aegisops-ai

Name: aegisops-ai
Author: sickn33

44kstars6.5kforks

How to install aegisops-ai?

sickn33/agentic-awesome-skills/aegisops-ai

$npx -y skills add sickn33/agentic-awesome-skills --skill aegisops-ai

Installs into the current project.

Summary

Autonomous DevSecOps & FinOps Guardrails. Orchestrates Gemini 3 Flash to audit Linux Kernel patches, Terraform cost drifts, and K8s compliance.

Files · 1

View on GitHub

SKILL.md

1	# /aegisops-ai — Autonomous Governance Orchestrator
2
3	AegisOps-AI is a professional-grade "Living Pipeline"
4	that integrates advanced AI reasoning directly into
5	the SDLC. It acts as an intelligent gatekeeper for
6	systems-level security, cloud infrastructure costs,
7	and Kubernetes compliance.
8
9	## Goal
10
11	To automate high-stakes security and financial audits by:
12	1. Identifying logic-based vulnerabilities (UAF, Stale
13	State) in Linux Kernel patches.
14	2. Detecting massive "Silent Disaster" cost drifts in
15	Terraform plans.
16	3. Translating natural language security intent into
17	hardened K8s manifests.
18
19	## When to Use
20	- Kernel Patch Review: Auditing raw C-based Git diffs for memory safety.
21	- Pre-Apply IaC Audit: Analyzing `terraform plan` outputs to prevent bill spikes.
22	- Cluster Hardening: Generating "Least Privilege" securityContexts for deployments.
23	- CI/CD Quality Gating: Blocking non-compliant merges via GitHub Actions.
24
25	## When Not to Use
26
27	- Web App Logic: Do not use for standard web vulnerabilities (XSS, SQLi); use dedicated SAST scanners.
28	- Non-C Memory Analysis: The patch analyzer is optimized for C-logic; avoid using it for high-level languages like Python or JS.
29	- Direct Resource Mutation: This is an auditor, not a deployment tool. It does not execute `terraform apply` or `kubectl apply`.
30	- Post-Mortem Analysis: For analyzing why a previous AI session failed, use `/analyze-project` instead.
31
32	---
33	## 🤖 Generative AI Integration
34
35	AegisOps-AI leverages the Google GenAI SDK to implement a "Reasoning Path" for autonomous security and financial audits:
36
37	* Neural Patch Analysis: Performs semantic code reviews of Linux Kernel patches, moving beyond simple pattern matching to understand complex memory state logic.
38	* Intelligent Cost Synthesis: Processes raw Terraform plan diffs through a financial reasoning model to detect high-risk resource escalations and "silent" fiscal drifts.
39	* Natural Language Policy Mapping: Translates human security intent into syntactically correct, hardened Kubernetes `securityContext` configurations.
40
41	## 🧭 Core Modules
42
43	### 1. 🐧 Kernel Patch Reviewer (`patch_analyzer.py`)
44
