Agent Bom

What it does

When your agent uses it

• The user mentions vulnerability scanning
• MCP server trust
• SBOM generation
• AI supply chain risk

Example prompts

• “/agent-bom”

Requirements

• Python 3
• Docker
• A credential in SNYK_TOKEN
• A credential in AZURE_CLIENT_SECRET
• Compatibility (from SKILL.md): Requires Python 3.11+. Install via pipx or pip. No credentials required for basic scanning. Native container image scanning — no external scanner required. CIS benchmark checks optionally use cloud SDK credentials (AWS/Azure/GCP/Snowflake).

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Shell commands in SKILL.md call:

  • pipx
  • pip

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Hosts in commands or code, which the agent is likely to contact:

  • github.com

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

• Compatibility

  Requires Python 3.11+. Install via pipx or pip. No credentials required for basic scanning. Native container image scanning — no external scanner required. CIS benchmark checks optionally use cloud SDK credentials (AWS/Azure/GCP/Snowflake).

  From compatibility in the SKILL.md frontmatter.

Context cost

Agent Bom loads about 4.4k tokens when it runs. Until then it costs about 106 tokens; SKILL.md has 845 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~106

When it runs · the whole SKILL.md, loaded when a task matches

~4.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its Apache-2.0 licence (© LeoYeAI). 845 words, ~4,374 tokens.

name

agent-bom

description

Open security platform for agentic infrastructure — broad scanning plus MCP discovery, CVEs, blast radius, SBOMs, CIS benchmarks (AWS, Azure, GCP, Snowflake), OWASP/NIST/MITRE compliance, AISVS v1.0, MAESTRO layer tagging, and vector database security checks. Use when the user mentions vulnerability scanning, MCP server trust, compliance, SBOM generation, CIS benchmarks, blast radius, or AI supply chain risk.

compatibility

Requires Python 3.11+. Install via pipx or pip. No credentials required for basic scanning. Native container image scanning — no external scanner required. CIS benchmark checks optionally use cloud SDK credentials (AWS/Azure/GCP/Snowflake).

version

0.75.10

license

Apache-2.0

metadata.author

msaad00

metadata.homepage

https://github.com/msaad00/agent-bom

metadata.source

https://github.com/msaad00/agent-bom

metadata.pypi

https://pypi.org/project/agent-bom/

metadata.scorecard

https://securityscorecards.dev/viewer/?uri=github.com/msaad00/agent-bom

metadata.tests

6533

pipx install agent-bom
agent-bom agents            # auto-discover + scan
agent-bom check langchain==0.1.0  # check a specific package with version
agent-bom fs .              # scan filesystem packages
agent-bom image nginx:1.25  # scan container image (native)
agent-bom cloud aws         # AWS CIS benchmark
agent-bom iac infra/        # scan Terraform/CloudFormation
agent-bom where             # show all discovery paths

{
  "mcpServers": {
    "agent-bom": {
      "command": "uvx",
      "args": ["agent-bom", "mcp"]
    }
  }
}

# Check a package before installing
check(package="@modelcontextprotocol/server-filesystem", ecosystem="npm")

# Map blast radius of a CVE
blast_radius(cve_id="CVE-2024-21538")

# Full agent discovery + scan
agents()

# Run CIS benchmark
cis_benchmark(provider="aws")

# Run AISVS v1.0 compliance
aisvs_benchmark()

# Scan vector databases for auth misconfigurations
vector_db_scan()

# Discover GPU containers, K8s GPU nodes, and unauthenticated DCGM endpoints
gpu_infra_scan()

# Assess trust of a skill file
skill_trust(skill_content="<paste SKILL.md content>")

# Step 1: Install
pip install agent-bom

# Step 2: Review redaction logic BEFORE scanning
# sanitize_env_vars() replaces ALL env var values with ***REDACTED***
# BEFORE any config data is processed or stored:
# https://github.com/msaad00/agent-bom/blob/main/src/agent_bom/security.py#L159

# Step 3: Review config parsing — only structural data extracted:
# https://github.com/msaad00/agent-bom/blob/main/src/agent_bom/discovery/__init__.py

# Step 4: Verify package provenance (Sigstore)
agent-bom verify agent-bom

# Step 5: Only then run scans
agent-bom agents

© LeoYeAI, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/agent-bom of LeoYeAI/openclaw-master-skills.

• SKILL.md
• _meta.json

Open the folder on GitHubat commit e5199b5