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OSS Security Forensics Skill · nousresearch | DecimalAI

Securityby nousresearch

OSS Security Forensics Skill

Supply chain investigation, evidence recovery, and forensic analysis for GitHub repositories. Covers deleted commit recovery, force-push detection, IOC extraction, multi-source evidence collection, hypothesis formation/validation, and structured forensic reporting. Inspired by RAPTOR's 1800+ line OSS Forensics system.

Maintained

SkillScore

SkillScore89/100

Test results 89

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

85%89%of test cases passed +4 pts

Without skill23 of 27

With skill24 of 27

2 fixed22 passed either way2 still failing1 broke

Measured on gemini-3.6-flash8/17/2026v225 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWhen analyzing `PushEvent` records in the GitHub Archive via BigQuery, what specific payload field and value indicates that commits were force-erased?

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 nousresearch-oss-forensics
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("nousresearch-oss-forensics")
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 nousresearch-oss-forensics

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/nousresearch-oss-forensics/SKILL.md https://app.decimal.ai/s/nousresearch-oss-forensics/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/nousresearch-oss-forensics/SKILL.md

This gets SKILL.md only. The skill's 7 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 nousresearch-oss-forensics

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("nousresearch-oss-forensics")
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/nousresearch-oss-forensics/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/nousresearch-oss-forensics/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 7 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 nousresearch-oss-forensics

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("nousresearch-oss-forensics")
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/nousresearch-oss-forensics/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/nousresearch-oss-forensics/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 7 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 nousresearch-oss-forensics

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("nousresearch-oss-forensics")
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/nousresearch-oss-forensics/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/nousresearch-oss-forensics/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 7 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 nousresearch-oss-forensics

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("nousresearch-oss-forensics")
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/nousresearch-oss-forensics/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/nousresearch-oss-forensics/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 7 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 nousresearch-oss-forensics

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("nousresearch-oss-forensics")
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/nousresearch-oss-forensics/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/nousresearch-oss-forensics/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 7 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 nousresearch-oss-forensics

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("nousresearch-oss-forensics")
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/nousresearch-oss-forensics/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/nousresearch-oss-forensics/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 7 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 nousresearch-oss-forensics

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("nousresearch-oss-forensics")
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/nousresearch-oss-forensics/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 7 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 nousresearch-oss-forensics

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("nousresearch-oss-forensics")
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/nousresearch-oss-forensics/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/nousresearch-oss-forensics/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 7 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 nousresearch-oss-forensics

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("nousresearch-oss-forensics")
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/nousresearch-oss-forensics/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/nousresearch-oss-forensics/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 7 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 nousresearch-oss-forensics

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("nousresearch-oss-forensics")
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/nousresearch-oss-forensics/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/nousresearch-oss-forensics/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 7 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 nousresearch-oss-forensics

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("nousresearch-oss-forensics")
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/nousresearch-oss-forensics/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/nousresearch-oss-forensics/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 7 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 nousresearch-oss-forensics

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("nousresearch-oss-forensics")
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/nousresearch-oss-forensics/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/nousresearch-oss-forensics/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 7 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 nousresearch-oss-forensics

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/nousresearch-oss-forensics/SKILL.md https://app.decimal.ai/s/nousresearch-oss-forensics/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/nousresearch-oss-forensics/SKILL.md

This gets SKILL.md only. The skill's 7 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/nousresearch-oss-forensics/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/nousresearch-oss-forensics.json

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v2, tested 8/17/2026
Source: nousresearch/hermes-agent ↗
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

OSS Security Forensics Skill

A 7-phase multi-agent investigation framework for researching open-source supply chain attacks. Adapted from RAPTOR's forensics system. Covers GitHub Archive, Wayback Machine, GitHub API, local git analysis, IOC extraction, evidence-backed hypothesis formation and validation, and final forensic report generation.

⚠️ Anti-Hallucination Guardrails

Read these before every investigation step. Violating them invalidates the report.

