Ready to supercharge your AI agents?

Install any skill in seconds. Free to start, no credit card required.

Dossier — Decision-Grade Entity Research · alirezarezvani | DecimalAI

Researchby alirezarezvani

Dossier — Decision-Grade Entity Research

Decision-grade entity research skill — produces a hypothesis-tested dossier on a specific company, person, nonprofit, or government org, not a generic profile. Forcing intake makes the user state their hypothesis upfront (what they already believe and want to verify or disprove) so the dossier tests it rather than confirms it. Output is an editable Word document (.docx) with verdict on the hypothesis, identity facts, 12-month activity timeline, network and reputation signals, red flags, conversa

Maintained

SkillScore

SkillScore52/100

Test results 52

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

13%52%of test cases passed +39 pts

Without skill3 of 23

With skill12 of 23

10 fixed2 passed either way10 still failing1 broke

Measured on gemini-3.6-flash9/1/2026v220 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskThe user specifies purpose option 1 (Sales meeting / partnership pitch) during intake Q3. Should intake Q6 regarding sensitivities be asked?

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 alirezarezvani-dossier
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("alirezarezvani-dossier")
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 alirezarezvani-dossier

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/alirezarezvani-dossier/SKILL.md https://app.decimal.ai/s/alirezarezvani-dossier/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/alirezarezvani-dossier/SKILL.md

This gets SKILL.md only. The skill's 6 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 alirezarezvani-dossier

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("alirezarezvani-dossier")
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/alirezarezvani-dossier/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/alirezarezvani-dossier/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 6 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 alirezarezvani-dossier

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("alirezarezvani-dossier")
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/alirezarezvani-dossier/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/alirezarezvani-dossier/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 6 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 alirezarezvani-dossier

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("alirezarezvani-dossier")
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/alirezarezvani-dossier/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/alirezarezvani-dossier/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 6 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 alirezarezvani-dossier

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("alirezarezvani-dossier")
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/alirezarezvani-dossier/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/alirezarezvani-dossier/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 6 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 alirezarezvani-dossier

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("alirezarezvani-dossier")
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/alirezarezvani-dossier/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/alirezarezvani-dossier/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 6 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 alirezarezvani-dossier

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("alirezarezvani-dossier")
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/alirezarezvani-dossier/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/alirezarezvani-dossier/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 6 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 alirezarezvani-dossier

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("alirezarezvani-dossier")
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/alirezarezvani-dossier/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 6 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 alirezarezvani-dossier

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("alirezarezvani-dossier")
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/alirezarezvani-dossier/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/alirezarezvani-dossier/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 6 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 alirezarezvani-dossier

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("alirezarezvani-dossier")
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/alirezarezvani-dossier/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/alirezarezvani-dossier/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 6 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 alirezarezvani-dossier

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("alirezarezvani-dossier")
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/alirezarezvani-dossier/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/alirezarezvani-dossier/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 6 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 alirezarezvani-dossier

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("alirezarezvani-dossier")
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/alirezarezvani-dossier/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/alirezarezvani-dossier/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 6 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 alirezarezvani-dossier

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("alirezarezvani-dossier")
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/alirezarezvani-dossier/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/alirezarezvani-dossier/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 6 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 alirezarezvani-dossier

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/alirezarezvani-dossier/SKILL.md https://app.decimal.ai/s/alirezarezvani-dossier/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/alirezarezvani-dossier/SKILL.md

This gets SKILL.md only. The skill's 6 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/alirezarezvani-dossier/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/alirezarezvani-dossier.json

This gets SKILL.md only. The skill's 6 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v2, tested 9/1/2026
Source: alirezarezvani/claude-skills ↗
License: MIT

Manage it in DecimalAI

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Use this skill

SKILL.md

Dossier — Decision-Grade Entity Research

Portability: Requires WebSearch + WebFetch, Node.js with docx package, and optionally bash_tool + curl for free APIs (SEC EDGAR, GitHub, ProPublica). BYOK MCPs (LinkedIn, Crunchbase, Apollo, Pitchbook, SimilarWeb) are optional enhancements. Works in Claude Code CLI natively.

