sinc-LLMCapabilities
- Explicit multi-shot decision and sample count
- Refusal paths for unsuitable regimes
- A CLI and Python policy interface
Public project · Python · agent CLI
Multi-Shot Intelligence
Decide whether another model sample is worth taking.
A sampling policy that can select an approach, request more diversity or refuse an unsuitable sampling regime.
MIT
Software use follows the repository’s terms. Installation, integration and model usage are separate considerations.
- Task estimates
- Sampling policy
- Decision + reason
A guide to how the project is used. Read its requirements and limits before installation.
Your starting point
A pipeline that samples a model repeatedly to choose an answer.
More samples can repeat correlated errors or reinforce a weak baseline.
The capability to add
Make the sample-count decision explicit and inspectable.
A policy evaluates supplied accuracy, bias, correlation and selection assumptions and returns a decision with its rationale.
What’s in the project
A tool you can inspect.
Requirements
- Python and the project’s declared dependencies
- Task-specific parameter estimates; supported model access for executor mode
Important limits
- Policy decisions depend on the supplied estimates and model assumptions.
- Mathematical checks are not a guarantee of factual answers on an arbitrary LLM task.
A useful fit
Consider this when…
Engineers evaluating repeated sampling with enough task evidence to estimate policy inputs.
A different starting point
Choose another approach when…
Tasks without useful estimates, or buyers expecting guaranteed accuracy from more samples.
Inspect the implementation
Public source, with a specific scope.
The repository and the files below show the implementation available to inspect. They do not establish results for your workflow.
Reviewed source snapshot: ca8b5c91f465.
Compatibility and behavior must be checked in your environment. No customer-result or quality-improvement guarantee is implied.
Code and implementation help
Choose the help you need.
Software. MIT. Read the repository’s terms and installation guide.
Setup and integration. Bring your current tools, a workflow and your desired result. Scope, fees, delivery and support are confirmed before work starts.
Operating costs. Existing CLI subscriptions, model-provider usage and hosting may apply depending on the tool and your configuration.
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