Evaluate document parsers for agent ingestion with ParseBench
Use ParseBench to run repeatable document-parser evaluations before an agent relies on PDF, table, chart, or enterprise-document output for downstream decisions.
npx skills add agentskillexchange/skills --skill evaluate-document-parsers-for-agent-ingestion-with-parsebench
Use ParseBench when an operator needs evidence about whether a document parsing pipeline is reliable enough for agent ingestion. The workflow is to select a parser or model runner, run ParseBench against representative documents, inspect structure-preservation scores, and decide whether the parsed output is safe to feed into retrieval, extraction, or decision-support agents.
What this skill actually does
The upstream benchmark focuses on failure modes that matter to production agent workflows: tables, charts, content faithfulness, semantic formatting, and visual grounding. A practical run can start with the quick test dataset, then move to the full benchmark once credentials and runner configuration are ready. The useful output is not a product comparison blurb; it is a reviewable evaluation record that shows which parser preserves the information the agent will act on.
Invoke this before shipping document-heavy workflows in finance, insurance, government, legal, or operations settings where a bad parse can create wrong downstream answers. The scope boundary is parser evaluation and acceptance gating. ParseBench does not replace the parser, LLM, or retrieval stack; it gives the operator a repeatable way to test candidates, compare outputs, and document a go/no-go decision before those outputs enter an agent pipeline.