git:20260517.5770401 to git:20260520.2f3d92e

# DESIGN

The design rationale for `scriptorium`: an agentic operating system for

scholarly writing.

## The thesis

AI changes the operating model of scholarly writing in the same way it

changes the operating model of research institutions more broadly. The

question isn't whether AI generates prose — that's table stakes — but

how it composes with the rest of the editorial workflow: shared state,

inspectable transformations, structured outputs, conservative edits,

versioned revisions. Scriptorium is that operating model, shipped as

code.

The medieval scriptorium had:

- A coordinated workspace (the room itself)

- Shared conventions (paleographic, structural, citational)

- Multiple specialized roles (copyist, illuminator, glossator, corrector)

- An inspectable artifact at every stage (the in-progress manuscript)

The agentic scriptorium has:

- A coordinated workspace (the manuscript repo, with `MANUSCRIPT_STATE.yaml`)

- Shared conventions (the schema; the structured-output discipline; preservation rules)

- Multiple specialized capabilities (citation-audit, reviewer-simulation, argumentative-flow, …)

- An inspectable artifact at every stage (semantic diffs, structured critiques, checkpointed revisions)

## Design philosophy

The system separates four kinds of operations:

| Category | What it does | Example skills |

|---|---|---|

| **Generation** | Produces new prose | (Phase 2: specific-aims, discussion-drafting) |

| **Critique** | Assesses existing prose without modifying it | citation-audit, reviewer-simulation |

| **Validation** | Checks for structural / factual / consistency issues | (Phase 3: figure-text-alignment, statistics-consistency) |

| **Normalization** | Applies style, terminology, journal conventions | (Phase 3: terminology-normalization, journal-style-conversion) |

| **Transformation** | Modifies prose to improve specific properties | argumentative-flow, (Phase 2: compression, redundancy-removal) |

And prefers:

- **Inspectable transformations.** Every edit is reviewable. No hidden rewrites.

- **Structured outputs.** Skills emit predictable section headers so downstream skills can consume them.

- **Semantic diffs.** When prose changes, the change is justified and minimized.

- **Checkpointing.** Long workflows checkpoint between stages; nothing is irreversible.

And avoids:

- **Giant prompts.** Each skill has one responsibility.

- **Unconstrained rewriting.** Transformative skills preserve citations, statistics, and terminology by default.

- **Hidden state.** Everything load-bearing lives in `MANUSCRIPT_STATE.yaml` or in explicit skill arguments.

- **Citation hallucination.** Critique skills don't invent citations; transformation skills don't add them.

## The shared-state contract

Every project that uses scriptorium maintains a `MANUSCRIPT_STATE.yaml`

file at its root. The schema is defined in `schemas/manuscript-state.schema.json`.

The file declares:

- Project metadata (title, target type, target venue)

- Document phase (draft, review, revision, submission)

- Core claims (what the manuscript is arguing)

- Known weaknesses (limitations the authors have already acknowledged)

- Terminology (preferred + forbidden words)

- Style (tone, voice)

- Constraints (preserve_citations, preserve_statistics, avoid_hype)

Every skill reads this file. Critique skills use it to ground their

assessment. Transformation skills use it to bound their edits. The

file is the gravity well that keeps the workflow coherent across

sessions and across skills.

A skill that doesn't read `MANUSCRIPT_STATE.yaml` is doing something

local — fine for utilities, but it isn't part of the OS.

## Skill granularity

One skill = one responsibility.

Good:

- `citation-audit` (audit citations)

- `argumentative-flow` (improve logical flow)

- `compression` (reduce length without losing meaning)

Bad:

- `improve-paper` (does what? touches what? touches everything?)

- `make-it-better` (no contract; no inspectability)

Granularity matters because composition matters. An orchestrator

calling 5 narrow skills produces an inspectable trace; an orchestrator

calling 1 wide skill produces a black box.

## Conservative-edit posture

Transformation skills default to:

- Preserving every citation

- Preserving every quantitative statement

- Preserving terminology declared in `MANUSCRIPT_STATE.yaml`

- Minimizing rewrite surface area

- Explaining each modification

This is uncomfortable for skills like `argumentative-flow` that want

to move things around — but the discomfort is the point. A scriptorium

copyist who silently improved the author's prose was not a copyist;

they were a problem. The same applies here.

Skills that need to make aggressive changes should be invoked

explicitly, named loudly in their description, and emit a clear

"Remaining Weaknesses" section so the author sees what they didn't

fix.

## Invocation discipline

Skills come in two flavors:

- **Auto-invocable** — the model may suggest these when their

description matches the user's intent. Reserved for read-only

critique skills where over-invocation costs nothing.

