Conciseness

The body is dense with tool-specific facts Claude could not know ("silently ignored", preset names, "ngram_range ... Config-file only", "cache warm --all-embeddings"), and explanations of general concepts are absent or minimal (one-line YAKE/RAKE characterizations used to guide algorithm choice). It is not a 5 because of minor trimmable redundancy — e.g. the min_score normalization rule is explained in full in Field notes ("Both YAKE and RAKE normalize their scores to the 0.0-1.0 range with higher-is-better") and then repeated almost verbatim in Common pitfalls.

Actionability

Every section gives copy-paste-ready, complete commands and code: the inline-JSON CLI invocation ("xberg extract paper.pdf --format json --config-json '{...}' | jq '.extracted_keywords'"), a full TOML config block with defaults, "xberg embed --text ... --preset balanced", the provider matrix with the llm example including --model/--api-key, cache pre-warm commands, and a complete runnable Python snippet. This matches the 5 anchor: fully executable, covering the common CLI, config-file, and programmatic cases.

Workflow Clarity

Each task path is unambiguous (enable keywords via config, "--detect-language true", embed via the standalone command), the one real multi-step flow is shown concretely ("xberg extract report.pdf | xberg embed --preset quality"), and the pitfalls section supplies error-avoidance diagnostics (silent feature-gate ignore leaves "result.extracted_keywords ... empty rather than erroring"; cold models fixed by "xberg cache warm"). It stops short of the 5 anchor because there are no explicit validation checkpoints (e.g. how to confirm the keywords feature was actually built in), though no destructive or batch operation triggers the workflow-clarity cap.

Progressive Disclosure

Structure is good: five well-scoped sections, detail appropriately deferred ("See references/advanced-features.md for the embeddings pipeline and references/cli-reference.md for the embed and cache warm flag sets"), matching the 4 anchor. It is not a 5 because navigation has gaps: no references/ directory (or any bundle files) actually exists alongside this SKILL.md, so the two cited reference paths cannot be verified, and two more references ("references/python-api.md and references/configuration.md in the sibling xberg skill") depend on files in a different skill, a fragile cross-skill pointer.

Specificity

The description lists multiple concrete capabilities — "extracting keywords (YAKE/RAKE)", "detecting document language", "generating embeddings for RAG and search" — and even specifies the surfaces it covers ("the keyword config (and its feature gating)", "--detect-language", "the standalone `embed` command"). This matches the anchor for multiple specific concrete actions with comprehensive coverage; there is no vague filler, so it is not the 4 anchor with 'minor gaps'.

Completeness

Both halves are explicit: the trigger is stated directly ("Use when extracting keywords (YAKE/RAKE) from documents — and, secondarily, when detecting document language or generating embeddings"), and the 'what' is concretely enumerated ("Covers the keyword config (and its feature gating), `--detect-language`, and the standalone `embed` command"). This clearly matches the 5 anchor (explicit what AND when with concrete trigger phrases); it is well above the 4 anchor where the 'when' is only serviceable.

Trigger Term Quality

Good natural-term coverage: users would plausibly say "extracting keywords", "YAKE", "RAKE", "document language", "embeddings", "RAG", "search". A few natural synonyms are missing (e.g. "key phrases", "vector embeddings", "semantic search", "language detection"), which fits the 4 anchor ('good keyword coverage; a few natural terms missing') rather than the 5 anchor's comprehensive synonym list.

Distinctiveness Conflict Risk

The niche is clear and distinctive (statistical keyword extraction, language detection, and embeddings for the xberg CLI, with named algorithms YAKE/RAKE), so it is well above the 'somewhat specific' 3 anchor. However, it sits inside a broader document-extraction domain — the body itself references a sibling `xberg` extraction skill — so there is minor overlap risk with a closely related general-extraction skill, fitting the 4 anchor rather than the 5 anchor's minimal conflict risk.