# Gradientize Task

Canonical: https://mosiah.org/articles/gradientize-task-skill-description/
Interactive: https://mosiah.org/#Articles%2Fgradientize-task-skill-description

//Related:// [[sources|Article Sources/gradientize-task-skill-description]] · [[notes|Article Notes/gradientize-task-skill-description]] · [[metadata|Article Metadata/gradientize-task-skill-description]] · [[Published Pieces]]

! Gradientize Task

//A skill description for converting long-running agent requests into locally orderable run geometries.//

Long-running agents fail when given checklists, vague goals, or ordinary functional specs. They complete visible steps, satisfy proxies, reward-hack tests, and drift away from the real objective.

Gradientize Task converts a user request into a run geometry: a locally orderable optimization surface for agentic work.

It outputs:

* the real artifact being improved;
* the ideal state;
* the value criterion;
* the invariant set;
* the homotopy from low-resolution reality to production complexity;
* verifier functionals;
* anti-Goodhart constraints;
* checkpoint, rollback, and escalation policy;
* stopping conditions.

Core principle: homotopy, not ladder.

Do not create a sequence of disconnected toy tasks. Define one real problem and continuously increase resolution while preserving topology and invariants.

The skill should reject or flag prompts that are not yet gradientized:

* checklist-shaped prompts;
* sparse functional specs;
* fake mocks;
* staged difficulty ladders;
* weak verifiers;
* objectives that reward proxy wins;
* tasks where local progress is not orderable.

The final output is not a plan. A plan may be generated later, but only after the run geometry is defined.

A plan says: walk this path.

A run geometry says: here is how to know uphill.