- **Explicit-only** — must be invoked by name (`/scriptorium:<name>`).

Reserved for transformative skills where surprise rewrites are

unwanted.

A skill's description declares which mode it intends. There's no

automatic enforcement — the discipline is in the description language

and in user habit.

## Defensive design choices

Several of scriptorium's design choices are *responses* to documented

failure modes of AI writing assistance. The choices look conservative

because the failure modes are real.

### Hallucinated citations

LLMs reliably invent references with plausible authors, journals, and

DOIs ([Walters & Wilder 2023 *Sci Reports*][walters-wilder-2023];

[Bhattacharyya et al. 2023 *Cureus*][bhattacharyya-2023]). Scriptorium's

critique skills (`citation-audit`, `reviewer-simulation`) **must not

generate, suggest, or add citations** under any circumstances. They can

only assess existing ones. This is hardcoded by convention and by

`MANUSCRIPT_STATE.yaml`'s `constraints.preserve_citations: true`

default. See [`knowledge/citations/hallucination-in-llm-citations.md`](knowledge/citations/hallucination-in-llm-citations.md).

### Authorial-voice loss / "ChatGPT smell"

[Kobak et al. 2024][kobak-2024] documented the lexical fingerprint of

LLM-edited scientific writing — over-use of "delve," "intricate,"

"underscore," and similar markers. Scriptorium's conservative-edit

posture (preserve source language, transformations are minimal and

inspectable, never auto-invoked) is the design defense against this.

We don't claim to eliminate the smell, only to minimize and surface it.

See [`knowledge/prior-art/ai-writing-failure-modes.md`](knowledge/prior-art/ai-writing-failure-modes.md).

### Automation complacency

[Parasuraman & Manzey 2010][parasuraman-2010] established that users

over-rely on automated decision aids, missing what the aid missed.

Scriptorium's structured outputs include explicit **"What this skill

did NOT check"** sections, forcing the user to engage with the limits

rather than treating the output as comprehensive.

### Suggestion-acceptance bias

Buschek, Jakesch, and colleagues' CHI work shows users accept LLM

suggestions even when they would have written differently — biasing

the resulting prose toward the model's stylistic defaults. Scriptorium

transformative skills are **explicit-invocation only** (never

auto-invoked) and emit inspectable diffs so the user can reject as

easily as accept.

### Author skill degradation

This is the failure mode scriptorium does **not** defend against.

Over-reliance on AI writing assistance plausibly atrophies the

underlying skill, but the evidence is too early to design around. We

flag this honestly as a known limit rather than pretend the

conservative-edit posture solves it. Author retains responsibility for

their writing.

[walters-wilder-2023]: https://doi.org/10.1038/s41598-023-41032-5

[bhattacharyya-2023]: https://doi.org/10.7759/cureus.39238

[kobak-2024]: https://arxiv.org/abs/2406.07016

[parasuraman-2010]: https://doi.org/10.1177/0018720810376055

## Scope

Scriptorium's evidence base is most thoroughly grounded in **biomedical

and clinical reporting standards** — EQUATOR Network guidelines,

CONSORT, STROBE, PRISMA, ARRIVE, STARD, TRIPOD+AI, CONSORT-AI/SPIRIT-AI.

This is where the reporting-guideline density is highest, where the

LLM-hallucination evidence is most thoroughly studied, and where the

audience overlap is greatest with the project's authors and

contributors.

Extensions to other fields — physics (PRL "Letters" format), CS/ML

(NeurIPS norms, no-theorems-vs-theorems papers), qualitative social

science, mathematics (theorem-proof structure), economics (alphabetical

authorship), humanities (argument-driven, not IMRaD) — are welcome

but not yet evidenced. PRs adding discipline-specific knowledge

layers and skill variants are explicitly invited; see

[`knowledge/scientific-writing/discipline-conventions.md`](knowledge/scientific-writing/discipline-conventions.md)

for the landscape.

For v0.1–v0.3, default behavior assumes biomedical/clinical conventions.

This is stated, not silent — so readers in other fields encounter the

bias as explicit scope rather than hidden assumption.

## Manuscript format scope

Scriptorium operates on manuscript text. **We strongly suggest converting

your manuscript to markdown first** using your preferred tool — see the

[Convert your manuscript to markdown](https://github.com/seandavi/scriptorium/blob/main/docs/src/content/docs/how-to/convert-to-markdown.md)

how-to guide. Scriptorium does not ship converters and does not promise

round-trip preservation of format-native features such as tracked

changes or field codes.

