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Hardening Cloud Posture

Community
trilwu
hardening-cloud-posture

Proactively harden a cloud account or organization before an incident — prioritizing IAM and identity risk over checkbox findings, closing the exposures that become attack paths (public storage, over-broad roles, missing audit logging, unencrypted data), reading CSPM output critically, and enforcing guardrails at the org level. Use when reviewing a cloud environment's security posture, triaging a Prowler/ScoutSuite/Security Hub report, deciding which misconfigurations actually matter, or setting preventive controls across AWS, Azure, or GCP.

Overview

Publishertrilwu
Repositorysecskills
Skill namehardening-cloud-posture
Stars
144
Forks
15
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by trilwu on GitHub. Read the source before you install it.

Installation

Install the Hardening Cloud Posture AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/trilwu/secskills.git /tmp/secskills
mkdir -p .claude/skills
cp -r /tmp/secskills/secskills-defense/skills/hardening-cloud-posture .claude/skills/hardening-cloud-posture
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hardening Cloud Posture in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Hardening Cloud Posture on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Hardening Cloud Posture is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Hardening Cloud Posture

A CSPM scan returns a thousand findings. Almost none of them are the way in. The job is not to close a thousand findings; it is to find the handful that form an attack path and close those, then set guardrails so they cannot recur. Posture work that treats every finding as equal drowns the real one.

The organizing principle: identity is the perimeter. In cloud, the attack path is almost always a chain of IAM permissions, not a network hop. Rank by "what does this let a principal reach?", not by a scanner's severity label.

When to Use

  • Reviewing an AWS/Azure/GCP account or organization's security posture
  • Triaging a Prowler, ScoutSuite, Security Hub, or Defender for Cloud report
  • Deciding which of many misconfigurations actually matter
  • Setting preventive guardrails (SCPs, Azure Policy, org policies)
  • Enabling the logging and controls an investigation will later depend on

When NOT to Use

  • Actively investigating a live incident — use investigating-aws-incidents, investigating-azure-incidents, or investigating-gcp-incidents
  • Attacking the environment to prove the path — use exploiting-cloud-platforms
  • Kubernetes cluster-plane hardening — use defending-kubernetes
  • Ranking CVEs in workloads rather than cloud config — use managing-vulnerabilities

Rank by Attack Path, Not Finding Count

Work findings in the order an attacker would exploit them, not the order the scanner lists them:

  1. Public exposure of data or compute. Public S3/GCS buckets and storage accounts, publicly reachable databases, management ports open to 0.0.0.0/0. These need no credential — they are pre-authentication. Close first.
  2. Identity that over-reaches. Wildcard Action/Resource policies, roles assumable by * or by external accounts without a condition, users with iam:PassRole to a privileged role, service principals with Owner. This is where a foothold becomes account-takeover.
  3. Missing or disabled audit logging. CloudTrail not multi-region, GCP Data Access logs off, Azure diagnostic settings absent. This does not create the breach but it blinds the investigation — enable it now, because you cannot retroactively log the incident you are about to have.
  4. Unencrypted data and absent key management. Real, but rarely the entry path. Fix after the above unless a compliance obligation reorders it.

A public bucket with customer data outranks a hundred "encryption not enabled" findings, even though the scanner may score them alike.

Read CSPM Output Critically

Prowler, ScoutSuite, Security Hub, and Defender for Cloud are the standard tools and they are useful — but their output is a starting point, not a verdict:

  • Severity is generic. The tool does not know your environment. A "medium" on a role assumable cross-account may be your worst finding; a "high" on an internal-only resource may be noise.
  • Findings lack blast radius. The tool reports that a policy has *; it does not tell you that role is attached to an internet-facing function. You supply the reachability.
  • Suppressions hide real risk. A finding suppressed months ago "because it was accepted" may no longer be acceptable. Review the suppression list as carefully as the open findings.
  • Green is not clean. The scanner checks what it checks. A passing scan with Data Access logging disabled has a large blind spot it will never report as a finding.

