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AI Super Intelligence Team — SKILL entry · coco-research | DecimalAI

AI/MLby coco-research

AI Super Intelligence Team — SKILL entry

Rijul's AI research + engineering brain trust **and decision-making partner**. 59 named personas drawn from the world's frontier model labs (US, UK, China, France), top AI universities, the open-source ecosystem, the independent-evaluation and AI-governance ecosystem, and product-history archetypes.

Never made a case worseMaintained

SkillScore

SkillScore86/100

Test results 86

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

36%86%of test cases passed +50 pts

Without skill8 of 22

With skill19 of 22

11 fixed8 passed either way3 still failing0 broke

Measured on gemini-3.6-flash8/30/2026v2

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWe are running a full-roster convene across our entire research panel on sparse attention mechanisms. Will this query run across 100 personas?

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 coco-research-ai-super-intelligence-team-skill-entry
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("coco-research-ai-super-intelligence-team-skill-entry")
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 coco-research-ai-super-intelligence-team-skill-entry

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/coco-research-ai-super-intelligence-team-skill-entry/SKILL.md https://app.decimal.ai/s/coco-research-ai-super-intelligence-team-skill-entry/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/coco-research-ai-super-intelligence-team-skill-entry/SKILL.md

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Claude Code docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add coco-research-ai-super-intelligence-team-skill-entry

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("coco-research-ai-super-intelligence-team-skill-entry")
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/coco-research-ai-super-intelligence-team-skill-entry/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/coco-research-ai-super-intelligence-team-skill-entry/SKILL.md").read_text()
options = ClaudeAgentOptions(system_prompt=f"You are a helpful agent.\n\n{skill}", max_turns=6)

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

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

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Claude Agent SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai-agents]"
decimalai skills add coco-research-ai-super-intelligence-team-skill-entry

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("coco-research-ai-super-intelligence-team-skill-entry")
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/coco-research-ai-super-intelligence-team-skill-entry/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/coco-research-ai-super-intelligence-team-skill-entry/SKILL.md").read_text()
agent = Agent(name="support", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

OpenAI Agents docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai]"
decimalai skills add coco-research-ai-super-intelligence-team-skill-entry

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("coco-research-ai-super-intelligence-team-skill-entry")
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/coco-research-ai-super-intelligence-team-skill-entry/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

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

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

OpenAI SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[anthropic]"
decimalai skills add coco-research-ai-super-intelligence-team-skill-entry

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("coco-research-ai-super-intelligence-team-skill-entry")
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/coco-research-ai-super-intelligence-team-skill-entry/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

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

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Anthropic docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[google-genai]"
decimalai skills add coco-research-ai-super-intelligence-team-skill-entry

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("coco-research-ai-super-intelligence-team-skill-entry")
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/coco-research-ai-super-intelligence-team-skill-entry/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/coco-research-ai-super-intelligence-team-skill-entry/SKILL.md").read_text()
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Hello!",
    config=types.GenerateContentConfig(system_instruction=f"You are a helpful agent.\n\n{skill}"),
)

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Gemini API docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add coco-research-ai-super-intelligence-team-skill-entry

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("coco-research-ai-super-intelligence-team-skill-entry")
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/coco-research-ai-super-intelligence-team-skill-entry/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/coco-research-ai-super-intelligence-team-skill-entry/SKILL.md").read_text()
agent = create_agent("openai:gpt-5.4-mini", tools=[], system_prompt=f"You are a helpful agent.\n\n{skill}")
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

LangChain docs

CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add coco-research-ai-super-intelligence-team-skill-entry

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("coco-research-ai-super-intelligence-team-skill-entry")
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/coco-research-ai-super-intelligence-team-skill-entry/SKILL.md

Give it to your agent

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

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

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

CrewAI docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[adk]"
decimalai skills add coco-research-ai-super-intelligence-team-skill-entry

