Conciseness

The body is lean and assumes competence — it never explains what chunking or RAG is, and the capitalized `chunker_type` and cross-language field-name notes document real tool quirks Claude cannot infer. Minor trimmable redundancy remains (the overlap-clamp rule appears both in "Inline during extraction" and "Common Pitfalls"), matching anchor 4 rather than 5.

Actionability

Fully executable, copy-paste-ready commands with real flags cover the common cases: inline chunking (`xberg extract ... --chunk --chunk-size 1500 --chunk-overlap 300`), standalone from flag and stdin, each chunker type, tokenizer sizing, TOML config, and runnable Python via `ExtractionConfig`/`ChunkingConfig`. Matches anchor 5.

Workflow Clarity

Usage paths are unambiguous, "Picking parameters" gives clear decision guidance by use case, and "Common Pitfalls" documents error cases (rejection, clamping, missing feature, empty input). There is no checkpoint for verifying output (e.g., inspecting `chunk_count` or chunk contents) after running, a minor validation gap matching anchor 4 rather than 5; no destructive/batch cap applies.

Progressive Disclosure

Section structure is good and bulk detail is correctly deferred ("See `references/configuration.md` for the full `[chunking]` schema and `references/cli-reference.md` for every chunk flag"), but no `references/` directory exists in this bundle, so those pointers dangle — as do the pointers to a sibling skill's `references/python-api.md` and `references/rust-api.md`. Well-organized content with broken navigation fits anchor 3 rather than 4.

Specificity

Enumerates concrete capability areas — "chunk size, overlap, markdown/yaml/semantic chunkers, tokenizer-based sizing, and the standalone `chunk` command" — plus the action "splitting extracted text into chunks". It lists covered topics rather than multiple concrete actions and omits items like inline-during-extraction chunking, matching anchor 4 rather than 5.

Completeness

Explicitly answers both: what ("Covers chunk size, overlap, markdown/yaml/semantic chunkers, tokenizer-based sizing, and the standalone `chunk` command") and when ("Use when splitting extracted text into chunks for LLM context windows or RAG ingestion") with concrete trigger phrases. Clearly matches anchor 5; the when-clause is already explicit and specific, so 4's "could be more explicit" does not apply.

Trigger Term Quality

Good natural-term coverage: "chunks", "RAG ingestion", "LLM context windows", "chunk size", "overlap". A few common variations users would say are missing (e.g., "split documents", "embeddings", "vector store"), so it fits anchor 4 rather than 5.

Distinctiveness Conflict Risk

Chunking for RAG/LLM ingestion is a clear niche with distinct triggers, but there is minor overlap risk with a closely related document-extraction skill (chunking also runs inline during extraction), which fits anchor 4 better than anchor 5's "minimal conflict risk".