Map the top findings to CIS Benchmark controls where a compliance frame helps, but do not let the benchmark set your priority — the benchmark is comprehensive, your remediation budget is not.

Guardrails Beat Findings

A fixed misconfiguration recurs the next time someone provisions a resource. A guardrail prevents the whole class:

  • AWS: Service Control Policies to deny public S3 org-wide, deny disabling CloudTrail, restrict regions; s3:BlockPublicAccess at the account level.
  • Azure: Azure Policy with deny effects for public network access, required encryption, required diagnostic settings.
  • GCP: Organization Policy constraints (storage.publicAccessPrevention, iam.disableServiceAccountKeyCreation, compute.requireOsLogin).

Prefer the org-level deny over the per-resource fix. The point-fix closes one finding; the guardrail closes the class and every future instance of it.

Rationalizations to Reject

  • "We closed all the high-severity findings." Severity is the scanner's guess about a generic environment. The finding that matters is the one on the attack path, whatever it is labelled.
  • "The scan is green, so the account is secure." The scan covers its checks. Disabled logging, an over-broad role the tool rates low, and a suppressed finding are all invisible to a green result.
  • "Encryption-at-rest is our top gap." Rarely the entry path. A public bucket or a cross-account-assumable admin role outranks it unless compliance forces the order.
  • "We fixed the public bucket." One bucket. Without an org-level guardrail the next team makes the same bucket public next week. Fix the class.
  • "That finding was accepted as a risk." By whom, when, and is it still true? Stale acceptances are where real exposure hides.
  • "IAM is too complex to audit fully." Then audit the escalation primitives first — PassRole, wildcard trust policies, cross-account assume — not nothing. Identity is the perimeter; it is not optional.

Reading External Sources

Fetch public advisories, specifications, and vendor reports as Markdown:

bash
curl -sL "https://defuddle.md/<url>"      # scheme in the path is optional

This strips page boilerplate — roughly 78% fewer tokens on a prose page — and returns the full text rather than a summary, so you can grep it and trust a negative result.

Three things it is not for. Fetch JSON and API responses raw, because readability extraction mangles structured data. Fetch authenticated or JavaScript-rendered pages directly, because it retrieves them anonymously. And never route adversary infrastructure (phishing links, C2, malware hosting), client-owned hosts, or engagement URLs through it — the request leaves your machine to a third party, and for live adversary infrastructure it also tips off the operator.

Some sites block the extractor and return an error blob rather than the page — {"error":"Failed to fetch: 418 I'm a teapot"} from freedesktop.org, for instance. That is the fetch being refused, not the source saying the thing does not exist. Re-fetch the URL directly before drawing any conclusion from it.

References

  • exploiting-cloud-platforms — the attack paths this posture work closes
  • investigating-aws-incidents / investigating-azure-incidents / investigating-gcp-incidents — what the audit logging you enable here feeds
  • defending-kubernetes — the cluster plane, when the account runs managed k8s
  • managing-vulnerabilities — the workload-CVE counterpart to config posture

Frequently asked questions

What does the Hardening Cloud Posture AI skill do?

Proactively harden a cloud account or organization before an incident — prioritizing IAM and identity risk over checkbox findings, closing the exposures that become attack paths (public storage, over-broad roles, missing audit logging, unencrypted data), reading CSPM output critically, and enforcing guardrails at the org level. Use when reviewing a cloud environment's security posture, triaging a Prowler/ScoutSuite/Security Hub report, deciding which misconfigurations actually matter, or setting preventive controls across AWS, Azure, or GCP.

Why use Hardening Cloud Posture on TypingMind?

Because you install it once and use it with any model. Hardening Cloud Posture is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Hardening Cloud Posture in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/trilwu/secskills/tree/main/secskills-defense/skills/hardening-cloud-posture. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Hardening Cloud Posture?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Hardening Cloud Posture?

As many as you like. As long as a model supports skills, you can use Hardening Cloud Posture with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Hardening Cloud Posture AI skill free?

Yes. It is published on GitHub by trilwu under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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