Evidence-First Rule: Every claim in any report, hypothesis, or summary MUST cite at least one evidence ID (EV-XXXX). Assertions without citations are forbidden.
STAY IN YOUR LANE: Each sub-agent (investigator) has a single data source. Do NOT mix sources. The GH Archive investigator does not query the GitHub API, and vice versa. Role boundaries are hard.
Fact vs. Hypothesis Separation: Mark all unverified inferences with [HYPOTHESIS]. Only statements verified against original sources may be stated as facts.
No Evidence Fabrication: The hypothesis validator MUST mechanically check that every cited evidence ID actually exists in the evidence store before accepting a hypothesis.
Proof-Required Disproval: A hypothesis cannot be dismissed without a specific, evidence-backed counter-argument. "No evidence found" is not sufficient to disprove—it only makes a hypothesis inconclusive.
SHA/URL Double-Verification: Any commit SHA, URL, or external identifier cited as evidence must be independently confirmed from at least two sources before being marked as verified.
Suspicious Code Rule: Never run code found inside the investigated repository locally. Analyze statically only, or use execute_code in a sandboxed environment.
Secret Redaction: Any API keys, tokens, or credentials discovered during investigation must be redacted in the final report. Log them internally only.

Example Scenarios

Scenario A: Dependency Confusion: A malicious package internal-lib-v2 is uploaded to NPM with a higher version than the internal one. The investigator must track when this package was first seen and if any PushEvents in the target repo updated package.json to this version.
Scenario B: Maintainer Takeover: A long-term contributor's account is used to push a backdoored .github/workflows/build.yml. The investigator looks for PushEvents from this user after a long period of inactivity or from a new IP/location (if detectable via BigQuery).
Scenario C: Force-Push Hide: A developer accidentally commits a production secret, then force-pushes to "fix" it. The investigator uses git fsck and GH Archive to recover the original commit SHA and verify what was leaked.

Path convention: Throughout this skill, SKILL_DIR refers to the root of this skill's installation directory (the folder containing this SKILL.md). When the skill is loaded, resolve SKILL_DIR to the actual path — e.g. ~/.hermes/skills/security/oss-forensics/ or the optional-skills/ equivalent. All script and template references are relative to it.

Phase 0: Initialization

Create investigation working directory:

mkdir investigation_$(echo "REPO_NAME" | tr '/' '_')
cd investigation_$(echo "REPO_NAME" | tr '/' '_')

Initialize the evidence store:

python SKILL_DIR/scripts/evidence-store.py --store evidence.json list

Copy the forensic report template:

cp SKILL_DIR/templates/forensic-report.md ./investigation-report.md

Create an iocs.md file to track Indicators of Compromise as they are discovered.
Record the investigation start time, target repository, and stated investigation goal.

Phase 1: Prompt Parsing and IOC Extraction

Goal: Extract all structured investigative targets from the user's request.

Actions:

Parse the user prompt and extract:
- Target repository (owner/repo)
- Target actors (GitHub handles, email addresses)
- Time window of interest (commit date ranges, PR timestamps)
- Provided Indicators of Compromise: commit SHAs, file paths, package names, IP addresses, domains, API keys/tokens, malicious URLs
- Any linked vendor security reports or blog posts

Tools: Reasoning only, or execute_code for regex extraction from large text blocks.

Output: Populate iocs.md with extracted IOCs. Each IOC must have:

Type (from: COMMIT_SHA, FILE_PATH, API_KEY, SECRET, IP_ADDRESS, DOMAIN, PACKAGE_NAME, ACTOR_USERNAME, MALICIOUS_URL, OTHER)
Value
Source (user-provided, inferred)

Reference: See evidence-types.md for IOC taxonomy.

Phase 2: Parallel Evidence Collection

Spawn up to 5 specialist investigator sub-agents using delegate_task (batch mode, max 3 concurrent). Each investigator has a single data source and must not mix sources.

Orchestrator note: Pass the IOC list from Phase 1 and the investigation time window in the context field of each delegated task.

Investigator 1: Local Git Investigator

ROLE BOUNDARY: You query the LOCAL GIT REPOSITORY ONLY. Do not call any external APIs.

Actions:

# Clone repository
git clone https://github.com/OWNER/REPO.git target_repo && cd target_repo

# Full commit log with stats
git log --all --full-history --stat --format="%H|%ae|%an|%ai|%s" > ../git_log.txt

# Detect force-push evidence (orphaned/dangling commits)
git fsck --lost-found --unreachable 2>&1 | grep commit > ../dangling_commits.txt

# Check reflog for rewritten history
git reflog --all > ../reflog.txt

# List ALL branches including deleted remote refs
git branch -a -v > ../branches.txt

# Find suspicious large binary additions
git log --all --diff-filter=A --name-only --format="%H %ai" -- "*.so" "*.dll" "*.exe" "*.bin" > ../binary_additions.txt

# Check for GPG signature anomalies
git log --show-signature --format="%H %ai %aN" > ../signature_check.txt 2>&1

Evidence to collect (add via python SKILL_DIR/scripts/evidence-store.py add):

Each dangling commit SHA → type: git
Force-push evidence (reflog showing history rewrite) → type: git
Unsigned commits from verified contributors → type: git
Suspicious binary file additions → type: git

Reference: See recovery-techniques.md for accessing force-pushed commits.

