Unscript is a terminal-based writing transformation agent built by Maisam Abbas. It stores its writing guidance as structured content in Sanity, retrieves the rules relevant to each piece of text, has a language model rewrite the text, and then runs deterministic checks on the result before showing it. The author describes it as a Sanity Challenge Path One submission, and the design question behind it is simple: can a writing agent keep its editorial knowledge outside the model, where it can be inspected and changed?
What Unscript is and what it is not
Unscript is a command-line tool, not a browser editor, a word processor plugin, or a Sanity product in its own right. Its builder presents it as an agent whose purpose is to make writing clearer and more natural while leaving intact the information that should not change. The project post was published on the DEV Community with a date of September 27, 2026, and it is the primary source for everything described below. Implementation details and results are the author’s own account; they have not been independently tested.
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How the parts divide the work
The author separates the system by responsibility. Each layer has a narrow job, which is the main reason the design is easier to reason about than a single large prompt.
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| Component | Role as described by the author |
|---|---|
| TypeScript CLI | Interactive navigation, environment inspection, knowledge inspection, and the transformation flow |
| Agent | Analyzes and classifies the input, applies the chosen transformation level and tone, retrieves relevant knowledge, and prepares the transformation task |
| Sanity | Stores the structured knowledge in schemas for writing patterns, content types, humanization levels, tone rules, transformation rules, preservation rules, sources, and user decisions |
| Sanity Context MCP | The connection used to retrieve that knowledge, including through a groq_query capability |
| Gemini 3.1 Flash-Lite | Performs the language transformation |
| Deterministic checks | Validate the generated result before it is displayed, along with information about the retrieved knowledge and its provenance |
The author states the split directly: the model handles the language transformation, while Sanity provides the structured rules and knowledge that guide it. The practical consequence is that a rule about, say, tone or preservation lives in a content record rather than inside a prompt string, so it can be read, edited, or removed without rewriting the agent’s code.
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What the knowledge base contains
The author reports the following initial inventory. These are counts from the project submission, not an independent audit of the live dataset, and they describe the starting state rather than what the system holds today.
| Knowledge type | Initial count (author-reported, 2026) |
|---|---|
| Sources | 3 |
| Content types | 8 |
| Humanization levels | 5 |
| Tone rules | 8 |
| Transformation rules | 14 |
| Preservation rules | 10 |
| Writing patterns | 10 |
| User decisions | 0 |
The zero for user decisions is worth noticing. It means the store had no recorded decisions at the time of the inventory, so the feature that would let the system learn from a user’s accept or reject choices is a schema in place rather than accumulated history.
The author names three reference sources for the guidance: U.S. Digital.gov and GSA plain-language guidance, the Microsoft Writing Style Guide, and Google’s writing guidance. The submission does not say which specific principles were encoded from each, so readers cannot yet audit how faithfully those guides are represented in the rules.
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Walking through a transformation
The author’s demo uses the Article content type, the Friendly tone, and the Custom transformation level. The flow runs in this order:
- Select the options. Choose the content type, tone, and transformation level in the CLI’s interactive navigation.
- Enter the text. Paste or type the passage to be revised.
- Analyze and classify. The agent classifies the input and considers the selected level and tone.
- Retrieve knowledge. The agent queries Sanity through the Context MCP connection for the rules that match the task.
- Transform. The retrieved guidance and the text are sent as a task to Gemini 3.1 Flash-Lite, which produces the revised version.
- Validate. Deterministic checks run against the generated output before anything is shown to the user.
- Display with provenance. The output is shown together with the knowledge that was used to produce it.
Step six is the design choice that separates Unscript from a plain prompt. Validation is a separate stage that does not depend on the model’s own judgment of its output, so checks such as whether required information survived can be applied the same way each time. The author does not publish the list of checks, so the exact rules they enforce should be read from the repository rather than assumed.
Why keep the rules outside the model
The author’s central framing question is: “What if a writing agent did not have to keep all of its knowledge inside the model?” Maisam Abbas poses it as the motivation for the project, and it points at a practical problem. When editorial rules are buried in a prompt, changing one means editing text that is hard to version, hard to test in isolation, and hard for a reader to inspect. Storing them as structured content gives each rule an identity, a type, and a place in a schema.
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That approach also makes the system’s behavior easier to explain. Because the output is displayed alongside the knowledge that informed it, a user can see which rule was in play instead of accepting a rewrite on trust. The trade-off is that the quality of the result now depends on the quality and coverage of the stored rules, which is an editorial job as much as an engineering one.
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The author lists development checks covering TypeScript compilation, linting, formatting, production build, CLI runtime, Sanity schema validation, MCP initialization and retrieval, Gemini integration, transformation, deterministic validation, and terminal input edge cases. The author also says the final end-to-end flow worked against the real Sanity Context MCP integration. These are self-reported. They show that the pieces were exercised; they do not show that the tool produces better writing.
- Established: the architecture, the stages, the stored knowledge types, and the author’s account of the checks and the end-to-end run.
- Not established: any user study, benchmark, controlled comparison, or measured improvement in writing quality or accuracy.
- Not established: that the tool outperforms other writing assistants, or that its edits are more accurate than those produced by a prompt-only approach.
Anyone evaluating Unscript should therefore treat it as a well-described design with a working demo, not as a measured product.
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How to compare it with other writing agents
Because no comparative data exists for Unscript, the fair way to compare it with other writing agents is along axes the project itself makes visible:
- Where the rules live: in the prompt, or in external structured content that can be edited independently.
- Whether retrieval can be inspected: whether the user can see which knowledge was pulled for a given task.
- Whether preservation is checked separately: whether a validation stage that is distinct from generation confirms that required information survived.
- Control surface: which content types, tones, and transformation levels the tool supports.
- Interface: here, a TypeScript CLI rather than an editor or web app.
- Published evaluation: whether any independent measurement of output quality exists. For Unscript, it does not yet.
Scoring Unscript against a competitor on these axes requires sources for both, which is outside what is available for this article.
Who this is for
Unscript is most relevant to developers who already use Sanity or want to see how editorial guidance can be modeled as content, and to writers and editors who are curious how a rule-based revision tool would expose its reasoning. It is less relevant to someone who wants a finished editing tool with a polished interface, because the project presents a demo workflow and a CLI, not a packaged product. The author’s own closing question, whether the knowledge itself can be part of the product, is the most useful lens for judging it.
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The project’s code and demo links are referenced in the original post. Readers who want to verify the claims should start there.
Source: Maisam Abbas, the author’s DEV Community post on Unscript, dated September 27, 2026.
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