The Tool Desk
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What the agent does, step by step
The project is a submission to a Sanity Challenge. Its goal is to answer questions about content while flagging where sources disagree. The author describes the flow as a short loop:
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- Receive a question from the user in the terminal, for example “According to the Nebula rulebook and errata, how many energy tokens do players start with? Are there any contradictions?”
- Write a GROQ query against the Sanity Content Lake. The model constructs the query itself, using the article schema it has been given.
- Run the query through a
query_documentsfunction, which accepts a GROQ string and returns JSON from the Sanity dataset. - Pass the JSON back to Gemini, which writes the answer using the retrieved records.
- Surface disagreements where two or more records make incompatible claims, citing the source URL for each claim.
Each article record in the described schema has four fields: title, slug, body, and source. The source field is what allows an answer to carry attribution rather than a bare statement.
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Why the schema mattered more than the model
The author reports that the first version of the agent guessed document types and returned empty results. Once the prompt specified the actual article type, the model began writing queries that returned useful records. A query of the general shape *[_type == "article" && ...] is the kind of filter that schema-aware prompting makes possible; this is an illustration of the technique, not a quoted query from the project.
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The author put it plainly in the original post: “The hardest part wasn’t the LLM logic, it was teaching the agent the schema.” For anyone building a similar tool, that is the practical lesson. Field names, document types, and the meaning of each field need to be in the prompt before the model writes a single query.
The project also sets useCdn(false) in the @sanity/client configuration so that queries go directly to the Content Lake. The author describes this as an implementation choice. The post does not measure latency or freshness, so it should not be read as evidence that direct queries are faster or more current than CDN-cached ones.
The contradiction instruction
The behaviour that gives the project its name comes from one instruction in the system prompt:
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“When two sources contradict each other, show both claims side by side with their sources. Cite the source URL for every claim. Never invent information.”
That instruction asks for three things at once: pairing conflicting claims, attaching a URL to every claim, and refusing to fill gaps. The model is only as reliable as the records it receives, so the prompt is a constraint on presentation, not a guarantee of correctness.
Two worked examples from the demo
The project ships with two built-in questions. Both use fictional Nebula data, and the source URLs are placeholders on example.com that the author labels as stand-ins for a real deployment.
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| Question | Claim in one record | Conflicting claim in another record | What the agent is designed to show |
|---|---|---|---|
| How many energy tokens do players start with? | 5 energy tokens (rulebook) | 8 energy tokens (errata) | Both values side by side, each with its source URL, and the errata treated as the correction because its title identifies it as errata |
| What is the win condition? | 10 stars | 12 stars | Both values side by side with source URLs; the demo does not state which record is authoritative beyond the labelling of the sources |
The token and star counts are invented for the demonstration. They show the shape of a contradiction answer, not the rules of any game.
What the demo does not establish
It is important to be precise about what the project proves. It shows that an agent can retrieve several records, compare their contents, and carry source URLs into a final answer. It does not report an accuracy rate, a benchmark, a controlled evaluation, or any independent test of how reliably the agent resolves conflicts.
Source authority is the weakest point. In the demo, the errata wins because its title says it is errata. That is a heuristic based on metadata, not a provenance analysis. A real publication could have an outdated errata, a draft mislabelled as final, or a page whose title misleads. A production system needs explicit rules, such as a field that records a source’s status, a date, and an ordering of authority, and the agent needs to be told how to apply them.
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Stack and versions as stated by the author
- Runtime: Node.js command-line application
- Language model: Gemini 3.5 Flash Lite
- Content queries:
@sanity/clientfor GROQ - Function calling:
@google/genai - Content store: Sanity Content Lake
These names and model versions come from the author’s write-up. The article’s author has not verified current package or model versions, and the project repository was not independently reviewed for this piece. Check the repository’s dependency file and the Gemini model list before reproducing the setup, since model names and client libraries change often.
Adapting the pattern to your own content
If you want to try the approach on a real content set, work through these items before writing any agent code:
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- Define the document types and their fields in the prompt, using the exact type name stored in the dataset.
- Store a URL on every record that might be cited, and make sure the URL is the public address of the claim, not an internal ID.
- Add a status or authority field, so that a superseded or draft record cannot be treated as final by default.
- Write the tie-break rule down in plain language, and test it on pairs of records where you already know the right answer.
- Keep the “never invent information” instruction, and check answers for citations that point to records the query did not return.
Key takeaways
- Structured fields make content queryable, and the agent is only as good as the schema description it receives.
- Keeping a source URL on each claim lets an answer show its evidence.
- Detecting a disagreement is not the same as deciding which side is correct; that decision needs explicit authority rules.
The original write-up is by the project’s author, Victor, and is available on DEV Community: Nebula Agent: An AI That Surfaces Contradictions in Structured Content, published September 27, 2026.
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