45	* Problem: Manual review of Linux Kernel memory safety is time-consuming and prone to human error.
46	* Solution: Gemini 3 performs a "Deep Reasoning" audit on raw Git diffs to detect critical memory corruption vulnerabilities (UAF, Stale State) in seconds.
47	* Key Output: `analysis_results.json`
48
49	### 2. 💰 FinOps & Cloud Auditor (`cost_auditor.py`)
50
51	* Problem: Infrastructure-as-Code (IaC) changes can lead to accidental "Silent Disasters" and massive cloud bill spikes.
52	* Solution: Analyzes `terraform plan` output to identify cost anomalies—such as accidental upgrades from `t3.micro` to high-performance GPU instances.
53	* Key Output: `infrastructure_audit_report.json`
54
55	### 3. ☸️ K8s Policy Hardener (`k8s_policy_generator.py`)
56
57	* Problem: Implementing "Least Privilege" security contexts in Kubernetes is complex and often neglected.
58	* Solution: Translates natural language security requirements into production-ready, hardened YAML manifests (Read-only root FS, Non-root enforcement, etc.).
59	* Key Output: `hardened_deployment.yaml`
60
61	## 🛠️ Setup & Environment
62
63	### 1. Clone the Repository
64
65	```bash
66	git clone https://github.com/Champbreed/AegisOps-AI.git
67	cd AegisOps-AI
68	```
69	## 2. Setup
70
71	```bash
72	python3 -m venv venv
73	source venv/bin/activate
74	pip install google-genai python-dotenv
75	```
76	### 3. API Configuration
77
78	Create a `.env` file in the root directory to securely
79	store your credentials:
80
81	```bash
82	printf 'GEMINI_API_KEY=%s\n' "$GEMINI_API_KEY" > .env
83	```
84	## 🏁 Operational Dashboard
85
86	To execute the full suite of agents in sequence and generate all security reports:
87
88	```bash
89	python3 main.py
90	```
91	### Pattern: Over-Privileged Container
92
93	* Indicators: `allowPrivilegeEscalation: true` or root user execution.
94	* Investigation: Pass security intent (e.g., "non-root only") to the K8s Hardener module.
95
96	---
97
98	## 💡 Best Practices
99
100	* Context is King: Provide at least 5 lines of context around Git diffs for more accurate neural reasoning.
101	* Continuous Gating: Run the FinOps auditor before every infrastructure change, not after.
102	* Manual Sign-off: Use AI findings as a high-fidelity signal, but maintain human-in-the-loop for kernel-level merges.
103
104	---
105
106	## 🔒 Security & Safety Notes
107
108	* Key Management: Use CI/CD secrets for `GEMINI_API_KEY` in production.
109	* Least Privilege: Test "Hardened" manifests in staging first to ensure no functional regressions.
110
111	## Links
112
113	+ - Repository: https://github.com/Champbreed/AegisOps-AI
114	+ - Documentation: https://github.com/Champbreed/AegisOps-AI#rea

