Excel to Feishu Dashboard Workflow

The problem

Moving Excel, CSV, or TSV files into Feishu Base is not just about successful upload. Teams often hit mismatched field types, uncleaned dirty data, duplicate appends, accidental production changes, and dashboard metrics that cannot be audited. This skill targets data delivery workflows, requiring a closed loop from source inventory, business-goal confirmation, field mapping, dry-run, and final writes, with a verifiable evidence trail.

Workflow and capabilities

• Data inventory and cleaning: identify sheets, header rows, merged cells, formulas, blank rows, and subtotal rows, then report missing rates, duplicates, anomalies, and sensitive-field handling.
• Field modeling: generate a field schema from sample values and business semantics, and maintain a mapping table covering source field, Feishu field, type, write format, and rules.
• Feishu write gates: check lark-cli, base commands, login state, and base_token; run dry-run or test-table writes before online writes, with explicit confirmation for high-risk actions.
• Delivery acceptance: provide cleaned data, anomaly logs, quality reports, import logs, failed rows, local-vs-Feishu validation, and view or dashboard configuration plans.

Caveats: if the user has not authorized access to the target Feishu space, or production overwrite scope is unclear, the workflow should stop online writes and fall back to local deliverables. Components unsupported by the API or CLI become manual configuration checklists.