Expert guidance for FedRAMP certification and compliance under CR26 (FedRAMP Consolidated Rules for 2026). Use this skill whenever a user asks about FedRAMP authorization, ATO (Authority to Operate), cloud security for federal government, NIST SP 800-53 controls, CSP compliance, or any of the core FedRAMP document types: SSP, SAP, SAR, POA&M, CIS/CRM workbooks. Also trigger for questions about FedRAMP Certification Classes (A, B, C, D — new baseline labels: A = pilot/transitional, B = LI-SaaS/Low, C = Moderate, D = High, per NTC-0004), FedRAMP 20x (now the primary authorization pathway), OSCAL mandate (September 2026), 3PAO assessments, continuous monitoring (ConMon), gap assessments, system boundary definition, or architecture reviews for federal cloud. FedRAMP Ready retires July 28, 2026. When in doubt, use this skill — it covers the full FedRAMP lifecycle from readiness through continuous monitoring.
npx skills add https://github.com/lawve-ai/awesome-legal-skills --skill fedramp
> Last verified: 2026-07-03
A comprehensive guide for helping users navigate FedRAMP authorization — from initial
readiness through ATO and ongoing continuous monitoring.
Identify the user's goal and jump to the appropriate section:
| User Goal | Go To |
|---|---|
| "Are we ready for FedRAMP?" / gap assessment | → Readiness & Gap Assessment |
| Writing SSP, POA&M, SAR, SAP, or other docs | → ATO Documentation |
| "Which controls apply to us?" / control mapping | → NIST 800-53 Control Mapping |
| Cloud architecture / AWS/Azure/GCP config | → Architecture Guidance |
| Already authorized, ongoing compliance | → Continuous Monitoring |
> ⚠️ CR26 (FedRAMP Consolidated Rules for 2026): FedRAMP has restructured its authorization framework. FIPS 199-based baseline labels (Low/Moderate/High/LI-SaaS) are replaced with Certification Classes A–D (per notice NTC-0004; CR26 rules valid through December 31, 2028). Class labels change the *names* of the baselines, not their requirements. CSPs already authorized under the old labels retain their authorization through a transition period in which old and new labels are linked.
references/readiness-checklist.md> FedRAMP Ready is retiring July 28, 2026. If a CSP is currently pursuing FedRAMP Ready, advise them to pivot immediately to FedRAMP 20x or begin a full authorization package.
The core FedRAMP authorization package consists of:
Authorization Package
├── System Security Plan (SSP) + Appendices A–Q
├── Security Assessment Plan (SAP) + Appendices A–D [3PAO-prepared]
├── Security Assessment Report (SAR) + Appendices A–F [3PAO-prepared]
└── Plan of Action & Milestones (POA&M) [SSP Appendix O]
> Important: CSPs must use official FedRAMP PMO templates. OSCAL-format submissions are mandatory by September 30, 2026.
> Templates: https://www.fedramp.gov/documents-templates/
For detailed guidance on each document type, read the appropriate reference file:
references/ssp-guide.mdreferences/poam-guide.mdreferences/sap-sar-guide.mdreferences/appendices-guide.md| ID | Family | Notes |
|---|---|---|
| AC | Access Control | IAM, RBAC, least privilege, remote access |
| AT | Awareness & Training | Security + privacy training (new in Rev 5) |
| AU | Audit & Accountability | Log retention, SIEM, audit review |
| CA | Assessment, Authorization & Monitoring | ConMon, 3PAO, ATO |
| CM | Configuration Management | Baselines, change control, CMDB |
| CP | Contingency Planning | BCP/DR, tested annually |
| IA | Identification & Authentication | MFA, PIV, FIPS 140-2/3 crypto |
| IR | Incident Response | IRP, tested annually, reporting SLAs |
| MA | Maintenance | Remote maintenance controls |
| MP | Media Protection | Data at rest, media sanitization |
| PE | Physical & Environmental | Datacenters; often inherited from IaaS |
| PL | Planning | SSP, rules of behavior |
| PM | Program Management | Enterprise-level security program |
| PS | Personnel Security | Screening, termination procedures |
| PT | PII Processing & Transparency | New family in Rev 5 — privacy controls |
| RA | Risk Assessment | Vulnerability scanning, MITRE ATT&CK scoring |
| SA | System & Services Acquisition | SDLC, supply chain |
| SC | System & Communications Protection | Encryption in transit, network segmentation |
| SI | System & Information Integrity | Patching, malware, integrity monitoring |
| SR | Supply Chain Risk Management | New family in Rev 5 — SCRM |
Under CR26, the FedRAMP PMO is aligning control baselines to Certification Classes. When users describe their system, map to a class:
> Legacy references: Many existing FedRAMP documents still reference Low/Moderate/High/LI-SaaS. These map to LI-SaaS/Low → Class B, Moderate → Class C, High → Class D (Class A is new — it has no legacy equivalent). During the CR26 transition, old and new labels are linked. Advise CSPs to check fedramp.gov for the latest.
The boundary defines what is IN scope for FedRAMP. This is one of the most common sources of findings and delays.
Key principles:
AWS GovCloud (US)
Azure Government
Google Cloud (FedRAMP-authorized regions)
Once authorized, CSPs must maintain compliance through ConMon activities:
Match output format to request type:
| Request Type | Preferred Format |
|---|---|
| Gap assessment | Table + prose summary |
| SSP control narrative | Prose paragraphs (one per control/enhancement) |
| POA&M entry | Structured table row with all required fields |
| Architecture review | Bullet findings + recommended remediations |
| Control mapping question | Table: Control ID \| Requirement \| How to Implement |
| Readiness overview | Executive summary prose + priority action list |
When generating document content, always note: *"Use official FedRAMP templates from fedramp.gov — this content should be inserted into the appropriate template section."*
Load these when more depth is needed:
references/readiness-checklist.md — Full readiness checklist (75+ items)references/ssp-guide.md — SSP section-by-section writing guidereferences/poam-guide.md — POA&M structure, field definitions, SLA tablereferences/sap-sar-guide.md — SAP/SAR overview and review tips for CSPsreferences/appendices-guide.md — Guide to all SSP appendices (A–Q)references/control-families.md — Deep-dive on each of the 20 control families> *This skill provides general compliance information, not legal advice. Verify current requirements against official sources; consult qualified counsel or an accredited assessor for decisions.*
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take lawve-ai/fedramp from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.