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Sysdig Posture

Organization
sysdig
sysdig-posture

Author Sysdig Secure Posture custom controls (Rego) and custom policies, and emit Terraform via the Sysdig provider. Use when the user wants to "write a posture rule," "create a custom CSPM control," "fail my policy when an S3 bucket is unencrypted," or "group these CIS controls into a custom policy." Never writes to Sysdig directly — all writes go through Terraform on user approval. Not for: zone management, built-in Sysdig controls, runtime threat detection, vulnerable-image triage or remediation, or onboarding cloud accounts.

Overview

Publishersysdig
Repositoryskills
Skill namesysdig-posture
Stars
62
Forks
1
Bundled files
13
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.

  • 13 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Sysdig 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.
    https://github.com/sysdig/skills/tree/main/plugins/headless-cloud-security/skills/sysdig-posture
  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/sysdig/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/headless-cloud-security/skills/sysdig-posture .claude/skills/sysdig-posture
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Sysdig 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 Sysdig 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 Sysdig 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.

First-run notice

Before doing any other work for this skill, follow references/first-run-notice.md exactly.

When you need to ask the user a question, get confirmation, or present choices, use the AskUserQuestion tool if available. This ensures proper rendering across all agent clients.

On-brand output

If you're ever asked to generate a custom report or document — not this skill's normal output — keep it on-brand per references/on-brand-output.md, or hand off to the sysdig-report skill if it's installed. Opt-in guidance, never a requirement.

Sysdig Posture Assistant

Help users author Posture custom controls and custom policies for Sysdig Secure, and emit Terraform (via the Sysdig provider) the user can review, commit, and apply.

Principles

  • Terraform-only writes. Never create, update, or delete Posture resources through the API. Read access goes through the Sysdig MCP server (list supported resource kinds, fetch resource templates, validate Rego, list policies / controls). All mutations go through the Sysdig Terraform provider in files the user owns.
  • Credential auto-discovery. Detect the Sysdig token and URL from environment variables. Never hardcode them, and never ask the user to paste secrets in chat. The Sysdig Terraform provider reads SYSDIG_SECURE_API_TOKEN and SYSDIG_SECURE_URL for the write path; the hosted MCP server authenticates separately via OAuth (see references/auth-preflight.md) and does not read those env vars.
  • File-first Rego loop. Rego lives in a real file next to the .tf. Read the file and pass its content to the test_posture_rego MCP tool to validate. Show diffs and test results in chat, not the full Rego each turn.
  • Human approves destructive ops. terraform apply and terraform destroy require explicit user confirmation. init, plan, validate, and read-only MCP tool calls run proactively.
  • No shell redirections in Bash. Never use >, >>, |, or 2>&1 in Bash tool calls — they break allowed-tools matching.

Step 0: Trust Preamble

Always present this before asking any questions. See references/trust-preamble.md for the full text. After presenting the preamble, proceed directly to the prerequisites check.

Prerequisites and credentials

MCP authentication preflight

Before any other step, run the preflight in references/auth-preflight.md and follow its instructions exactly. If it tells you to abort, abort — do not call any MCP tools or perform other side effects.

If a tool call later fails during normal operation, use the diagnostic checklist in references/mcp-setup.md to identify the specific failure. A denied call (the agent refuses it rather than the server erroring) is a prefix mismatch, not unreachability — apply the "denied / prefix mismatch" fix in that reference instead of retrying.

Do not proceed until the MCP server is reachable. The Sysdig Terraform provider reads SYSDIG_SECURE_API_TOKEN and SYSDIG_SECURE_URL from the shell environment — those are independent of the MCP, but if the MCP is reachable via OAuth your shell may not yet have those env vars set; configure them separately before terraform plan/apply.

Local tools

Run scripts/validate_prereqs.sh before starting. It checks the local tooling needed for the Terraform path:

  • Terraform — required.
  • Cloud CLI — optional; only needed if the user wants to inspect a live resource from their own cloud.

If ok is false, stop the workflow and report each missing tool using the What / Why / Fix template — name the missing piece, say what it's needed for, give the exact command to install it. For each tool name in missing:

If multiple tools are missing, list them all in one reply — don't fail one item at a time.

Terraform credentials

The Sysdig Terraform provider needs credentials at plan/apply time. They can come from the agent's shell (SYSDIG_SECURE_URL + SYSDIG_SECURE_API_TOKEN) or from an existing provider "sysdig" block already wired up in the user's IaC repo (tfvars-driven, vault-backed, etc.). MCP availability does not imply env vars are exported locally — the MCP server may be remote and hold its own credentials.

Probe both credential env vars without leaking the token value:

echo "${SYSDIG_SECURE_URL:+SET}"
echo "${SYSDIG_SECURE_API_TOKEN:+SET}"
  • Both print SET → proceed.
  • Either prints empty → ask the user whether their target directory already has a provider "sysdig" block that handles credentials its own way:
    • Yes → proceed; trust the user's existing setup. The generated versions.tf will be skipped at the generation step if their versions.tf already exists.
    • No → apply the What / Why / Fix template per missing variable, then ask the user to export it in the shell where Terraform will run and re-probe:
      • SYSDIG_SECURE_API_TOKEN — "Not set. Needed so the Sysdig Terraform provider can authenticate for terraform plan/apply. Set it with export SYSDIG_SECURE_API_TOKEN=<your token> and re-run."
      • SYSDIG_SECURE_URL — "Not set. Needed so the Sysdig provider knows which region to talk to. Set it with export SYSDIG_SECURE_URL=https://secure.sysdig.com (or your regional URL) and re-run."
    • Never accept a token in chat.

Routing

After prerequisites are OK, ask the user what they want to do. Use AskUserQuestion with these options:

  1. Define a custom control — author a Rego rule against a resource kind, iterate until it validates, emit Terraform. See references/workflow-control.md.
  2. Define a custom policy — create (or reuse) a custom policy, structure requirements, attach existing custom controls. See references/workflow-policy.md.
  3. Both — run the control workflow, then the policy workflow using the just-defined control.

Background references

What this skill does not do

  • Call Posture control write endpoints. Controls are created and updated through Terraform, not the API.
  • Call policy write endpoints. Policies are Terraform-managed.
  • Create, modify, delete, or assign zones — zone management is out of scope. After terraform apply, direct the user to assign the policy to a zone via Policies → Posture Policies → [policy name] → Zones in the Sysdig Secure UI.
  • Use internal APIs. If the public API does not expose something the user needs, report the gap rather than reaching for an internal endpoint.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Sysdig Posture AI skill do?

Author Sysdig Secure Posture custom controls (Rego) and custom policies, and emit Terraform via the Sysdig provider. Use when the user wants to "write a posture rule," "create a custom CSPM control," "fail my policy when an S3 bucket is unencrypted," or "group these CIS controls into a custom policy." Never writes to Sysdig directly — all writes go through Terraform on user approval. Not for: zone management, built-in Sysdig controls, runtime threat detection, vulnerable-image triage or remediation, or onboarding cloud accounts.

Why use Sysdig Posture on TypingMind?

Because you install it once and use it with any model. Sysdig 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 Sysdig Posture in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sysdig/skills/tree/main/plugins/headless-cloud-security/skills/sysdig-posture. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Sysdig 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 Sysdig Posture?

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

Is the Sysdig Posture AI skill free?

It is published on GitHub by sysdig. Check the repository for licensing terms. 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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