Investigator 2: GitHub API Investigator

ROLE BOUNDARY: You query the GITHUB REST API ONLY. Do not run git commands locally.

Actions:

# Commits (paginated)
curl -s "https://api.github.com/repos/OWNER/REPO/commits?per_page=100" > api_commits.json

# Pull Requests including closed/deleted
curl -s "https://api.github.com/repos/OWNER/REPO/pulls?state=all&per_page=100" > api_prs.json

# Issues
curl -s "https://api.github.com/repos/OWNER/REPO/issues?state=all&per_page=100" > api_issues.json

# Contributors and collaborator changes
curl -s "https://api.github.com/repos/OWNER/REPO/contributors" > api_contributors.json

# Repository events (last 300)
curl -s "https://api.github.com/repos/OWNER/REPO/events?per_page=100" > api_events.json

# Check specific suspicious commit SHA details
curl -s "https://api.github.com/repos/OWNER/REPO/git/commits/SHA" > commit_detail.json

# Releases
curl -s "https://api.github.com/repos/OWNER/REPO/releases?per_page=100" > api_releases.json

# Check if a specific commit exists (force-pushed commits may 404 on commits/ but succeed on git/commits/)
curl -s "https://api.github.com/repos/OWNER/REPO/commits/SHA" | jq .sha

Cross-reference targets (flag discrepancies as evidence):

PR exists in archive but missing from API → evidence of deletion
Contributor in archive events but not in contributors list → evidence of permission revocation
Commit in archive PushEvents but not in API commit list → evidence of force-push/deletion

Reference: See evidence-types.md for GH event types.

Investigator 3: Wayback Machine Investigator

ROLE BOUNDARY: You query the WAYBACK MACHINE CDX API ONLY. Do not use the GitHub API.

Goal: Recover deleted GitHub pages (READMEs, issues, PRs, releases, wiki pages).

Actions:

# Search for archived snapshots of the repo main page
curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO&output=json&limit=100&from=YYYYMMDD&to=YYYYMMDD" > wayback_main.json

# Search for a specific deleted issue
curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO/issues/NUM&output=json&limit=50" > wayback_issue_NUM.json

# Search for a specific deleted PR
curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO/pull/NUM&output=json&limit=50" > wayback_pr_NUM.json

# Fetch the best snapshot of a page
# Use the Wayback Machine URL: https://web.archive.org/web/TIMESTAMP/ORIGINAL_URL
# Example: https://web.archive.org/web/20240101000000*/github.com/OWNER/REPO

# Advanced: Search for deleted releases/tags
curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO/releases/tag/*&output=json" > wayback_tags.json

# Advanced: Search for historical wiki changes
curl -s "https://web.archive.org/cdx/search/cdx?url=github.com/OWNER/REPO/wiki/*&output=json" > wayback_wiki.json

Evidence to collect:

Archived snapshots of deleted issues/PRs with their content
Historical README versions showing changes
Evidence of content present in archive but missing from current GitHub state

Reference: See github-archive-guide.md for CDX API parameters.

Investigator 4: GH Archive / BigQuery Investigator

ROLE BOUNDARY: You query GITHUB ARCHIVE via BIGQUERY ONLY. This is a tamper-proof record of all public GitHub events.

Prerequisites: Requires Google Cloud credentials with BigQuery access (gcloud auth application-default login). If unavailable, skip this investigator and note it in the report.

Cost Optimization Rules (MANDATORY):

ALWAYS run a --dry_run before every query to estimate cost.
Use _TABLE_SUFFIX to filter by date range and minimize scanned data.
Only SELECT the columns you need.
Add a LIMIT unless aggregating.

