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 agentmods add skills/cognisn/cloudledger/account-inventorynpx skills add Cognisn/cloudledger --skill account-inventorygit clone --depth 1 https://github.com/Cognisn/cloudledgerWrote 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/cognisn/cloudledger/account-inventory)<a href="https://agentmods.dev/skills/cognisn/cloudledger/account-inventory"><img src="https://agentmods.dev/badge/skills/cognisn/cloudledger/account-inventory.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00084 | $0.00785 |
| Opus 5 | $0.00042 | $0.00392 |
| Sonnet 5 | $0.00017 | $0.00157 |
| Haiku 4.5 | $0.00008 | $0.00078 |
Grade A, and why
account-inventory 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 4d 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Account inventory
Report which AWS services a scanned account uses and to what extent — accurate counts, not a sample.
The one rule that matters
Inventory questions are answered by the summary and resource tools, never by
the security tools. get_security_assessment_data and
analyze_service_exposure return findings only — a resource appears there
only if it triggered a check. Using them to count resources will undercount
badly (e.g. reporting 1 EC2 instance when the account has 9, because only one
had a finding). Always start from get_scan_summary.
Workflow
1. Resolve the scan
Use the latest scan unless the user names a scan_id or account. Use
list_scans (it accepts an account_number filter) to find the right scan,
and state which account and scan date you are inventorying.
2. Get the authoritative counts
Call get_scan_summary. Its resource_counts object is the source of truth for
how many of each resource the account has (ec2_instances, vpcs, subnets,
security_groups, s3_buckets, iam_users, iam_roles, route53_hosted_zones,
route53_record_sets, prowler_findings, and so on). Report these counts directly.
A count of 0 means the account does not use that service — state it as "none", not "unknown".
3. Add per-service detail where asked
For "to what extent" questions, enrich the headline counts with the dedicated summaries and resource tools:
get_ec2_summary_by_account— EC2 breakdown per account (types, states).get_lambda_summary,get_workspaces_summary— Lambda and WorkSpaces detail.get_load_balancers,get_nat_gateways,get_internet_gateways,get_auto_scaling_groups— networking and compute resources.get_route53_zones/get_route53_records— DNS footprint.get_s3_lifecycle_policies— S3 configuration.get_organizations_structure,get_sso_permissions,get_directory_services,get_bedrock_resources,analyze_managed_services(ElastiCache, OpenSearch, MSK, DynamoDB) — for broader service coverage.find_unused_resources,analyze_tag_compliance— usage quality, if the user wants it.
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.
- 4d ago First seen · 72 lines · 0 tokens per session scan A 8c80a7b87866
account-inventory is a skill published in the GitHub repository Cognisn/cloudledger (0 stars, last pushed 16d ago), licensed MIT. It adds 84 tokens to every session and 785 once invoked, about $0.0004 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 2026-08-31.
Other skills, from other repositories
aws-cloud-penetration-testing
Penetration test AWS cloud environments for misconfigurations, privilege escalation, data exposure, and lateral movement. Use this skill when assessing AWS accounts for security weaknesses including S3 bucket misconfigurations, IAM policy flaws, EC2 metadata exploitation, Lambda function abuse, and cross-account…
conducting-cloud-infrastructure-penetration-test
Perform a cloud infrastructure penetration test across AWS, Azure, and GCP to identify IAM misconfigurations, exposed storage buckets, insecure serverless functions, and cloud-native attack paths using Pacu, ScoutSuite, and Prowler.
conducting-cloud-infrastructure-penetration-test
Perform a cloud infrastructure penetration test across AWS, Azure, and GCP to identify IAM misconfigurations, exposed storage buckets, insecure serverless functions, and cloud-native attack paths using Pacu, ScoutSuite, and Prowler.
aws-cloud-penetration-testing
Penetration test AWS cloud environments for misconfigurations, privilege escalation, data exposure, and lateral movement. Use this skill when assessing AWS accounts for security weaknesses including S3 bucket misconfigurations, IAM policy flaws, EC2 metadata exploitation, Lambda function abuse, and cross-account…
agentcore-investigation
Investigate Bedrock AgentCore runtime sessions via CloudWatch Logs Insights — resolve session/trace IDs, query OTEL spans, filter noise, build timelines. Use when debugging AgentCore agent sessions, tracing tool calls, or analyzing latency.
detecting-s3-data-exfiltration-attempts
Detecting data exfiltration attempts from AWS S3 buckets by analyzing CloudTrail S3 data events, VPC Flow Logs, GuardDuty findings, Amazon Macie alerts, and S3 access patterns to identify unauthorized bulk downloads and cross-account data transfers.