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 nitinjain999/platform-skills --skill opagit clone --depth 1 https://github.com/nitinjain999/platform-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/nitinjain999/platform-skills/opa)<a href="https://agentmods.dev/skills/nitinjain999/platform-skills/opa"><img src="https://agentmods.dev/badge/skills/nitinjain999/platform-skills/opa/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/nitinjain999/platform-skills/opa"><img src="https://agentmods.dev/badge/skills/nitinjain999/platform-skills/opa.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 | $0.00098 | $0.01969 |
| Opus 5.5 | $0.00039 | $0.00788 |
| Sonnet 5.5 | $0.00020 | $0.00394 |
| Haiku 4.5 | $0.00010 | $0.00197 |
Grade A, and why
opa 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 5d 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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Write, test, validate, explain, and debug OPA Rego policies with Conftest.
AI-agent behavior, not infra policy: to gate what an AI coding agent (Copilot, Claude Code) can edit or run in a repo, use
/platform-skills:ai-governance— a different policy engine for a different input shape (tool calls and diffs, not Terraform/Kubernetes manifests).
Interactive Wizard (fires when no arguments are provided)
When invoked with no arguments, ask before proceeding:
Q1 — Mode?
What do you need?
1. generate — write a new production-ready Rego policy
2. test — write _test.rego unit tests for an existing policy
3. validate — run the full pipeline (fmt, lint, tests) against a directory
4. explain — translate an existing Rego policy into plain English
5. debug — diagnose why a policy is not firing as expected
Enter 1–5 or mode name:
Q2 — Context (after mode selected, one at a time):
- generate:
Describe the policy — target resource type (Terraform/Kubernetes/GHA/Dockerfile) and what to deny or warn on: - test:
Paste the Rego policy to write tests for: - validate:
Provide the directory path containing your .rego files: - explain:
Paste the Rego policy to explain: - debug:
Describe the symptom — is the policy not firing, producing no output, or throwing an error? Paste the conftest output:
Then proceed into the relevant mode below.
Mode: generate
Write a production-ready Rego policy from a description.
Steps:
- Ask for: target resource type (Terraform HCL, Kubernetes manifest, GitHub Actions workflow, Dockerfile, JSON/YAML), the rule logic (what to deny or warn on), and whether a named namespace is needed
- Parse what Conftest sees — use
conftest parse <file>to show the input shape if the user has a file, then write rules that match that shape exactly - Generate the policy file:
- Start with a
# METADATAblock:title,description,authors,entrypoint: true - Declare
package <namespace>— usemainonly when a single policy set applies; use a named package (package terraform.iam,package k8s.pods) for multi-domain repos - Add
import rego.v1— required for modern Rego syntax - Use
denyfor hard failures,warnfor advisory violations,violationfor OPA framework integrations (Gatekeeper, Conftest policy sets) - Generate a descriptive
msgthat includes the offending resource name/value and a remediation hint - Use
somefor iteration,infor membership,startswith/containsfor string matching
- Start with a
- Show the Conftest command to test the policy against sample input
- State CI placement: conftest runs after
terraform validateand beforeterraform planas a blocking gate — failing conftest must prevent plan from running
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.
- 5d ago First seen · 142 lines · 98 tokens per session scan A 9bb1413548f3
opa is a skill published in the GitHub repository nitinjain999/platform-skills (70 stars, last pushed 3d ago), licensed Apache-2.0. It adds 98 tokens to every session and 1,969 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-10-12.
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