# Template: safe BigQuery query for PushEvents to OWNER/REPO
bq query --use_legacy_sql=false --dry_run "
SELECT created_at, actor.login, payload.commits, payload.before, payload.head,
       payload.size, payload.distinct_size
FROM \`githubarchive.month.*\`
WHERE _TABLE_SUFFIX BETWEEN 'YYYYMM' AND 'YYYYMM'
  AND type = 'PushEvent'
  AND repo.name = 'OWNER/REPO'
LIMIT 1000
"
# If cost is acceptable, re-run without --dry_run

# Detect force-pushes: zero-distinct_size PushEvents mean commits were force-erased
# payload.distinct_size = 0 AND payload.size > 0 → force push indicator

# Check for deleted branch events
bq query --use_legacy_sql=false "
SELECT created_at, actor.login, payload.ref, payload.ref_type
FROM \`githubarchive.month.*\`
WHERE _TABLE_SUFFIX BETWEEN 'YYYYMM' AND 'YYYYMM'
  AND type = 'DeleteEvent'
  AND repo.name = 'OWNER/REPO'
LIMIT 200
"

Evidence to collect:

Force-push events (payload.size > 0, payload.distinct_size = 0)
DeleteEvents for branches/tags
WorkflowRunEvents for suspicious CI/CD automation
PushEvents that precede a "gap" in the git log (evidence of rewrite)

Reference: See github-archive-guide.md for all 12 event types and query patterns.

Investigator 5: IOC Enrichment Investigator

ROLE BOUNDARY: You enrich EXISTING IOCs from Phase 1 using passive public sources ONLY. Do not execute any code from the target repository.

Actions:

For each commit SHA: attempt recovery via direct GitHub URL (github.com/OWNER/REPO/commit/SHA.patch)
For each domain/IP: check passive DNS, WHOIS records (via web_extract on public WHOIS services)
For each package name: check npm/PyPI for matching malicious package reports
For each actor username: check GitHub profile, contribution history, account age
Recover force-pushed commits using 3 methods (see recovery-techniques.md)

Phase 3: Evidence Consolidation

After all investigators complete:

Run python SKILL_DIR/scripts/evidence-store.py --store evidence.json list to see all collected evidence.
For each piece of evidence, verify the content_sha256 hash matches the original source.
Group evidence by:
- Timeline: Sort all timestamped evidence chronologically
- Actor: Group by GitHub handle or email
- IOC: Link evidence to the IOC it relates to
Identify discrepancies: items present in one source but absent in another (key deletion indicators).
Flag evidence as [VERIFIED] (confirmed from 2+ independent sources) or [UNVERIFIED] (single source only).

Phase 4: Hypothesis Formation

A hypothesis must:

State a specific claim (e.g., "Actor X force-pushed to BRANCH on DATE to erase commit SHA")
Cite at least 2 evidence IDs that support it (EV-XXXX, EV-YYYY)
Identify what evidence would disprove it
Be labeled [HYPOTHESIS] until validated

Common hypothesis templates (see investigation-templates.md):

Maintainer Compromise: legitimate account used post-takeover to inject malicious code
Dependency Confusion: package name squatting to intercept installs
CI/CD Injection: malicious workflow changes to run code during builds
Typosquatting: near-identical package name targeting misspellers
Credential Leak: token/key accidentally committed then force-pushed to erase

For each hypothesis, spawn a delegate_task sub-agent to attempt to find disconfirming evidence before confirming.

Phase 5: Hypothesis Validation

The validator sub-agent MUST mechanically check:

For each hypothesis, extract all cited evidence IDs.
Verify each ID exists in evidence.json (hard failure if any ID is missing → hypothesis rejected as potentially fabricated).
Verify each [VERIFIED] piece of evidence was confirmed from 2+ sources.
Check logical consistency: does the timeline depicted by the evidence support the hypothesis?
Check for alternative explanations: could the same evidence pattern arise from a benign cause?

Output:

VALIDATED: All evidence cited, verified, logically consistent, no plausible alternative explanation.
INCONCLUSIVE: Evidence supports hypothesis but alternative explanations exist or evidence is insufficient.
REJECTED: Missing evidence IDs, unverified evidence cited as fact, logical inconsistency detected.

Rejected hypotheses feed back into Phase 4 for refinement (max 3 iterations).

Phase 6: Final Report Generation

Populate investigation-report.md using the template in forensic-report.md.

