CATO Chat and Workspace

Analysis Modes

Change modes with the interface control or /standard, /fast, and/research. The mode is a product behavior contract; CATO may route work across specialized analysis, reading, research, or coding capabilities as the task requires.

Build the Context

CATO performs best when the active evidence is explicit. Use @ to mention project datasets, select workspace artifacts or versions, or send a curated literature set from the Library. Context cards let you see what will accompany the prompt.

• Refer to exact filenames, versions, variables, cohorts, and timepoints.
• State whether CATO may combine context with general knowledge or must use only selected sources.
• Remove irrelevant attachments; more context is not always better context.
• For follow-up work, confirm that the intended generated dataset or figure version is selected.

Compose and Control a Prompt

Plan mode asks CATO to present a reviewable approach before acting. Use it when the method, inclusion criteria, transformations, model specification, or expected outputs need your approval. You can also ask CATO to revise the plan rather than approving it as written.

A Reproducible Prompt Pattern

Durable Responses and Reconnection

Active responses are conversation-scoped and persisted outside a single browser request. Navigation, refreshes, frontend restarts, and transient connections do not necessarily discard the run. Reopening the conversation reconnects to available stream events and finalized output.

• A reconnecting indicator means CATO is recovering state; avoid immediately duplicating the request.
• Cancellation is durable: use Cancel once and wait for the run to acknowledge it.
• Finalization prevents a completed response from remaining indefinitely in an active state.
• Partial output may be preserved when a budget, provider, execution, or validation error interrupts the run.

Background Sandbox Jobs

Long code or scientific-computing tasks can continue as durable background jobs. Their cards remain associated with the conversation after reload and expose current state, progress messages, completion, produced artifacts, failure details, and cancellation.

The Artifact Workspace

Artifact tabs can be restored when you return to the conversation. The selected file version is part of the working state; unsaved-edit prompts protect local changes before a tab, version, or view changes.

Figures and Scientific Viewers

Generated figures appear inline and in the Workspace. Supported SVG figures can be opened in the editor to select, move, group, layer, restyle, and annotate elements. Figure workbenches preserve interactive context for supported scientific visualizations, including molecular and other domain-specific views.

• Save edits as a new version and retain the original.
• Use accessible colors, legible labels, units, uncertainty, and complete captions.
• Run visual QA after edits; confirm that data marks and axes were not accidentally altered.
• Export only after checking raster/vector fidelity and the target journal's dimensions.
• A public figure citation link can identify a shared figure; do not use it for restricted data.

Notebooks, Code, and Execution

CATO can create Python or R code, execute it in an isolated sandbox, and preserve notebooks and files as artifacts. Persistent project kernels support iterative notebook work while durable jobs support tasks that should survive a browser navigation.

• Inspect generated code, dependency versions, seeds, inputs, and output paths.
• Use Ctrl/Cmd+Enter in a focused notebook cell to run it.
• Do not assume a package is present merely because a language supports it; ask CATO to check the environment.
• Sandbox networking, compute, storage, and job duration are governed by production security and capacity controls.
• Re-run critical results from a clean state or exported environment before publication.

Manuscripts, Readers, and Annotations

Manuscript artifacts support Markdown or LaTeX editing, preview, citation-aware review, and export. PDF, HTML, and transcript readers can retain anchored annotations where the format supports them. Reviewer findings can point to evidence and artifacts without overwriting your source.

Memory

CATO can recall user-provided preferences or stable facts from project memory or, when enabled, global memory. Memory entries are visible and manageable: edit, pin, delete, disable recall, disable automatic learning, or prevent cross-project use from the memory controls.

• Project memory is appropriate for protocol choices, defined terminology, or stable study context.
• Global memory is appropriate for durable personal preferences such as writing or plotting conventions.
• Automatic learning is grounded in what you say; generated analysis findings do not become evidence by repetition.
• Review memory when CATO appears to carry an outdated preference into a new task.

Literature-to-Chat

Select works in Literature Library and send a bounded context packet to Chat. Prompt templates support asking with context, structured extraction, drafting, and planning next steps. Fetched full-text papers, when legally available, can also open in the Workspace for reading and annotation.

The reviewer can use prior evidence attached to the conversation, but you should still open the cited source and confirm that the claim is supported. Learn more in the Literature Intelligence guide.

Peer Review and Assumptions

Review controls can ask CATO to challenge methodology, trace claims to evidence, inspect artifacts, or surface assumptions. Treat the result as a structured second pass—not independent peer review. Resolve material findings in the underlying data, code, figure, or manuscript and preserve the revision.

Usage and Billing Interruptions

AI activity is metered against included credit or pay-as-you-go usage. CATO reserves usage before concurrent requests, updates the meter as work proceeds, and can stop an active operation if an account budget, rate, or concurrency boundary is reached. Settings → Billing shows the current meter, included balance, and upcoming metered charge where applicable.

Prompt Checklist

• Is the research question falsifiable and the population explicit?
• Are the correct files and versions selected?
• Are variable definitions, units, missing-value codes, and exclusions clear?
• Did you request diagnostics, uncertainty, sensitivity checks, and limitations?
• Did you specify which outputs should be saved as artifacts?
• Will you validate code, results, figures, and citations before use?

For applied examples, continue to Best Practices or use the Quick Reference.