Operate Ai Stack
Develop, test, and troubleshoot this generated AI or ML workload across its framework, model and embedding providers, retrieval stores, interfaces, training, serving, and observability layers. Use when changing prompts, tools, agents, RAG, MCP, inference, fine-tuning, evaluations, or provider integration.
Install
$ agentstack add skill-leynier-python-template-if-ai-capabilities-none-operate-ai-stack-endif ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ● Environment & secrets Used
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-leynier-python-template-if-ai-capabilities-none-operate-ai-stack-endif) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Operate AI Stack
Treat .copier-answers.yml as the layer map and inspect the generated modules before assuming a framework or provider API.
Workflow
- Identify the workload, framework, model provider, embedding provider, data roles, interfaces, training extensions, serving engine, and quality tools that are actually enabled.
- Preserve the boundary between model and embedding providers. Keep provider-specific construction in the generated provider module and inject it into framework code.
- Use deterministic fakes for unit tests. Put real-provider checks behind explicit environment variables and never make the normal test suite spend tokens or require cloud credentials.
- For agents and MCP, test tool schemas and error paths. For RAG, test ingestion, retrieval, empty results, and citation metadata. For training, test a tiny local batch and artifact creation. For inference, test health plus one prediction.
- Record required secrets in
.env.example, use the settings layer, and redact prompt, credential, and personal data from logs. - Run the project quality gates from
project-workflow, followed by the smallest representative end-to-end check for the enabled stack.
Operational Checks
- Pin or bound model and API dependencies; review upstream breaking changes before updating.
- Track latency, token or compute usage, provider errors, and evaluation quality separately.
- Make external calls timeout and fail clearly; do not silently switch providers or models.
- Require an explicit review before changing production prompts, tools with side effects, or model artifacts.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: leynier
- Source: leynier/python-template
- License: MIT
- Homepage: https://python-template.leynier.dev
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.