Conciseness

The body is command-dense with no padding: it never explains what a PDF or table model is, and every section (output shapes, model table, config alternative, failure modes) delivers operational detail that Claude would not already know. Phrases like "(`bounding_box` is omitted when no position data is available.)" add edge-case value per token, matching the 'every token earns its place' anchor.

Actionability

All guidance is executable and copy-paste ready: complete CLI invocations (`xberg extract report.pdf --layout --content-format markdown`), a jq pipeline for inspecting `result.tables`, a runnable Python example with imports and field access, and a TOML config block. Common cases (markdown vs JSON output, model choice, noisy scans, spreadsheets) each get a specific example.

Workflow Clarity

The single extraction operation is unambiguous and the "Common failure modes" section provides explicit error-recovery feedback loops ("Empty `tables` with `--layout` on — ... Drop `--layout-confidence` to 0.3, try `--layout-table-model tatr`"). It falls short of a 5 because there is no explicit validation checkpoint (e.g., inspecting `result.tables` before proceeding) — recovery guidance exists but is remedial rather than a sequenced check.

Progressive Disclosure

Sections are well-organized and deep material is pushed to clearly signaled one-level-deep references ("See `references/cli-reference.md` for the full layout flag set"). However, the bundle ships no references/ directory, and the four referenced paths are split between an explicitly qualified "sibling `xberg` skill" (python-api.md, nodejs-api.md) and two unqualified paths (cli-reference.md, advanced-features.md) whose location is ambiguous — a minor navigation gap against the 'easy navigation' anchor.

Specificity

The description enumerates several concrete capabilities — "extracting tabular data from PDFs, spreadsheets, or images", "layout-aware table detection", "table model selection", "output formats (markdown / JSON cells)" — going beyond the 1-2 actions of the score-3 anchor. It stops short of a 5 because the capabilities are framed as topic coverage ("Covers...") rather than a comprehensive list of concrete actions.

Completeness

Both halves are explicit: "Use when extracting tabular data from PDFs, spreadsheets, or images" answers when with concrete trigger phrases, and "Covers layout-aware table detection, table model selection, output formats (markdown / JSON cells), and known limits" answers what. This matches the score-5 anchor example's structure of explicit what + explicit when.

Trigger Term Quality

Natural user phrases are present: "extracting tabular data", "PDFs", "spreadsheets", "images", "table". It misses the file extensions (.pdf, .xlsx, .csv) and common synonyms (e.g., "table extraction", "get tables out of") that the score-5 anchor requires, so it fits the 'good coverage, a few natural terms missing' anchor.

Distinctiveness Conflict Risk

Table/tabular-data extraction is a clear niche with distinct triggers; the description would not fire for general text extraction, document editing, or spreadsheet analysis tasks. Like the score-5 anchor example, it names a specific extraction target with specific trigger conditions, keeping conflict risk minimal.