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("coco-research-ai-super-intelligence-team-skill-entry")
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/coco-research-ai-super-intelligence-team-skill-entry/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/coco-research-ai-super-intelligence-team-skill-entry/SKILL.md").read_text()
agent = LlmAgent(
    name="support",
    model="gemini-3.5-flash",
    instruction=lambda context: f"You are a helpful agent.\n\n{skill}",
)

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Google ADK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add coco-research-ai-super-intelligence-team-skill-entry

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("coco-research-ai-super-intelligence-team-skill-entry")
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/coco-research-ai-super-intelligence-team-skill-entry/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

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

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Pydantic AI docs

Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add coco-research-ai-super-intelligence-team-skill-entry

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("coco-research-ai-super-intelligence-team-skill-entry")
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/coco-research-ai-super-intelligence-team-skill-entry/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/coco-research-ai-super-intelligence-team-skill-entry/SKILL.md").read_text()
agent = create_deep_agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Deep Agents docs

Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add coco-research-ai-super-intelligence-team-skill-entry

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("coco-research-ai-super-intelligence-team-skill-entry")
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/coco-research-ai-super-intelligence-team-skill-entry/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/coco-research-ai-super-intelligence-team-skill-entry/SKILL.md").read_text()
agent = Agent(client=client, instructions=f"You are a helpful agent.\n\n{skill}")
asyncio.run(agent.run("Hello!"))

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Microsoft Agent Framework docs

Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add coco-research-ai-super-intelligence-team-skill-entry

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("coco-research-ai-super-intelligence-team-skill-entry")
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/coco-research-ai-super-intelligence-team-skill-entry/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/coco-research-ai-super-intelligence-team-skill-entry/SKILL.md").read_text()
agent = Agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent("Hello!")

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Strands docs

Adds every file the skill has, pinned so you can update it. Cursor, Copilot, Cline and Warp read this folder by themselves.

pip install "decimalai>=0.15"
decimalai skills add coco-research-ai-super-intelligence-team-skill-entry

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/coco-research-ai-super-intelligence-team-skill-entry/SKILL.md https://app.decimal.ai/s/coco-research-ai-super-intelligence-team-skill-entry/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/coco-research-ai-super-intelligence-team-skill-entry/SKILL.md

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Cursor and other editors docs

Fetch the file and give its text to your model as instructions. No account or package needed.

Get the file

curl -fsSL https://app.decimal.ai/s/coco-research-ai-super-intelligence-team-skill-entry/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/coco-research-ai-super-intelligence-team-skill-entry.json

This gets SKILL.md only. The skill's 2 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v2, tested 8/30/2026
Source: coco-research/coco ↗
License: NOASSERTION

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

AI Super Intelligence Team — SKILL entry

Your AI research + engineering brain trust and decision-making partner. 59 named personas drawn from the world's frontier model labs (US, UK, China, France), top AI universities, the open-source ecosystem, the independent-evaluation and AI-governance ecosystem, and product-history archetypes. Reusable across any CoCo-routed prompt — invoked by /superintelligenceTeam-* slash commands. The team's primary purpose is to help take decisions, not just review work after the fact — every cell carries a decision-relevant lens that convene synthesis explicitly draws on.

Status: All three phases complete.

Phase 1: 49 personas across 8 cells (baseline roster + founding v2 panelists).

Phase 2: 10 priority additions from the EXPANSION.md gap analysis (roster grew to 59).

Phase 3: 22 slash commands installed at ~/.claude/commands/superintelligenceTeam*.md — orchestrator-first action surface plus explicit-override identity surface. See Slash Commands section below.

This file is the user-facing entry point for the team. The machine source of truth is registry.json, regenerated from persona frontmatter by python3 superintelligenceTeam/scripts/build_registry.py.

