Problem-solving guide

How to connect local code to cloud AI tools safely

Keep control of repository scope, credentials, provider data transfer, approvals, and checks when a cloud model helps with local code.

Problem and general approach

Use explicit repository scope, provider credentials configured outside committed files, dry-run and approval boundaries, project checks, and a reviewable run record. Confirm the provider's data handling and cost terms before sending repository content.

Where ad-hoc workflows fall short

Pasting source into chat can bypass repository checks and expose secrets. A cloud model or remote integration may have broader permissions than the task requires. Remote MCP Stage 1's documented tools are read-only; it is not a repository write path.

When minitok fits

Use minitok when the workflow should remain local-first around a user-selected model provider, with explicit scopes, deterministic project checks, approval controls, and evidence. The provider remains separate and may receive repository content for a model request.

npm install -g @flotic/minitok
minitok doctor
cd your-repository
minitok migrate
minitok run --dry-run "Review the API change and preserve existing behavior"
minitok status

Do not place provider keys, JWTs, or private material in prompts, approval files, logs, or committed configuration.

Alternatives and limits

A fully local model, provider enterprise data controls, a sandbox, or a manually reviewed pull request may better fit sensitive repositories. minitok does not replace provider security terms, network policy, secret scanning, or human review, and it does not make a cloud provider local.