Put lasting feedback where it can do its job: session-wide guidance belongs in rules, repeatable procedures belong in skills, and the history behind decisions belongs in memory. A July 2026 follow-up by matsumotory describes this three-layer framework for an AI-assisted publishing workflow. It is a practical model, not evidence that every AI project uses the same system or that the method has been independently validated.
What the three layers are for
The framework separates feedback by how it should be used later, rather than keeping every correction in one growing instruction file. As matsumotory puts it, the layers are “the rules documents that are read every session, the skills that gather up fixed procedures, and the memory that keeps the history of decisions.” The author’s July 2026 explanation describes how feedback can move between them as it proves reusable.
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| Layer | Best for | Example |
|---|---|---|
| Rules | Guidance the AI should consult across ordinary sessions or tasks. | “Use concrete language and explain technical terms for a general reader.” |
| Skills | A stable procedure that should be followed when a particular kind of task occurs. | A repeatable checklist for reviewing an article before publication. |
| Memory | Decision history that helps explain why a preference or rule exists. | A note that a previous numeric readability rule made prose choppy, so it was removed. |
These examples illustrate the distinction; they are not quoted examples from the September 25 post. The author’s profile lists that post, but the accessible full explanation is the later follow-up. Author post listing
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- Record the correction and its context. Keep a dated note of what went wrong, what was changed, and why. This preserves the decision history in memory without prematurely treating every one-off correction as a permanent rule.
- Promote repeated, broadly useful guidance into rules. If the feedback should shape many future sessions, write it as an actionable instruction the AI will actually read. Avoid vague wording such as “make it better”; specify the desired behavior.
- Turn a repeatable sequence into a skill. If success depends on performing the same steps in a particular order, package those steps as a procedure rather than expanding general rules with task-specific detail.
- Connect rules to review checks. Convert important rules into criteria that can be checked after generation. Use human or AI review for matters requiring judgment; reserve automatic checks for unambiguous conditions.
- Correct work already affected. When feedback reveals a recurring defect in published material, update the relevant work as well as the instruction system, so the correction is not limited to future output.
This sequence reflects matsumotory’s described publishing workflow. It does not establish that the same arrangement is optimal for every AI product, project, or persistence mechanism.
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Separate values, habits, judgment, and hard boundaries
The July follow-up further distinguishes four kinds of instruction. Keeping them conceptually separate makes it easier to decide whether a note should guide style, describe a procedure, support a judgment, or stop publication altogether.
- Values: broad priorities that sit above individual style rules and help resolve conflicts.
- Writing habits: concrete behaviors that can be expressed as reusable rules.
- Judgment yardsticks: criteria that help assess new cases, rather than merely matching a list of prohibited words.
- Publication boundaries: non-negotiable conditions that should trigger a stop rather than a stylistic adjustment.
The author’s account illustrates why not every preference should become a rigid numeric test: they tried numeric limits on commas and sentence length, then removed them after the prose became choppy. In that workflow, mechanical automation was reserved for prohibitions with clear outcomes, while readability and other contextual judgments remained matters for review.
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What the reported workflow does—and does not—show
Matsumotory reports that, across their own publishing workflow on July 10–11, 2026, they added 48 instruction-record sections (32 dated July 10 and 16 dated July 11) and made 17 commits to a style skill (7 on July 10 and 10 on July 11). The author also reports six issues found in a rewrite check of an already published search-strategy article, alongside eight review points for an AI judge and four machine-checked prohibitions. These are the author’s counts for a two-day period, not independent measurements or general AI performance statistics.
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The author explicitly says there was not yet a yardstick for measuring whether recurring feedback had decreased: “There is still no yardstick to measure whether things have taken hold.” The figures show activity in one workflow, but do not establish that feedback became less frequent or that the system improved by a measured amount.
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Make the framework workable in your own project
- Store session-wide instructions where the AI is reliably prompted to read them; a rule that is never loaded cannot guide behavior.
- Keep skills narrow enough that the AI can select the right procedure for the task at hand.
- Use memory to retain rationale and dated decisions, not as a substitute for clear operating instructions.
- Review rules periodically: promote recurring feedback, refine ambiguous wording, and remove tests that create worse results than the behavior they were meant to prevent.
- Track recurring corrections if you want to know whether the approach is working. The author’s account does not supply a measured reduction, so that outcome should be evaluated rather than assumed.
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