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 addxai/enterprise-harness-engineering --skill terraform-auditgit clone --depth 1 https://github.com/addxai/enterprise-harness-engineeringWrote 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/addxai/enterprise-harness-engineering/terraform-audit)<a href="https://agentmods.dev/skills/addxai/enterprise-harness-engineering/terraform-audit"><img src="https://agentmods.dev/badge/skills/addxai/enterprise-harness-engineering/terraform-audit/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/addxai/enterprise-harness-engineering/terraform-audit"><img src="https://agentmods.dev/badge/skills/addxai/enterprise-harness-engineering/terraform-audit.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 | $0.00041 | $0.01528 |
| Opus 5 | $0.00020 | $0.00764 |
| Sonnet 5 | $0.00008 | $0.00306 |
| Haiku 4.5 | $0.00004 | $0.00153 |
Grade A, and why
terraform-audit 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 — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Terraform Audit Skill
Perform a comprehensive audit of a Terraform codebase across four dimensions: security & compliance, cost optimization, code quality, and architecture design. The audit produces a structured Markdown report with findings classified by severity. Rules are grounded in the CIS AWS Foundations Benchmark and the AWS Well-Architected Framework, with a primary focus on AWS resources.
Description
Perform a comprehensive audit of a Terraform codebase covering four dimensions: security and compliance, cost optimization, code quality, and architecture design. Based on the CIS AWS Foundations Benchmark and the AWS Well-Architected Framework, output a structured Markdown audit report for AWS resources classified by Critical / Important / Minor severity levels.
Execution Guidelines
- Scan the project structure before auditing; determine scale by the number of
.tffiles (Small / Medium / Large) and choose the corresponding strategy - Execute the four dimensions in order: Security -> Cost -> Quality -> Architecture, reading the corresponding sub-module checklist for each
- Every finding must include a severity level, affected file and line number, and specific remediation advice (including HCL code)
- Use the report-template.md template for the report, saved to the project root directory
- When cross-dimensional complementary rules exist (e.g., Multi-AZ in both architecture and cost), annotate the cross-reference context
Examples
Bad
# S3 bucket with no encryption, no versioning, public access
resource "aws_s3_bucket" "data" {
bucket = "my-data-bucket"
}
resource "aws_s3_bucket_public_access_block" "data" {
bucket = aws_s3_bucket.data.id
block_public_acls = false
block_public_policy = false
ignore_public_acls = false
restrict_public_buckets = false
}
✅ Good
resource "aws_s3_bucket" "data" {
bucket = "${var.project}-${var.environment}-data"
}
resource "aws_s3_bucket_versioning" "data" {
bucket = aws_s3_bucket.data.id
versioning_configuration { status = "Enabled" }
}
resource "aws_s3_bucket_server_side_encryption_configuration" "data" {
bucket = aws_s3_bucket.data.id
rule {
apply_server_side_encryption_by_default {
sse_algorithm = "aws:kms"
kms_master_key_id = aws_kms_key.main.arn
}
}
}
resource "aws_s3_bucket_public_access_block" "data" {
bucket = aws_s3_bucket.data.id
block_public_acls = true
block_public_policy = true
ignore_public_acls = true
restrict_public_buckets = true
}
What ships with it
5 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 · 178 lines · 41 tokens per session scan A 4394d4cade21
terraform-audit is a skill published in the GitHub repository addxai/enterprise-harness-engineering (44 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 41 tokens to every session and 1,528 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 2026-08-30.
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peon-ping-log
Log exercise reps for the Peon Trainer. Use when user says they did pushups, squats, or wants to log reps. Examples - "/peon-ping-log 25 pushups", "/peon-ping-log 30 squats", "log 50 pushups".