Outputs are structured markdown reports; applying suggestions back to

the original manuscript is the author's responsibility.

For deeper format-specific integration — Quarto pre-render hooks,

LaTeX `\cite{}` parsing, Word tracked-changes round-tripping — see the

optional adapter packages (none ship with v0.1).

### Tier structure

Three tiers, increasing in coupling to a specific source format:

| Tier | What ships when | Scope |

|---|---|---|

| **Tier 1 — Format-neutral core** | v0.1 | Markdown-flavored text in, structured markdown reports out. Universal scope. No source-format awareness. |

| **Tier 2 — Optional `source_format:` hint** | Schema lands v0.1; skills consume v0.2+ | `MANUSCRIPT_STATE.yaml` carries `project.source_format` (enum: `quarto \| latex \| markdown \| docx-via-pandoc \| gdocs-export \| other`). Skills *may* use the hint for smarter parsing; never required. |

| **Tier 3 — Format-specific adapter packages** | Out of repo, out of v0.1 | Optional packages — quartobot integration, scriptorium-latex, scriptorium-docx, etc. Live as independent projects; depend on scriptorium's CLI / schema; ship their own knowledge layer extensions where needed. |

The reason "we strongly suggest" rather than "only works on": soft

expandability. The framing doesn't box out future Tier 2/3 work, doesn't

overpromise on Word/LaTeX/Quarto native handling today, and preserves

agency for users who know what they're doing with their own format

pipelines.

The `source_format` hint also informs what skills can and cannot do.

A skill that operates on citations might use `format: latex` to look

for `\cite{}` rather than `[@key]`; one that operates on figures

might use `format: quarto` to look for `#| label:` cross-references.

Until a skill *uses* the hint, it's metadata for the author and for

future tooling, not behavior.

## Roadmap

### v0.1 (this release): the leaves

Three skills, schema, Venice example. Goal: prove the shared-state +

structured-output pattern works on a real manuscript. If the three

skills don't compose usefully on the Venice paper, the design is

wrong and we revisit before building more.

- `citation-audit` (critique)

- `reviewer-simulation` (critique)

- `argumentative-flow` (transformation)

### v0.2 (next): coordination

If v0.1 holds up, add the first orchestrator. The orchestrator's

sole job is to sequence the leaves intelligently for a given

document phase.

- `manuscript-pipeline` (orchestrator)

- `revision-summary` (utility)

### v0.3: more leaves

Once the orchestrator pattern works, add high-leverage additional

leaves driven by what we needed and didn't have.

Likely candidates: `compression`, `redundancy-removal`,

`statistics-consistency`, `terminology-normalization`. Order

determined by which gap bites first.

### v0.4: drafting

Generation skills are harder than critique skills — they're the most

opinionated and the most likely to produce off-key prose. Defer until

the critique/validation/normalization layers are mature enough to

catch generation mistakes.

Likely candidates: `specific-aims`, `results-narrative`,

`discussion-drafting`.

### v0.5+: knowledge layer + platform adapters

Once skill content stabilizes:

- A `knowledge/` directory of reusable editorial heuristics (journal

styles, grant-program patterns, reviewer-archetype tells) that

individual skills reference explicitly.

- Adapters that compile SKILL.md down to platform-neutral prompts for

Codex / Gemini / ChatGPT / Hermes / etc.

## Naming

The project is `scriptorium`. Repo name on GitHub will be

`seandavi/scriptorium`. PyPI package name, if published, will be

something more disambiguated (e.g., `agentic-scriptorium`) because

the bare PyPI name is taken — but the project itself is `scriptorium`

everywhere it appears in narrative.

The tagline is *an agentic scriptorium for scholarly writing*. Use it

in talks, posts, papers. The repo name does the search/install work;

the tagline does the brand work.

## Non-goals

- **Replacing pandoc/Quarto/LaTeX.** Scriptorium produces structured

outputs about prose; it does not produce final-formatted documents.

- **Replacing a reference manager.** Citation audit works with whatever

bibliography the manuscript already uses.

- **Replacing peer review.** Reviewer simulation is a sparring partner,

not a substitute for real reviewers.

- **Generating manuscripts from scratch.** This is intentionally not a

v0.1 goal. The harder design questions are about coordination, not

generation.

## Why this is open source

The thesis — that AI changes the operating model of research

institutions — is more credible when the operating model itself is

inspectable. A closed-source agentic scriptorium would be a black-box

peer-review-as-a-service play; an open one is documentation of what

the new operating model actually looks like, available to anyone who

wants to fork it.

Practically: the scholarly-writing audience is small enough that

network effects come from openness, not from lock-in.