What this team is for

When a CoCo prompt is high-stakes enough to deserve a parallel review panel, the AI Super Intelligence Team plays the role of named external voices. Instead of "the panel said," every claim is attributed to a specific researcher with documented stances. This lets convene synthesis:

Surface real disagreement (Hinton vs LeCun on existential risk; Karpathy vs Schulman on RL).
Anchor stances to citable evidence (every public_stance in every persona has an evidence_url).
Stay honest about who actually drove which decision (lead-driver vs validator vs specialist vs swing).

The 5-cell × 4-persona founding Memory v2 panel from May 26-27, 2026 was the first consumer. This team supersedes that roster: five of those panelists (Karpathy, Wei, LeCun, Tri Dao, Hyung Won Chung) continue here with their v2_panel_attribution records preserved. The rest of the founding v2 panel (memory / cloud / security / privacy personas) belong to future Super Intelligence Teams (Memory Systems, Cloud, Data, Compliance) and are not part of this AI-focused roster.

Cells (8)

Cell	Count	Focus	File
`frontier-labs-research`	6	CSO / co-founder tier at frontier labs (US, China, France)	cells/frontier-labs-research.md
`applied-ai-leadership`	7	Product / strategy founders shipping AI (incl. archetypes)	cells/applied-ai-leadership.md
`model-architects`	7	Pretraining + scaling + model design + retrieval + open-source	cells/model-architects.md
`reasoning-rl-agents`	7	Post-training, RL, agentic systems, test-time compute	cells/reasoning-rl-agents.md
`alignment-interp-safety`	9	Alignment + mech interp + safety policy + independent evals + governance	cells/alignment-interp-safety.md
`theory-science`	9	DL theory + science + Turing laureates + AI-for-science + common-sense	cells/theory-science.md
`multimodal-embodied`	7	Vision + diffusion + robotics + embodied	cells/multimodal-embodied.md
`systems-kernels-serving`	7	Kernels + serving + GPU + quantization + anti-NVIDIA silicon	cells/systems-kernels-serving.md

Personas (59)

Listed by cell. Each has a YAML-frontmatter profile under personas/<slug>.md plus a research dump under research/<slug>/. Bold marks Phase 2 additions (built 2026-05-28 from EXPANSION.md).

Frontier Labs Research (6): ilya-sutskever · dario-amodei · demis-hassabis · jakub-pachocki · liang-wenfeng · arthur-mensch

Applied AI Leadership (7): sam-altman · mira-murati · greg-brockman · aravind-srinivas · aidan-gomez · elon-musk · steve-jobs (archetype, deceased 2011)

Model Architects (7): andrej-karpathy · jared-kaplan · noam-shazeer · jason-wei · sebastian-raschka · patrick-lewis · thomas-wolf

Reasoning, RL, Agents (7): john-schulman · noam-brown · hyung-won-chung · nathan-lambert · barret-zoph · karina-nguyen · sasha-rush

Alignment, Interp, Safety (9): chris-olah · paul-christiano · jan-leike · dan-hendrycks · stuart-russell · neel-nanda · lilian-weng · beth-barnes · helen-toner

Theory and Science (9): yann-lecun · yoshua-bengio · geoffrey-hinton · john-jumper · percy-liang · christopher-manning · yejin-choi · sara-hooker · aleksander-madry

Multimodal, Embodied (7): fei-fei-li · pieter-abbeel · sergey-levine · chelsea-finn · robin-rombach · aditya-ramesh · prafulla-dhariwal

Systems, Kernels, Serving (7): tri-dao · bryan-catanzaro · andrew-feldman · albert-gu · horace-he · woosuk-kwon · tim-dettmers

Files in this team

superintelligenceTeam/
├── SKILL.md This file — user-facing entry.
├── registry.json Machine source of truth. Read by slash commands.
├── EXPANSION.md (Phase 2) Gap-analysis and roster-expansion candidates.
├── templates/
│ ├── persona.md Schema source-of-truth for every persona file.
│ └── convene.md Multi-persona session template.
├── personas/ 59 *.md files, one per persona. YAML frontmatter + 6 narrative sections.
├── cells/ 8 *.md cell summaries.
├── research/ 59 directories, one per persona. Raw research dumps so future re-syntheses don't recrawl.
└── scripts/
    └── build_registry.py Regenerates registry.json from persona frontmatter. Run after any persona edit.

