Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add adriannoes/awesome-agentic-ai --skill performing-cloud-asset-inventory-with-cartographygit clone --depth 1 https://github.com/adriannoes/awesome-agentic-aiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/performing-cloud-asset-inventory-with-cartography)<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/performing-cloud-asset-inventory-with-cartography"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/performing-cloud-asset-inventory-with-cartography/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/performing-cloud-asset-inventory-with-cartography"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/performing-cloud-asset-inventory-with-cartography.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high YARA Match · line 193 YARA rule matched a known malware signature (reverse shell, backdoor, ransomware, C2 framework, or info stealer).Fix: Remove the malware payload or compromised file entirely. Investigate how it entered the skill and audit all other artifacts for additional indicators of compromise.
- medium MCP Rug Pull · line 67 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
- medium Tool Misuse · line 114 Tool defaults are unsafe or overly permissive (e.g. disabled TLS verification, no authentication, world-writable permissions). Unsafe defaults widen the attack surface.Fix: Override unsafe defaults with secure settings (verify=True, auth required, restrictive permissions). Review and harden all tool configurations.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00049 | $0.01877 |
| Opus 5 | $0.00024 | $0.00938 |
| Sonnet 5 | $0.00010 | $0.00375 |
| Haiku 4.5 | $0.00005 | $0.00188 |
Grade A, and why
performing-cloud-asset-inventory-with-cartography scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 256 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performing Cloud Asset Inventory with Cartography
Overview
Cartography is a CNCF sandbox project (originally created at Lyft) that consolidates infrastructure assets and their relationships into a Neo4j graph database. It queries cloud APIs to discover resources, maps relationships between them, and enables security teams to identify attack paths, generate asset reports, and find areas for security improvement. The graph model reveals hidden connections such as IAM permission chains, network paths, and cross-account trust relationships.
When to Use
- When conducting security assessments that involve performing cloud asset inventory with cartography
- When following incident response procedures for related security events
- When performing scheduled security testing or auditing activities
- When validating security controls through hands-on testing
Prerequisites
- Python 3.8+
- Neo4j 4.x or 5.x database
- Cloud provider credentials (AWS, GCP, Azure)
- Docker (optional, for Neo4j deployment)
- Minimum 4GB RAM for Neo4j, more for large environments
Installation
# Install Cartography
pip install cartography
# Verify installation
cartography --help
Deploy Neo4j with Docker
docker run -d \
--name neo4j \
-p 7474:7474 -p 7687:7687 \
-e NEO4J_AUTH=neo4j/changethispassword \
-e NEO4J_PLUGINS='["apoc"]' \
-v neo4j_data:/data \
neo4j:5-community
Running Cartography
Basic AWS Sync
# Sync AWS account data to Neo4j
cartography \
--neo4j-uri bolt://localhost:7687 \
--neo4j-user neo4j \
--neo4j-password-env-var NEO4J_PASSWORD
Sync specific AWS modules
cartography \
--neo4j-uri bolt://localhost:7687 \
--neo4j-user neo4j \
--neo4j-password-env-var NEO4J_PASSWORD \
--aws-sync-all-profiles
GCP Sync
cartography \
--neo4j-uri bolt://localhost:7687 \
--neo4j-user neo4j \
--neo4j-password-env-var NEO4J_PASSWORD \
--gcp-requested-syncs compute iam storage
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 256 lines · 49 tokens per session scan A 5499f9e12d4e
performing-cloud-asset-inventory-with-cartography is a skill published in the GitHub repository adriannoes/awesome-agentic-ai (57 stars, last pushed 14d ago), licensed MIT. It adds 49 tokens to every session and 1,877 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
performing-cloud-asset-inventory-with-cartography
Perform comprehensive cloud asset inventory and relationship mapping using Cartography to build a Neo4j security graph of infrastructure assets, IAM permissions, and attack paths across AWS, GCP, and Azure.
performing-cloud-asset-inventory-with-cartography
Perform comprehensive cloud asset inventory and relationship mapping using Cartography to build a Neo4j security graph of infrastructure assets, IAM permissions, and attack paths across AWS, GCP, and Azure.
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Use when standing up the core AWS surface a small product needs: hardening a fresh account, a private S3 bucket, encrypted RDS Postgres, ECS Fargate vs EC2, CloudFront + OAC, or scoping an IAM policy to least privilege. NOT the CI pipeline that ships the container (that is deployment), NOT app-code access-control…