Non-Generic Framing — The Differentiator

This skill is decision-grade entity research with hypothesis-testing. It refuses to be "tell me about Microsoft". Every invocation forces the user to expose their hypothesis upfront (Q4) so the dossier tests it rather than confirms it.

The use case shape:

"I'm pitching Microsoft Tuesday. My hypothesis is they're consolidating AI spend on their first-party Foundry platform. Validate or disprove, and give me three conversation hooks tied to what you find."

NOT:

"Tell me about Microsoft."

The forcing Q4 — the hypothesis question — is the non-generic anchor. Skip it and the skill produces a Wikipedia summary.

See references/hypothesis_testing_discipline.md for the canon.

Agent Integrity Rules (Research-Pack Convention)

Locked verbatim per PR #657 audit.

Execution discipline. Sequential search calls. WebSearch + WebFetch have looser rate limits than Consensus but still apply 1 q/sec etiquette. Confirm response received before next call.
Source discipline. Cite only sources returned by this session's tool calls. Wikipedia / training knowledge labeled [Background — verify before quoting] and excluded from primary findings count.
Three-count tracking. Queries sent / sources received / sources cited. Plus per-tier breakdown (primary / secondary / tertiary) unique to dossier. Surfaced in audit log.
Retry policy. On failure → wait 3s → retry once → log. After 3 consecutive failures: stop, alert user.
Source reliability tier. Each citation tagged primary (official, SEC, court records) / secondary (mainstream news, trade press) / tertiary (blogs, forums). DOCX surfaces tier on every flag.

Phase 1: Grill-Me Intake (6 forcing questions, one at a time)

Q1 (root) — Subject identity

Who is the subject? Give me the exact name and, if a company, the website or LinkedIn URL. If a person, their LinkedIn URL or a unique identifier (company affiliation + role).

Why I'm asking: Disambiguation. There are 47 John Smiths. There are three companies called "Atlas". I need a specific entity to research.

If user gives only a name, push for a second identifier. Refuse to proceed on ambiguous names.

Q2 (depends on Q1) — Subject type

What kind of subject is this? Pick one: person / company / nonprofit / government org / other.

Why I'm asking: Different source matrices apply. For people I check LinkedIn, GitHub, Scholar, news; for companies I check SEC EDGAR (if public), Crunchbase, news, GitHub for tech orgs; for nonprofits I check Form 990s on ProPublica.

Forcing choice. "Other" requires a one-line description.

Q3 (depends on Q2) — Purpose

What are you preparing for? Pick one:

Sales meeting / partnership pitch

Investment diligence

Acquisition diligence

Journalism / due diligence

Job interview prep

Competitive intelligence

Personal vetting (date, hire, business partner)

Other (specify)

Why I'm asking: The purpose dictates the angle, the depth, and the red-flag sensitivity. Sales prep needs conversation hooks. Investment diligence needs traction signals. Personal vetting needs careful sensitivity boundaries.

Q4 (depends on Q3) — Hypothesis — MANDATORY

What's your hypothesis going in? What do you already believe about this subject, and what do you want to verify or disprove?

Why I'm asking: This is the critical question. A dossier that just confirms what you already think is worthless. By stating your hypothesis upfront, I can search for evidence that would disprove it as well as evidence that supports it — and give you a verdict you can actually use.

Examples:

"I believe Microsoft is consolidating AI spend on first-party Foundry. Verify or disprove."

"I think the CEO is over their head — too much TAM talk, no traction. Test that."

"I believe this nonprofit's overhead ratio is sketchy. Check the 990s."

"I think this person is technical enough to handle a CTO role. Verify."

MANDATORY. If user says "I don't have one", push back once: "Then guess. Commit to a position you can update later. The dossier needs a hypothesis to test, otherwise it's a generic profile and won't help you make a decision."