Slash commands (Phase 3 — installed)

22 slash command files live at ~/.claude/commands/superintelligenceTeam*.md (user-global install, parallel to the existing /team family). Each is auto-registered as a discoverable Skill — no separate SKILL.md registration is needed. The architecture is orchestrator-first: every action verb invokes the orchestrator to pick a custom 16-32 persona team and gate on user approval before executing.

Dispatcher (1)

Command	Purpose
`/superintelligenceTeam`	No args → print roster + cell heatmap. With a subcommand as first token, routes to the sibling file. With free text and no subcommand, defaults to `:meeting`.

Orchestrator (1)

Command	Purpose
`/superintelligenceTeam:orchestrate "<prompt>"`	Standalone team selection. Reads `registry.json`, scores all 59 personas via domain match (40%) + cell coverage (30%) + productive-conflict pairing (30%), picks 16-32, asks for user approval via AskUserQuestion with per-persona one-line rationale. Hard 16-32 size enforcement. Re-picks every invocation. Does NOT load CoCo.

Identity surface — explicit overrides (4)

Command	Purpose	Skips orchestrator?
`/superintelligenceTeam:ask <slug> "<question>"`	1-on-1 with one persona in their voice.	Yes
`/superintelligenceTeam:huddle <cell-slug> "<topic>"`	Whole cell (4-9 personas) synthesizes.	Yes
`/superintelligenceTeam:meeting "<prompt>"`	Full 59-persona convene with mandatory attribution.	Yes
`/superintelligenceTeam:read <slug>`	Print the persona file inline (not voice-channeled).	Yes

Roster management (1)

Command	Purpose
`/superintelligenceTeam:recruit <domain> "<why>"`	Propose 2-3 new persona candidates for an under-covered domain. Reads EXPANSION.md to avoid re-proposing. Does not write personas itself.

Action surface (15) — orchestrator-first by default

All 15 invoke /superintelligenceTeam:orchestrate first unless --no-orchestrate, --cells, or --personas flag is supplied.

Command	Output shape
`/superintelligenceTeam:analyse "<topic>"`	Per-persona analysis + synthesis table of strongest signals
`/superintelligenceTeam:decide "<question>"`	Primary verb. Decision matrix: options × personas × verdict + recommendation + named dissent
`/superintelligenceTeam:review <target>`	Multi-persona findings classified CRITICAL/MAJOR/MINOR/SUGGESTION + ship verdict
`/superintelligenceTeam:re-analyse "<topic>" [--prior <path>] [--evidence <text>]`	Updated stances + diff vs prior analysis
`/superintelligenceTeam:pre-mortem "<plan>"`	Ranked failure modes + early warning signs + mitigations
`/superintelligenceTeam:post-mortem "<what failed>"`	Per-persona 5 Whys + most-likely root cause + remediation plan
`/superintelligenceTeam:full-cycle "<topic>"`	Heaviest verb. Chains `:review → :analyse → mitigate-risk → :decide → finalize` as real subcommand invocations. 5× latency. Finalized action plan.
`/superintelligenceTeam:tradeoff "<A vs B>"`	Side-by-side dimensions × options table + most-opposed cell named
`/superintelligenceTeam:plan "<goal>"`	Phased plan with owner cell + dissenting voices per phase
`/superintelligenceTeam:design "<feature>"`	Component-level architecture with per-decision attribution
`/superintelligenceTeam:vote "<binary question>"`	Yes/no per persona + tally by cell + recommendation
`/superintelligenceTeam:debug "<problem>"`	Ranked root-cause hypotheses + diagnostic test order
`/superintelligenceTeam:stress-test "<proposal>"`	Adversarial attacks per persona + severity matrix + SHIP/HARDEN-THEN-SHIP/REFRAME/DO-NOT-SHIP verdict
`/superintelligenceTeam:defend "<position>" [as <slug>]`	Steelman with sharpened claim + counter-objection rebuttal + "what would change my mind"
`/superintelligenceTeam:roast "<thing>"`	One-line cutting roast per persona + single line that hurt most + convergent critique

