Syncs Claude config between prime repo and target projects. Trigger on 'sync', 'prime-sync', 'push config', 'pull config', 'update prime', 'sync claude config'. Push mode deploys to targets; pull mode imports changes back.
npx skills add https://github.com/avibebuilder/claude-prime --skill prime-sync
Ultrathink.
Detect operating mode before anything else:
VERSION file exists in CWD → push mode (CWD is the prime repo).claude/.prime-version exists in CWD → pull mode (CWD is a target project)Source = CWD (prime repo). Target = resolved project path.
State file: .claude/prime-projects.json
Tracks known target paths, last sync time, and version. Shape:
{ "projects": [{ "path": "/absolute/path", "lastSynced": "2025-12-30T10:30:00Z", "version": "1.4.2" }] }
Create as { "projects": [] } if missing.
Resolution flow:
State file management:
.claude/ only if you need to persist the state fileSource = cloned prime repo. Target = CWD.
Flow:
.claude/.prime-version in CWDdate +%Y%m%d%H%M%Sgit clone https://github.com/avibebuilder/claude-prime.git /tmp/claude-prime-sync-<timestamp>//tmp/claude-prime-sync-<timestamp>/, target = CWD/tmp/claude-prime-sync-<timestamp>/ after sync completes (success or failure)No state file in pull mode — the target project is self-contained.
.claude/ is appropriate.claude/.prime-version) and prime (VERSION)If the path fails those checks, abort and explain why.
Change Detection:
.claude/ based on the target's recorded version stateHEAD and include uncommitted changes; warn if the tag is missingStack Detection (only if skills or starter-skills changed): check target for stack indicators relevant to each changed skill/starter.
File Comparison: compare each changed file with target. Detect: NEW, UPDATE, IDENTICAL, CONFLICT.
Aggregate results and present:
CHANGED (will sync): ...
IDENTICAL (skip): ...
CONFLICTS (will ask): ...
IRRELEVANT (skip, wrong stack): ...
GATE: User approves sync plan.
For each conflict, show the relevant diff and ask the user how to proceed:
.claude/ in target if needed.claude/starter-skills/ in target.prime-version with prime's VERSIONSync complete! (prime vX.X.X)
Updated: ...
Skipped: ...
Version: X.X.X → written to .prime-version
Push mode only: update state file (lastSynced timestamp + version).
Pull mode only: clean up /tmp/claude-prime-sync-* clone directory.
prime-sync stays in prime — do not copy this skill into target projects.<target-path>$ARGUMENTS</target-path>
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 avibebuilder/prime-sync 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.