If still refused: fall back to implicit hypothesis "what's the most surprising thing I could find?" and flag the fallback in audit log.

This question is the non-generic anchor. Skip it and the skill becomes a Wikipedia summary.

Q5 (depends on Q3) — Depth

Time horizon: 5-minute brief or 15-minute decision-grade dossier?

Why I'm asking: Brief mode caps at ~10 searches and skips the network + reputation passes. Decision-grade goes deeper on every section. Pick based on how much skin you have in this decision.

Forcing choice.

Q6 (asked only if Q3 ∈ {journalism, personal vetting}) — Sensitivities

Anything sensitive to exclude? E.g., personal medical, family details, political history, or specific topics off-limits?

Why I'm asking: Some research contexts have ethical constraints. I'd rather know upfront than surface something you'd never share.

Skip for sales/investment/acquisition/competitive intel (low sensitivity); ask for journalism/personal vetting (high sensitivity).

Stop condition: After Q6 (or earlier with dependency skips), commit and start Phase 2. Never re-open intake after Phase 2 begins.

Phase 2: Subject Disambiguation

Before Phase 3, resolve the subject to a specific entity:

For people: confirm LinkedIn URL OR (employer + role + city)
For companies: confirm domain OR (legal name + incorporation jurisdiction)
For nonprofits: confirm EIN OR (legal name + state)
For government orgs: confirm official .gov URL

If still ambiguous after Q1 push-back: halt and re-ask Q1 with disambiguating identifiers. Refuse to proceed.

Phase 3: Source Matrix Selection

Routed by Q2 subject type. See references/subject_type_source_matrix.md for the full canon.

Person

LinkedIn (manual fetch or LinkedIn MCP if BYOK)
Personal website
Twitter/X (rate-limited; degrade gracefully)
GitHub (if technical subject)
Google Scholar (if academic)
News (WebSearch + WebFetch)
Conference talk transcripts, podcasts (WebSearch)

Company

Official website (about, leadership, news, careers)
SEC EDGAR (free API; 10-Ks, 10-Qs, 8-Ks for public co's)
Crunchbase free tier (or Crunchbase MCP if BYOK)
News (WebSearch + WebFetch)
GitHub (for tech orgs)
Glassdoor + Comparably (sentiment; degrade gracefully if scraping blocked)
LinkedIn company page

Nonprofit

ProPublica Nonprofit Explorer (free; Form 990s)
Official website
News
GuideStar (if accessible)

Government org

Official .gov sites
News
ProPublica (for federal agencies)

If a paid MCP is connected (Apollo, Pitchbook, SimilarWeb), use it but mark findings as BYOK-sourced in the audit log.

Phase 4: Hypothesis-Driven Search

Every Phase 4 search MUST be classified as either:

Supporting evidence (confirms hypothesis), OR
Disconfirming evidence (would refute hypothesis)

≥30% of search budget allocated to disconfirming queries. Enforced via scripts/disconfirming_evidence_balance.py.

Example for hypothesis "Microsoft is consolidating AI spend on Foundry":

Supporting: "Microsoft Foundry adoption 2026", "Microsoft AI infrastructure consolidation"
Disconfirming: "Microsoft OpenAI deal renegotiation", "Microsoft AI vendor diversification", "Microsoft third-party model partnerships 2026"

This is what makes the dossier decision-grade rather than confirmation-biased.

For each search:

Record via citation_tracker.py with classification (supporting / disconfirming)
Apply source tier from source_tier_classifier.py to each result URL

Phase 5: 12-Month Activity Timeline

Default 12-month window for activity timeline; deeper for foundational identity.

Categories:

News (acquisitions, hires, departures, product launches)
Funding rounds / financial events
Controversies / legal events
Public statements / strategy shifts

Reverse chronological. Each entry hyperlinked + tiered.

