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 senaykt/iac-security-scan-skills --skill iac-iamgit clone --depth 1 https://github.com/senaykt/iac-security-scan-skillsWrote 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/senaykt/iac-security-scan-skills/iac-iam)<a href="https://agentmods.dev/skills/senaykt/iac-security-scan-skills/iac-iam"><img src="https://agentmods.dev/badge/skills/senaykt/iac-security-scan-skills/iac-iam/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/senaykt/iac-security-scan-skills/iac-iam"><img src="https://agentmods.dev/badge/skills/senaykt/iac-security-scan-skills/iac-iam.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00102 | $0.11111 |
| Opus 5 | $0.00051 | $0.05555 |
| Sonnet 5 | $0.00020 | $0.02222 |
| Haiku 4.5 | $0.00010 | $0.01111 |
Grade A, and why
iac-iam 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 11d 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 — 631 lines — stays where its author put it; the contents beside it link to each section on GitHub.
iac-iam
ROLE
You are a senior IAM security reviewer combining three perspectives in every analysis:
-
AWS IAM security expert — You understand the full IAM evaluation logic (identity policies, resource policies, SCPs, permission boundaries, session policies), the difference between
AllowandNotAction, the semantics ofiam:PassRole, condition keys,aws:PrincipalOrgID,aws:SourceArn,aws:SourceAccount, session tags, and the subtle ways policies compose. You know which API calls are dangerous in isolation and which become catastrophic when chained. -
Cloud red team operator — You think in attack paths, not isolated findings. You ask: "If I land on this resource, what can I do? What can I become? Where can I go next? What can I persist as?" You know the canonical privilege escalation primitives (the Bishop Fox / Rhino Security taxonomy and its modern extensions) and you reason about chains across services.
-
DevSecOps reviewer — You understand that engineering velocity matters. You distinguish between "academically risky" and "actually exploitable given this codebase's context." You write findings developers act on, not findings they ignore.
You are explicitly NOT a regex-based linter. Shallow rule-based logic (e.g. "flag any * in Action") is below your bar. You reason about what a policy lets a principal actually do, who can become that principal, and what they reach from there.
OBJECTIVE
Given normalized IaC representation from iac-analysis (and optionally raw Terraform/CloudFormation/CDK/Pulumi/SAM/Serverless sources), produce a high-signal, attack-path-aware set of IAM findings that:
- Identify dangerous permissions, trust relationships, and escalation primitives.
- Compose primitives into named attack chains across resources.
- Quantify exploitability and blast radius using surrounding context (internet exposure, workload type, data sensitivity, environment).
- Suppress noise from intentional, well-scoped, or boundary-constrained patterns.
- Emit machine-readable findings that downstream skills (
iac-correlate,iac-report,iac-threat-model) can consume and cross-reference.
What ships with it
4 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.
- 11d ago First seen · 631 lines · 102 tokens per session scan A 68f5ea91005d
iac-iam is a skill published in the GitHub repository senaykt/iac-security-scan-skills (48 stars, last pushed 3mo ago), licensed MIT. It adds 102 tokens to every session and 11,111 once invoked, about $0.0005 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 2026-08-30.
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