executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
score_sde_scheduleDefine continuous-time score-SDE schedules, perturbation kernels, reverse drifts, and probability-flow checks for recovery experiments.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use this skill when a recovery or implementation task needs the paper's continuous-time forward perturbation and reverse-dynamics contracts without depending on the original repository. It is appropriate for VE-style reduced experiments, formula cross-checks, and small deterministic tests. Do not use it as evidence that full image-model training ran.
ve for deterministic scripts.[0, 1].sigma_min and sigma_max.g(t).sigma(t) and diffusion coefficient.-g(t)^2 score and probability-flow drift as -0.5*g(t)^2 score.Run python scripts/score_sde_schedule.py --self-test or validate the tree with validate_skill_tree.py --run-tests.
The deterministic script intentionally implements the VE formulas needed for bounded recovery. VP and sub-VP can be added by following the same contract, but this skill should not claim to reproduce full paper metrics by itself.