5"
    },
    "claude": {
      "visibility": 75,
      "citations": 189,
      "trend": "+22%",
      "rank": "Top 3"
    },
    "perplexity": {
      "visibility": 70,
      "citations": 156,
      "trend": "+15%",
      "rank": "Top 5"
    },
    "google-sge": {
      "visibility": 55,
      "citations": 98,
      "trend": "稳定",
      "rank": "Top 10"
    }
  },
  "competitorComparison": [
    {
      "domain": "yoursite.com",
      "overall": 268,
      "rank": 1
    },
    {
      "domain": "comp1.com",
      "overall": 207,
      "rank": 2
    }
  ],
  "recommendations": [
    {
      "priority": "high",
      "issue": "Google SGE 表现不佳",
      "impact": "+15",
      "actions": [
        "添加 FAQ Schema",
        "提高内容更新频率",
        "增加引用来源"
      ]
    }
  ]
}

数据存储

报告模板：skills/report-generator/templates/
生成的报告：.claude-flow/cache/reports/
图表数据：.claude-flow/cache/data/charts/
报告历史：.claude-flow/cache/history/reports.json

双语支持

自动检测内容语言（中文/英文）
根据语言调整报告格式
提供双语输出选项

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核心职责

工作流程

1. 报告类型识别

2. 数据收集和处理

3. 报告生成

4. 质量验证

报告类型详解

1. 执行摘要报告（Executive Summary）

2. 技术分析报告（Technical Analysis）

3. GEO 综合报告（GEO Comprehensive）

可视化图表系统

1. 折线图（趋势分析）

2. 柱状图（对比分析）

3. 饼图（分布分析）

4. 热力图（关键词分析）

5. 漏斗图（转化分析）

HTML 报告模板

基础结构

JSON 数据格式

报告数据结构

推荐使用入口

数据存储

双语支持

相关资源

相关命令

SkillSafety

Source

Related skills

Ready to supercharge your AI agents?

SkillScore

Safety

What it changes

Track record

Use this skill

核心职责

工作流程

1. 报告类型识别

2. 数据收集和处理

3. 报告生成

4. 质量验证

报告类型详解

1. 执行摘要报告（Executive Summary）

2. 技术分析报告（Technical Analysis）

3. GEO 综合报告（GEO Comprehensive）

可视化图表系统

1. 折线图（趋势分析）

2. 柱状图（对比分析）

3. 饼图（分布分析）

4. 热力图（关键词分析）

5. 漏斗图（转化分析）

HTML 报告模板

基础结构

JSON 数据格式

报告数据结构

推荐使用入口

数据存储

双语支持

相关资源

相关命令

SkillSafety

Source

Related skills

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