Library · Workflows & capabilities
Verified Completion Kernel
Check a task against stated completion rules and retain what remains unfinished or needs review.
Repository: CinvanaAI/verified-completion-kernel
Recorded synthetic example. Source with a complete offline example.
See the idea in action.
fail → fail → pass; an unimplemented semantic check remains requires_review.
An explicit artifact-completion rule and successive missing, stub and substantive files.
{
"kind": "captured synthetic execution projection",
"command": "python -m examples.demo",
"input_example": "examples/demo.py",
"observed_output": {
"stages": [
"fail",
"fail",
"pass"
],
"semantic_check": "requires_review"
},
"projection_note": "Selected fields from a successful local run. Machine paths, temporary identifiers and execution timestamps are omitted. Re-run the linked example for fresh evidence."
}Try the example.
From the repository root, follow the dependency requirements in the README. This example uses supplied synthetic material.
python -m pip install -e . python -m examples.demo
fail → fail → pass; an unimplemented semantic check remains requires_review.
Complete setup and instructions ↗The interesting part.
Completion can be a record of specific satisfied and unresolved obligations rather than a free-form claim that the task is done.
Where it came from.
The pks procedural verification layer beneath a larger semantic-memory workflow.
Preserves models, verifier, artifact writers, rules, resume records and API; packages generic examples.
The larger private memory workflow and a semantic/model verifier are not supplied.