ablate sections
Full section attribution: which lines earn their keep. ~$110 default at
K=12, ~90 min. Also registered as ablate ablate (same command — sections
is the form used everywhere so it never reads as a typo).
ablate sections CLAUDE.md
ablate sections CLAUDE.md --confirm 5 --granularity bulletArguments & flags
| Argument/flag | Default | What |
|---|---|---|
FILE | auto-detected | Config file. |
--tasks DIR | .ablate/tasks | Directory of task YAML files. |
--tasks-only IDS | none | Run only these task ids or globs. |
--tasks-tag TAGS | none | Run only tasks carrying any of these tags. |
--trials N | 5 | Replicates per cell, confirmatory stage. |
--agent NAME | claude-code | claude-code or codex. |
--model ID | Sonnet 5 | Target model for the confirmatory stage. |
--screen-model ID | Haiku 4.5 | Model for the screening stage. |
--no-screen | off | Screen on the target model instead (~4× the price). |
--confirm N | 3 | How many sections to run a confirmatory A/B on. |
--granularity | section | section or bullet. Bullet is literally per-item and roughly triples the cost. |
--concurrency N | 4 | Parallel trials. |
--max-turns N | 40 | Turn budget per trial. |
--max-spend USD | 25 | Hard ceiling. |
--margin PCT | 0.05 | Equivalence margin for the confirmatory stage. |
--alpha A | 0.05 | Significance level. |
--seed N | none | Design and bootstrap seed. |
--yes | off | Skip the confirm prompt. |
--fresh | off | Ignore cached trials. |
--keep-worktrees | off | Don’t clean up trial worktrees afterward. |
What it does
Two stages:
- Screening — every section is independently kept or dropped across a
randomized subset design (
R = 2K + 8variants) on a cheap model, one trial per (variant, task). Fits a ridge regression with task fixed effects to rank sections by estimated effect. This stage ranks; it does not produce verdicts. Read the order of the table, not the coefficient magnitudes — they’re shrunk toward zero by design. - Confirmation — the top-ranked sections and the largest low-signal
(“deletion candidate”) sections get a dedicated, pre-specified paired A/B
on the target model, at full
--trials. Only this stage produces a verdict, and only an EQUIVALENT verdict licenses a deletion.
ablate apply performs the actual edit on sections this command recommends
dropping.
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