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aegisops-ai

44kstars6.5kforks

View on GitHub

How to install aegisops-ai?

sickn33/agentic-awesome-skills/aegisops-ai

$npx -y skills add sickn33/agentic-awesome-skills --skill aegisops-ai

Installs into the current project.

Summary

Autonomous DevSecOps & FinOps Guardrails. Orchestrates Gemini 3 Flash to audit Linux Kernel patches, Terraform cost drifts, and K8s compliance.

Files · 1

View on GitHub

SKILL.md

1	# /aegisops-ai — Autonomous Governance Orchestrator
2
3	AegisOps-AI is a professional-grade "Living Pipeline"
4	that integrates advanced AI reasoning directly into
5	the SDLC. It acts as an intelligent gatekeeper for
6	systems-level security, cloud infrastructure costs,
7	and Kubernetes compliance.
8
9	## Goal
10
11	To automate high-stakes security and financial audits by:
12	1. Identifying logic-based vulnerabilities (UAF, Stale
13	State) in Linux Kernel patches.
14	2. Detecting massive "Silent Disaster" cost drifts in
15	Terraform plans.
16	3. Translating natural language security intent into
17	hardened K8s manifests.
18
19	## When to Use
20	- Kernel Patch Review: Auditing raw C-based Git diffs for memory safety.
21	- Pre-Apply IaC Audit: Analyzing `terraform plan` outputs to prevent bill spikes.
22	- Cluster Hardening: Generating "Least Privilege" securityContexts for deployments.
23	- CI/CD Quality Gating: Blocking non-compliant merges via GitHub Actions.
24
25	## When Not to Use
26
27	- Web App Logic: Do not use for standard web vulnerabilities (XSS, SQLi); use dedicated SAST scanners.
28	- Non-C Memory Analysis: The patch analyzer is optimized for C-logic; avoid using it for high-level languages like Python or JS.
29	- Direct Resource Mutation: This is an auditor, not a deployment tool. It does not execute `terraform apply` or `kubectl apply`.
30	- Post-Mortem Analysis: For analyzing why a previous AI session failed, use `/analyze-project` instead.
31
32	---
33	## 🤖 Generative AI Integration
34
35	AegisOps-AI leverages the Google GenAI SDK to implement a "Reasoning Path" for autonomous security and financial audits:
36
37	* Neural Patch Analysis: Performs semantic code reviews of Linux Kernel patches, moving beyond simple pattern matching to understand complex memory state logic.
38	* Intelligent Cost Synthesis: Processes raw Terraform plan diffs through a financial reasoning model to detect high-risk resource escalations and "silent" fiscal drifts.
39	* Natural Language Policy Mapping: Translates human security intent into syntactically correct, hardened Kubernetes `securityContext` configurations.
40
41	## 🧭 Core Modules
42
43	### 1. 🐧 Kernel Patch Reviewer (`patch_analyzer.py`)
44
45	* Problem: Manual review of Linux Kernel memory safety is time-consuming and prone to human error.
46	* Solution: Gemini 3 performs a "Deep Reasoning" audit on raw Git diffs to detect critical memory corruption vulnerabilities (UAF, Stale State) in seconds.
47	* Key Output: `analysis_results.json`
48
49	### 2. 💰 FinOps & Cloud Auditor (`cost_auditor.py`)
50
51	* Problem: Infrastructure-as-Code (IaC) changes can lead to accidental "Silent Disasters" and massive cloud bill spikes.
52	* Solution: Analyzes `terraform plan` output to identify cost anomalies—such as accidental upgrades from `t3.micro` to high-performance GPU instances.
53	* Key Output: `infrastructure_audit_report.json`
54
55	### 3. ☸️ K8s Policy Hardener (`k8s_policy_generator.py`)
56
57	* Problem: Implementing "Least Privilege" security contexts in Kubernetes is complex and often neglected.
58	* Solution: Translates natural language security requirements into production-ready, hardened YAML manifests (Read-only root FS, Non-root enforcement, etc.).
59	* Key Output: `hardened_deployment.yaml`
60
61	## 🛠️ Setup & Environment
62
63	### 1. Clone the Repository
64
65	```bash
66	git clone https://github.com/Champbreed/AegisOps-AI.git
67	cd AegisOps-AI
68	```
69	## 2. Setup
70
71	```bash
72	python3 -m venv venv
73	source venv/bin/activate
74	pip install google-genai python-dotenv
75	```
76	### 3. API Configuration
77
78	Create a `.env` file in the root directory to securely
79	store your credentials:
80
81	```bash
82	printf 'GEMINI_API_KEY=%s\n' "$GEMINI_API_KEY" > .env
83	```
84	## 🏁 Operational Dashboard
85
86	To execute the full suite of agents in sequence and generate all security reports:
87
88	```bash
89	python3 main.py
90	```
91	### Pattern: Over-Privileged Container
92
93	* Indicators: `allowPrivilegeEscalation: true` or root user execution.
94	* Investigation: Pass security intent (e.g., "non-root only") to the K8s Hardener module.
95
96	---
97
98	## 💡 Best Practices
99
100	* Context is King: Provide at least 5 lines of context around Git diffs for more accurate neural reasoning.
101	* Continuous Gating: Run the FinOps auditor before every infrastructure change, not after.
102	* Manual Sign-off: Use AI findings as a high-fidelity signal, but maintain human-in-the-loop for kernel-level merges.
103
104	---
105
106	## 🔒 Security & Safety Notes
107
108	* Key Management: Use CI/CD secrets for `GEMINI_API_KEY` in production.
109	* Least Privilege: Test "Hardened" manifests in staging first to ensure no functional regressions.
110
111	## Links
112
113	+ - Repository: https://github.com/Champbreed/AegisOps-AI
114	+ - Documentation: https://github.com/Champbreed/AegisOps-AI#rea

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6 picks

Type