Phase 6: Network + Reputation Signals

Network

Companies: investors (in/out), customers (named), partners
People: co-founders, advisors, mentors, employers, board roles
Nonprofits: funders, board, leadership

5-10 entries, ranked by relevance to hypothesis.

Reputation

Sentiment from news (recent 12 months)
Glassdoor for companies (overall rating + 3 representative reviews)
Peer mentions for people
Caveat: reputation data is noisy; tier accordingly

Phase 7: Red-Flag Pass

Surface but don't sensationalize:

Litigation (court records → primary tier)
Regulatory actions (SEC, DOJ, agency actions → primary)
Unusual departures (key personnel exits within 90 days)
Financial signals (going-concern notes in 10-Ks → primary)
Reputation hits (sustained negative coverage → secondary)

Each flag tiered. Tier shows up next to every flag in the DOCX.

Phase 8: Conversation Hook Generation

3-5 specific hooks tied to actual findings, not generic talking points.

See references/conversation_hook_quality.md for the canon.

❌ Generic	✅ Finding-tied
"Ask about their roadmap"	"Mention their recent acquisition of [X] — it signals they're investing in vertical Y. Suggested framing: 'Saw the [X] announcement — how does that change your roadmap on Y?'"
"Ask about hiring"	"Their VP Engineering left 3 weeks ago (LinkedIn). Suggested framing: 'I noticed [name] moved on — what's the eng leadership plan?'"
"Talk about their values"	"They updated their pricing page last week (their official site). Suggested framing: 'Saw the pricing refresh — what drove that?'"

Each hook:

The hook (one sentence)
The finding it's tied to (with hyperlink + tier)
Suggested framing (verbatim phrasing user can adapt)

Phase 9: DOCX Generation (9 Sections)

Via Node.js + docx library.

Executive Summary — one paragraph: who they are + why they matter + verdict on the hypothesis (SUPPORTED / PARTIALLY SUPPORTED / DISPROVEN / INCONCLUSIVE) + 3 things-you-should-know bullets.
Identity Facts Table — founded/born, location, size/stage, current role, key affiliations. All cells sourced; hover-text tier.
Hypothesis Test — user's hypothesis stated verbatim. Supporting evidence (3-5 bullets with hyperlinked citations). Disconfirming evidence (3-5 bullets with hyperlinked citations). Verdict paragraph (2-3 sentences explaining the weight).
12-Month Activity Timeline — News, funding, hires, departures, product launches, controversies. Reverse chronological. Each entry hyperlinked.
Network Signals — Collaborators / investors / associates. 5-10 entries, ranked by relevance to hypothesis.
Reputation Signals — Sentiment from news, Glassdoor for companies, peer mentions for people. Caveat: reputation data is noisy.
Red Flags + Hidden Patterns — Litigation, regulatory actions, unusual departures, financial signals, reputation hits. Tiered.
Conversation Hooks — 3-5 specific hooks tied to findings. Each: hook + finding + suggested framing.
Source Provenance + Audit Log — Per-source list with tier. Search summary table (#, query, classification, sources returned, sources cited). Three counts + per-tier counts. Failed searches. BYOK-MCP usage flag.

Styling

Arial 12pt body, navy headings (#1a3a5c), light blue table headers (#e8f0f8), red red-flag callout, green conversation-hook callout.

Hyperlink patterns

new ExternalHyperlink({
  link: "https://...",
  children: [new TextRun({ text: title, style: "Hyperlink" })],
});

Phase 10: Deliver

Save: <output-dir>/dossier_<entity-slug>_<YYYY-MM-DD>.docx
Chat summary: file path + verdict on hypothesis + audit counts + tier breakdown + BYOK MCPs used (if any)
Validate: check zip integrity with python3 -c "import zipfile,sys; zipfile.ZipFile(sys.argv[1]).testzip()" <docx> (no output = intact), then confirm the required sections are present