Global flags

Every command accepts these:

Flag	Effect
`--no-orchestrate`	Skip orchestrator; use all 59 personas
`--cells <comma-list>`	Manually scope to specific cells; skip orchestrator
`--personas <comma-list>`	Manually scope to specific persona slugs; skip orchestrator

Conventions enforced by every command

Registry is source of truth. Re-read every invocation; no caching.
Attribution at every line. No "the team said" — name a persona or a cell.
Approval gate mandatory for action verbs unless an explicit flag bypasses.
Hard 16-32 size band on orchestrator output.
Full English prose — no caveman compression in command output.

Cross-team note

This is the first Super Intelligence Team in CoCo. Planned follow-ons:

cloud-super-intelligence — AWS / GCP / Azure architecture
finance-super-intelligence — FP&A, accounting, finance ops
coding-super-intelligence — software-engineering practice
design-super-intelligence — UX, design systems, visual design
product-super-intelligence — PM craft, product strategy

All teams share the same schema (teams: [...] array on each persona, functional cell slugs, slash-command surface). A persona may belong to multiple teams — Karpathy will likely also appear in a future coding-super-intelligence team because of his pedagogical reach into engineering practice. The teams: field is an array exactly so this is cheap.

Conventions

Schema source of truth: templates/persona.md. Edit there first; persona files conform.
Registry regeneration: run python3 superintelligenceTeam/scripts/build_registry.py after any persona edit.
Caveman mode does NOT apply to persona files, cell files, or this SKILL.md. Documentation is always full English prose per the project rule.
Citation is mandatory. Every public_stance has an evidence_url. No uncited claims.
v2 panel preservation. Personas with v2_panel_attribution entries anchor to actual material from the founding Memory v2 synthesis on 2026-05-26. Convene uses those first.
Confidence calibration. confidence < 0.85 means we suspect identifier or biographical detail may need re-verification. Persona is still usable but flag in convene.

Build provenance

Forged 2026-05-27 during /coco session.
Quality bar set by Karpathy reference (12 cited URLs, 5 recent_signal_12mo entries, full 6-section narrative).
46 remaining personas built by parallel research sub-agents in 6 waves (8 + 8 + 8 + 8 + 8 + 6).
Several agent runs corrected the user-supplied hints with verified facts (e.g., Lilian Weng's PhD = Indiana not NYU; Sergey Levine's PhD advisor = Vladlen Koltun not Pieter Abbeel; Barret Zoph fired from TML Jan 2026 and returned to OpenAI; Aditya Ramesh stayed at OpenAI Worldsim VP not TML).
All persona files written in full English prose. Caveman mode active in chat throughout.

About this skill

Automatic

AI Engineer pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v5 — 8/26/2026

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live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

coco-research / coco

systems/superintelligence/ai/SKILL.md

View on GitHub ↗

License NOASSERTIONImported 9/18/2026Version v2Bundled 2 files · scripts

Illustrative Want to see what coco-research/ai-super-intelligence-team-skill-entry would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

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What it changes

Track record

Use this skill

AI Super Intelligence Team — SKILL entry

What this team is for

Cells (8)

Personas (59)

Files in this team

Slash commands (Phase 3 — installed)

Dispatcher (1)

Orchestrator (1)

Identity surface — explicit overrides (4)

Roster management (1)

Action surface (15) — orchestrator-first by default

Global flags

Conventions enforced by every command

Cross-team note

Conventions

Build provenance

SkillSafety

Source

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