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Unscript is a terminal-based writing transformation agent built by Maisam Abbas. It stores writing guidance as structured content in Sanity, retrieves the entries relevant to the text you submit, has Gemini 3.1 Flash-Lite perform the rewrite, runs deterministic checks on the output, and displays the knowledge it used alongside the result. The design is clearly described in the author’s September 27, 2026 DEV Community post for the Sanity Challenge Path One submission. The evidence for how well it works is thinner: the post describes what was built and reports its own checks, but it contains no user study, benchmark, or comparison against other tools.
What Unscript is and what it is not
Unscript is a command-line tool. Its stated purpose is to revise text so it reads more clearly and naturally while keeping information that should stay intact. It is not a general-purpose editor, a browser extension, or a Sanity product. Sanity is the content platform where the agent’s writing knowledge lives; the agent itself is the work of an individual developer, and the post presents it as a demonstration of an agent that queries real structured content.
The core design idea: rules live outside the model
Most writing assistants encode their style guidance in a system prompt or in whatever the underlying model has learned. Unscript takes a different route. Writing patterns, content types, tone rules, transformation rules, and preservation rules are modeled as Sanity schemas and retrieved for each task. The author frames the motivation as a question: “What if a writing agent did not have to keep all of its knowledge inside the model?”
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The practical consequence is that the guidance can be inspected, edited, and extended as content, without rewriting prompts. It also means the output can be traced back to specific retrieved entries, which is the basis for the “knows why” idea in the headline. That traceability has limits, covered below.
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How the system is divided
The author separates the system by responsibility. Each part has one job, which makes it possible to see where a problem originates.
The TypeScript CLI
The CLI provides interactive navigation, environment inspection, knowledge inspection, and the transformation flow. It is the only interface described in the post.
The agent
The agent analyzes and classifies the input, takes the selected transformation level and tone into account, retrieves relevant knowledge, and prepares the transformation task.
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Sanity and Sanity Context MCP
Sanity stores the structured knowledge. The author lists schemas for writing patterns, content types, humanization levels, tone rules, transformation rules, preservation rules, sources, and user decisions. Retrieval runs through Sanity Context MCP, which the post describes as exposing a groq_query capability.
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Gemini 3.1 Flash-Lite
The model performs the language transformation. The author puts the division of labor this way: “The model handles the language transformation, while Sanity provides the structured rules and knowledge that guide that transformation.”
Deterministic validation
Before output is shown, deterministic application checks validate the generated result. The post does not enumerate which checks run, so readers should not assume they cover tone, meaning, or factual accuracy. Their purpose, as described, is to confirm the generated result conforms to the application’s requirements.
Following one revision from start to finish
The author’s demo uses the Article content type, the Friendly tone, and the Custom transformation level. A run proceeds in this order:
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- Enter the text to revise.
- The agent classifies the input and retrieves relevant knowledge from Sanity through Sanity Context MCP.
- The agent sends the transformation task, including the retrieved guidance, to Gemini 3.1 Flash-Lite.
- Deterministic checks validate the generated result.
- The CLI displays the revised text together with the knowledge that was retrieved and its provenance.
This sequence is the author’s description of a demo run. It shows the intended flow; it is not a usability review of how smoothly the flow works for other users or texts.
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What the knowledge base contains
The author reports the following counts for the initial Sanity knowledge base. These are figures from the project submission, not an independent count of the live dataset, and they describe the setup at the time of the post.
| Knowledge type | Count reported by the author (2026) | What the count does and does not show |
|---|---|---|
| Sources | 3 | Reference material the agent draws on. The post does not list their titles. |
| Content types | 8 | Kinds of text the agent can classify and transform. |
| Humanization levels | 5 | Graded settings for how far the text is pushed toward a natural voice. |
| Tone rules | 8 | Rules that shape tone, such as the Friendly option used in the demo. |
| Transformation rules | 14 | Rules governing how the text is rewritten. |
| Preservation rules | 10 | Rules meant to keep information that should remain intact. |
| Writing patterns | 10 | Patterns of clear writing the agent can draw on. |
| User decisions | 0 | The initial inventory held no recorded user decisions, so the post does not show this schema in use. |
The author names three reference families for the guidance: U.S. Digital.gov/GSA plain-language guidance, the Microsoft Writing Style Guide, and Google’s writing guidance. The post does not say which specific principles were encoded from each, so the link between those sources and any individual rule cannot be audited from the post alone.
What “knowing why” means in practice
The display of retrieved knowledge and provenance is the feature that most directly answers the headline. It tells you which entries the agent pulled for a given revision. It does not prove that the model applied those entries faithfully, nor that the validation step would catch a rewrite that changed meaning. Treat the provenance panel as a record of inputs to the transformation, and check the output against the original yourself.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchPreservation rules are the part most relevant to editorial work, because they are the mechanism meant to protect facts, names, and figures. The post describes them as Sanity content, and the validation step as deterministic. Neither description amounts to a demonstrated accuracy guarantee.
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What the evidence establishes and what it does not
The author lists the following development checks, all self-reported:
- TypeScript compilation, linting, formatting, and production build
- CLI runtime and terminal input edge cases
- Sanity schema validation
- MCP initialization and retrieval
- Gemini integration, the transformation step, and deterministic validation
The author says the final end-to-end flow worked against the real Sanity Context MCP integration. Those statements describe the author’s own testing. The post does not establish the following:
- That the output is more accurate, clearer, or more natural than what other tools produce
- That any measured improvement in writing quality occurred
- That the tool performs better than other writing agents on any metric
- How the system behaves across a broad range of texts, languages, or users
The post contains no user study, benchmark, or controlled comparison, so any claim about output quality rests on the design and the author’s description of a demo.
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Comparisons are most useful when they use concrete axes. The table below lists the axes that matter for this design and what the post supports for Unscript. It does not score competing tools, because no comparable sources were used.
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| Axis | Unscript, per the author’s post | Other tools |
|---|---|---|
| Where writing rules live | External structured content in Sanity | Check each tool’s documentation; not assessed here |
| Whether retrieval is inspectable | Yes, the CLI displays retrieved knowledge and provenance | Not stated here |
| Whether preservation checks are separate from generation | Yes, deterministic validation runs as a distinct stage | Not stated here |
| Content and tone controls | Content types, tone rules, humanization levels, and transformation levels | Not stated here |
| Interface | Terminal CLI | Check each tool |
| Published evaluation evidence | None in the post | Check each tool |
Who should look at it, and how to evaluate it
Unscript is most relevant to developers who want to see how structured content can drive a writing pipeline, or who are building a similar agent on Sanity. It is less clearly suited to writers who want a finished editor, because the post documents a developer tool and no end-user workflow. To judge it for your own use:
- Open the project code and demo links in the author’s post and read the repository before running anything.
- Run the demo path with Article, Friendly, and Custom selected, and note which knowledge entries the CLI displays.
- Test the same tool on a short text containing names, dates, and figures, then check each one in the output.
- Repeat with a different tone or transformation level and compare the displayed knowledge to see whether the changes follow from the retrieved entries.
Those steps test the claims the post actually makes. The inventory counts are a description of setup, not a measure of how well the agent writes.
The Bottom Line
Unscript is a clearly described and interesting design: writing guidance kept as inspectable Sanity content, retrieved per task, and separated from generation and validation. Its strongest evidence is the architecture itself. Its claims about output quality remain unproven, so judge it by running it on your own text.
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