local-file-grounding

Grounding AI responses in local files prevents hallucinations by enforcing directory inspection and input-first reading. The assistant reads named txt, md, csv, or json files before concluding, ensuring summaries and output artifacts match the source content without fabricating unsupported details.

To generate a report based strictly on local markdown and txt documents, use a file-first reasoning approach that inspects the task directory and reads the named files before summarizing. This process enforces grounded quoting and deterministic output writing aligned to the source material.

Yes, you can enforce deterministic output file creation by applying a grounding skill that reads local task directory files first. This ensures any created or updated result file in the directory strictly matches the read contents and prevents fabricating unsupported details.

The best way to summarize local task documents without fabricating details is to enforce file-first reasoning. This mechanism requires inspecting the task directory, reading named input files, and producing grounded summaries or quotes supported only by those specific source materials.

Yes, grounded document summarization works with SVG and PNG files alongside txt, md, csv, and json formats. The process inspects the task directory, reads the named files, and applies grounded quoting to ensure all conclusions and output artifacts are supported by the local source files.