Tooling

Script	Role
`scripts/citation_tracker.py`	Three-count audit + supporting/disconfirming classification + source-tier tagging at `~/.dossier_sessions/<session>.json`
`scripts/disconfirming_evidence_balance.py`	Verifies ≥30% of search budget allocated to disconfirming queries; warns if biased
`scripts/source_tier_classifier.py`	URL → primary / secondary / tertiary classification via domain heuristics

References

references/hypothesis_testing_discipline.md — ≥30% rule + decision-grade vs encyclopedic (7+ sources)
references/subject_type_source_matrix.md — person/company/nonprofit/gov source matrices (7+ sources)
references/conversation_hook_quality.md — finding-tied hook discipline (7+ sources)

Error Handling

Failure	Behavior
Subject name ambiguous	Refuse to proceed. Re-ask Q1 with disambiguating identifier.
User refuses to state hypothesis	Push back once. If still refused, fall back to "what's the most surprising thing I could find?" implicit hypothesis. Flag in audit.
Subject has zero public footprint	Surface explicitly. Suggest different name or early-stage. Don't fabricate.
LinkedIn scrape blocked	Note in audit; fall back to WebSearch; suggest user verify manually.
SEC EDGAR fails	Retry once. If still failing, note "public filings not retrieved" and continue.
Sentiment data sparse	Mark reputation section as "limited public signal"; don't infer from training.
Sensitive topic surfaces (Q6 exclusion)	Exclude from DOCX. Note in chat (not in DOCX) so user knows the exclusion was honored.
3 consecutive tool failures	Stop, alert user, share collected so far.
DOCX generation fails	Save raw data as JSON fallback.

Anti-Patterns To Reject

Producing a dossier without forcing Q4 hypothesis
Allocating <30% of search budget to disconfirming evidence
Batching intake questions
Accepting ambiguous subject names
Generic conversation hooks ("ask about their roadmap")
Sensationalizing red flags (tier them, don't editorialize)
Skipping the source-reliability tier on flags
Fabricating coverage when LinkedIn or scraping is blocked
Using BYOK-MCP data without flagging in audit log
Including sensitive topics user excluded in Q6
Confirmation-biased verdict ("SUPPORTED" without engaging with disconfirming evidence)

Version: 1.0.0 Source spec: megaprompts/12-dossier-megaprompt.md (maintainer-local draft spec — gitignored, not present in the public repository) Build pattern: Path B (direct conversion). Research-pack sibling, hypothesis-testing variant.

About this skill

Automatic

Research pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v5 — 9/1/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

alirezarezvani / claude-skills

.gemini/skills/dossier/SKILL.md

View on GitHub ↗

License MITImported 11/7/2026Version v2Bundled 6 files · scripts

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

Related skills

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

From the same source and nearby in the catalogue.

Ready to supercharge your AI agents?

SkillScore

Safety

What it changes

Track record

Use this skill

Dossier — Decision-Grade Entity Research

Non-Generic Framing — The Differentiator

Agent Integrity Rules (Research-Pack Convention)

Phase 1: Grill-Me Intake (6 forcing questions, one at a time)

Q1 (root) — Subject identity

Q2 (depends on Q1) — Subject type

Q3 (depends on Q2) — Purpose

Q4 (depends on Q3) — Hypothesis — MANDATORY

Q5 (depends on Q3) — Depth

Q6 (asked only if Q3 ∈ {journalism, personal vetting}) — Sensitivities

Phase 2: Subject Disambiguation

Phase 3: Source Matrix Selection

Person

Company

Nonprofit

Government org

Phase 4: Hypothesis-Driven Search

Phase 5: 12-Month Activity Timeline

Phase 6: Network + Reputation Signals

Network

Reputation

Phase 7: Red-Flag Pass

Phase 8: Conversation Hook Generation

Phase 9: DOCX Generation (9 Sections)

Styling

Hyperlink patterns

Phase 10: Deliver

Tooling

References

Error Handling

Anti-Patterns To Reject

SkillSafety

Source

Related skills

More in the registry