Mandatory sections:

Executive Summary: one-paragraph verdict (Compromised / Clean / Inconclusive) with confidence level
Timeline: chronological reconstruction of all significant events with evidence citations
Validated Hypotheses: each with status and supporting evidence IDs
Evidence Registry: table of all EV-XXXX entries with source, type, and verification status
IOC List: all extracted and enriched Indicators of Compromise
Chain of Custody: how evidence was collected, from what sources, at what timestamps
Recommendations: immediate mitigations if compromise detected; monitoring recommendations

Report rules:

Every factual claim must have at least one [EV-XXXX] citation
Executive Summary must state confidence level (High / Medium / Low)
All secrets/credentials must be redacted to [REDACTED]

Phase 7: Completion

Run final evidence count: python SKILL_DIR/scripts/evidence-store.py --store evidence.json list
Archive the full investigation directory.
If compromise is confirmed:
- List immediate mitigations (rotate credentials, pin dependency hashes, notify affected users)
- Identify affected versions/packages
- Note disclosure obligations (if a public package: coordinate with the package registry)
Present the final investigation-report.md to the user.

Ethical Use Guidelines

This skill is designed for defensive security investigation — protecting open-source software from supply chain attacks. It must not be used for:

Harassment or stalking of contributors or maintainers
Doxing — correlating GitHub activity to real identities for malicious purposes
Competitive intelligence — investigating proprietary or internal repositories without authorization
False accusations — publishing investigation results without validated evidence (see anti-hallucination guardrails)

Investigations should be conducted with the principle of minimal intrusion: collect only the evidence necessary to validate or refute the hypothesis. When publishing results, follow responsible disclosure practices and coordinate with affected maintainers before public disclosure.

If the investigation reveals a genuine compromise, follow the coordinated vulnerability disclosure process:

Notify the repository maintainers privately first
Allow reasonable time for remediation (typically 90 days)
Coordinate with package registries (npm, PyPI, etc.) if published packages are affected
File a CVE if appropriate

API Rate Limiting

GitHub REST API enforces rate limits that will interrupt large investigations if not managed.

Authenticated requests: 5,000/hour (requires GITHUB_TOKEN env var or gh CLI auth) Unauthenticated requests: 60/hour (unusable for investigations)

Best practices:

Always authenticate: export GITHUB_TOKEN=ghp_... or use gh CLI (auto-authenticates)
Use conditional requests (If-None-Match / If-Modified-Since headers) to avoid consuming quota on unchanged data
For paginated endpoints, fetch all pages in sequence — don't parallelize against the same endpoint
Check X-RateLimit-Remaining header; if below 100, pause for X-RateLimit-Reset timestamp
BigQuery has its own quotas (10 TiB/day free tier) — always dry-run first
Wayback Machine CDX API: no formal rate limit, but be courteous (1-2 req/sec max)

If rate-limited mid-investigation, record the partial results in the evidence store and note the limitation in the report.

Reference Materials

github-archive-guide.md — BigQuery queries, CDX API, 12 event types
evidence-types.md — IOC taxonomy, evidence source types, observation types
recovery-techniques.md — Recovering deleted commits, PRs, issues
investigation-templates.md — Pre-built hypothesis templates per attack type
evidence-store.py — CLI tool for managing the evidence JSON store
forensic-report.md — Structured report template

About this skill

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0 critical · 0 warning · 0 info — scanner v5 — 8/17/2026

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nousresearch / hermes-agent

optional-skills/security/oss-forensics/SKILL.md

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License MITImported 8/17/2026Version v2Bundled 7 files · scripts

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gadievron/oss-forensics-orchestrationOrchestrates multi-agent forensic investigations on public GitHub repositories, coordinating parallel evidence collection, hypothesis formation, verification, and report generation.Close match+17 pts pass-rate lift

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OSS Security Forensics Skill

⚠️ Anti-Hallucination Guardrails

Example Scenarios

Phase 0: Initialization

Phase 1: Prompt Parsing and IOC Extraction

Phase 2: Parallel Evidence Collection

Investigator 1: Local Git Investigator

Investigator 2: GitHub API Investigator

Investigator 3: Wayback Machine Investigator

Investigator 4: GH Archive / BigQuery Investigator

Investigator 5: IOC Enrichment Investigator

Phase 3: Evidence Consolidation

Phase 4: Hypothesis Formation

Phase 5: Hypothesis Validation

Phase 6: Final Report Generation

Phase 7: Completion

Ethical Use Guidelines

API Rate Limiting

Reference Materials

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