I fixed my AI reviewer. Then I kept solving the wrong problem

I've been building an AI-assisted editorial pipeline for technical writing. Notion cards become markdown drafts in the repo, pass through review, then sync to dev.to.

Last month I shipped a post about the first big fix to my editor-critique reviewer skill: showed feedback was consistently additive, but not subtractive. editor-critique found missing framing and evidence boundaries reliably. It did not ask what should be removed when new material arrived.

The result was layered drafts: an opening stacked on another opening, the same four-step investigation loop restated in three sections, a mental-model diagram that walked through event flow the prose had already established in the previous section.

The fix was not "be shorter" in the rubric. It was naming another cognitive job in the read-through: subtractive editing. Every paragraph should continue earning its place. Flag existing redundancy and addition-induced redundancy. Pair expansion recommendations with material that would become redundant if adopted.

A companion technique, single-owner ideas, lists 2–4 core ideas and flags when the same idea appears in multiple sections without new evidence. I codified subtractive editing in the skill file along with a test case that catches additive-only critique regressions and a lightweight subtractive pass in the human revision step.

The primary critique still owns expansion. Subtractive editing is a separate observational pass, not a rewrite engine.

Incident 3: When a section becomes its own article

The last failure pushed past critique mechanics into reader cognition. While critiquing draft variants in my editorial workflow, several middle-body sections were technically correct but felt wrong in context. In one draft, an implementation walkthrough interrupted the investigation arc. In another, a full section on validation tooling read like its own mini-article.

The failure mode was narrow: a section stopped advancing the reader's current question and temporarily made another explanatory thread the center of gravity.

Adding a rubric dimension for "section focus" would have been vague. What worked was an observational lens in the editorial read-through step: name the primary thread, name the secondary thread, decide whether to compress, delay, embed later, or leave as-is.

I codified this as a Secondary explanatory thread lens in the skill file. The rubric stayed the same. It simply added a named cognitive job: track whether prose is serving the reader's current question or drifting into a side article.

What stayed constant

Three incidents, three skill changes, one pattern. Across all three, a few constraints held:

• The five-dimension rubric stayed mostly intact.


• Read-only governance did not change: critique still does not write repo files or gate publish.


• Each pass added another observational lens, not another scoring dimension.


• The expensive part was naming the cognitive job precisely enough to operationalize in a skill file.

The recurring mistake was treating undifferentiated reasoning as one pass. Each fix changed the sequence, not the rubric weight. A capable reviewer can read before it scores and still under-read if falsifying primary judgment, displacing redundant prose, and tracking reader focus all compete in the same step.

Before you expand the rubric

1. 
   List the failure modes that survived your last sequence fix.


2. 
   For each one, name the cognitive job that failed (self-falsification, subtractive editing, reader-focus tracking).


3. 
   Stage that job as its own observational pass with a frozen handoff to the next step.


4. 
   Expand the rubric only if that observational pass still misses failures in production.

Once editor-critique understood before judging, the remaining improvements came from separating kinds of reasoning into distinct stages, not from a bigger rubric or a longer single pass. I suspect the pattern may generalize beyond editorial critique.

Takeaway: When a reviewer skill plateaus after a sequence fix, ask which cognitive jobs are still sharing one undifferentiated pass. Stage them before you expand the rubric.

If you'd like to see the project behind these workflow experiments, try Codenames AI.