Conciseness

The body is lean and example-driven (e.g., "xberg extract report.pdf --chunk --format json | jq '.chunks | length'", the chunker-type table, the "Picking parameters" ranges) with almost no explanation of concepts Claude already knows. The 8-line naming-alias blockquote ("the public Python ChunkingConfig (a dataclass) uses constructor kwargs max_characters / overlap; the Rust core struct fields are also...") is over-explanation that could be trimmed or deferred to the API references.

Actionability

Every section gives copy-paste-ready commands with real flags ("xberg chunk --text \"$(cat README.md)\" --chunker-type markdown", "--chunking-tokenizer Xenova/gpt-4o --chunk-size 512") and the Python example is complete and executable. Concrete numbers in "Picking parameters" ("500–1000 chars (or 256–512 tokens) with 10–20% overlap") cover the common cases.

Workflow Clarity

Usage paths are clearly organized (inline extraction vs standalone chunk, chunker types, tokenizer sizing, config file) and the "Common pitfalls" section documents error conditions ("Overlap ≥ size — rejected on extract; clamped to size / 4", "Tokenizer without the feature ... errors", "the standalone chunk command bails on empty text"). Chunking is non-destructive so the validation cap does not apply, but there are no explicit verification steps after chunking (e.g., checking chunk_count or chunk contents), which keeps it below a 5.

Progressive Disclosure

Sections are well-organized and the references are clearly signaled, but the body points to files that do not exist in the bundle: "See references/configuration.md for the full [chunking] schema and references/cli-reference.md for every chunk flag" — there is no references/ directory. The pointer to "references/python-api.md and references/rust-api.md in the sibling xberg skill" also cannot be verified. Broken navigation defeats the disclosure chain, matching the anchor for structure present but poorly realized.

Specificity

The description lists multiple concrete capabilities — "chunk size, overlap, markdown/yaml/semantic chunkers, tokenizer-based sizing, and the standalone `chunk` command" — comprehensively covering the skill's surface with no vague language. It narrowly beats anchor 4 ("minor gaps") since only config-file/programmatic access are omitted, which are secondary to the core actions.

Completeness

Both parts are explicit: an explicit trigger clause ("Use when splitting extracted text into chunks for LLM context windows or RAG ingestion") with concrete contexts, and a clear what ("Covers chunk size, overlap, ... the standalone `chunk` command"). This matches the anchor-5 pattern of answering both what and when with concrete trigger phrases.

Trigger Term Quality

Good natural-term coverage: "splitting extracted text into chunks", "LLM context windows", "RAG ingestion", "tokenizer". A few natural synonyms users might say are missing ("vector store", "embeddings", "segment"), which fits anchor 4 ("good keyword coverage; a few natural terms missing") rather than anchor 5's comprehensive synonym coverage.

Distinctiveness Conflict Risk

It carves a clear niche (chunking for LLM/RAG ingestion) with distinct triggers — "chunks", "RAG ingestion", "LLM context windows", "tokenizer-based sizing" — that a sibling extraction or tokenization skill would not claim, so conflict risk is minimal.