vastai-upgrade-migration
Guides the safe upgrade of the Vast.ai CLI and SDK while identifying breaking changes and ensuring API compatibility.
Install
mkdir -p .claude/skills/vastai-upgrade-migration && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/4218" && unzip -o skill.zip -d .claude/skills/vastai-upgrade-migration && rm skill.zipInstalls to .claude/skills/vastai-upgrade-migration
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Upgrade Vast.ai CLI, migrate API versions, and handle breaking changes.Key capabilities
- →Upgrade Vast.ai CLI and Python SDK
- →Detect breaking changes in CLI commands
- →Migrate between API endpoints
- →Update Docker images for CUDA compatibility
- →Verify authentication and instance status post-upgrade
How it works
The process involves upgrading the CLI via pip, comparing CLI help output to identify removed commands, and testing API connectivity against different base URLs.
Inputs & outputs
When to use vastai-upgrade-migration
- →Upgrade Vast.ai CLI version
- →Detect API breaking changes
- →Migrate to newer GPU configurations
- →Validate SDK auth settings
About this skill
Reversible Vast.ai Client and Template Upgrade
Overview
Treat the provider client and workload template as separate compatibility boundaries. Pin and test the managed CLI or PyPI package, preserve the vastai_sdk compatibility shim during migration, and canary template changes before a rolling update.
Prerequisites
- Current CLI/SDK version, install channel, import usage, commands, templates, and dependent automation
- Target version and release evidence plus contract tests for critical commands and response adapters
- Rollback client version, prior template hash, and an acceptance deadline
Instructions
Step 1: Freeze the current contract
Record versions, install location, key precedence, imports, command help, structured outputs, and immutable template/model identities.
Step 2: Upgrade in isolation
For the managed CLI, use its version-aware update mechanism and retain the prior version. For Python, pin the target vastai package in a disposable environment.
Step 3: Test client compatibility
Run auth, offer search, instance read, response normalization, and expected-denial tests without creating paid resources unless the plan requires a canary.
Step 4: Migrate SDK imports deliberately
Move from vastai_sdk to vastai while the documented compatibility shim remains; test high-level and sync/async client boundaries actually used.
Step 5: Canary workload changes
Create a separate endpoint or disposable instance from the target template, prove output and recovery, then trigger a controlled rolling update if Serverless.
Step 6: Accept or revert
Compare contract and SLO evidence. Restore the prior client pin or template reference on failure and verify the rollback path.
Authentication
Confirm the upgraded client still reads the intended XDG or environment credential and preserves least privilege. Never test upgrades with an unscoped production key by default.
Tool Discipline
Use Read and Grep to inspect manifests, configuration, provider output, and existing tests before proposing a mutation. Use Write or Edit only for the approved plan, implementation, test, or redacted receipt; do not create, update, destroy, or fund Vast.ai resources without explicit operator approval.
Output
- Before/after client and template contract inventory
- Compatibility, canary, SLO, and expected-denial results
- Acceptance or rollback receipt with retained prior versions
Return install channel, old/new versions, import boundary, old/new template hashes, tests, decision, and rollback verification.
Examples
A service moves from the vastai_sdk shim to vastai in staging, pins the package, validates its response adapter, canaries a new template, then performs a monitored Serverless rolling update with the prior hash retained.
Error Handling
| Failure | Response |
|---|---|
| Structured output changes | Fail at the adapter contract and keep the previous pin. |
| Credential source changes | Stop and restore the intended key precedence before any mutation. |
| Canary output or recovery regresses | Reject the template and retain production on the old hash. |
| Rollback artifact is unavailable | Issue NO-GO until the prior client and template are recoverable. |
Resources
When not to use it
- →When the current environment is stable and no updates are required
- →When working in a locked-version production environment
Prerequisites
Limitations
- →Requires manual verification of custom scripts
- →API endpoint migration depends on Vast.ai availability
How it compares
This workflow provides a systematic verification process to ensure script compatibility after an upgrade, rather than simply updating the package.
Compared to similar skills
vastai-upgrade-migration side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| vastai-upgrade-migration (this skill) | 1 | 2mo | Review | Intermediate |
| fastapi-templates | 520 | 4mo | No flags | Intermediate |
| llama-cpp | 21 | 10mo | Review | Intermediate |
| textual | 143 | 10mo | Review | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
More by jeremylongshore
View all by jeremylongshore →You might also like
fastapi-templates
wshobson
Create production-ready FastAPI projects with async patterns, dependency injection, and comprehensive error handling. Use when building new FastAPI applications or setting up backend API projects.
llama-cpp
zechenzhangAGI
Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
textual
KyleKing
Expert guidance for building TUI (Text User Interface) applications with the Textual framework. Invoke when user asks about Textual development, TUI apps, widgets, screens, CSS styling, reactive programming, or testing Textual applications.
mcp-builder
anthropics
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
streamlit
sverzijl
When working with Streamlit web apps, data dashboards, ML/AI app UIs, interactive Python visualizations, or building data science applications with Python
python-testing-patterns
wshobson
Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. Use when writing Python tests, setting up test suites, or implementing testing best practices.