Conciseness

The pitfall catalog is high-value, non-obvious material Claude does not know, but the body carries clear padding: an entire duplicated section ('## makeTimeseries Command' appears twice with near-identical intro, example, and parameter list), a ~30-line inline catalog of every function name, and two full chained-lookup example queries where one plus the rule would do. Matches anchor 3 ('mostly efficient but includes some unnecessary... content that could be tightened'); not 4 because a whole verbatim-duplicated section exceeds 'minor instances'.

Actionability

Fully executable, copy-paste-ready guidance throughout: wrong→right pitfall pairs with exact syntax and real error codes ('summarize p95 = percentile(duration, 95, rollup: avg)' fails with 'UNKNOWN_PARAMETER_DEFINED'), and complete snippets like 'timeseries avg_cpu = avg(dt.host.cpu.usage, scalar:true), by:{dt.smartscape.host}' and the two chained-lookup queries. Matches the 5 anchor: executable examples covering the common cases.

Workflow Clarity

The 'When to Load References' table gives a clear task→reading routing sequence, and the pitfall rows function as error→fix recovery loops (error message → corrected syntax). Not 5: there is no explicit end-to-end sequence for building a query (e.g., discover fields → choose fetch target → filter → aggregate), so checkpoints remain implicit rather than stated as a sequence.

Progressive Disclosure

References are one level deep and every cited path (9 task references plus all 24 references/dql/*.md spec files) exists in the bundle, with a task-keyed routing table and a function-group index for navigation — good structure per the 4 anchor. Not 5: the duplicated makeTimeseries section and the large inline function-name catalog (which mirrors the references/dql/ directory) are minor organization/placement gaps that keep it short of 'clear overview, content appropriately split'.

Specificity

Lists multiple concrete actions with comprehensive coverage: 'Core DQL syntax, pitfalls, query patterns, and query optimization', 'write, build, fix, or OPTIMIZE a DQL query', 'prevents syntax errors and makes queries faster, more efficient, and cheaper', and an explicit coverage list ('fetch commands, data models, field namespaces, time alignment, entity/smartscape patterns, metric discovery'). Not 4: coverage is comprehensive rather than having minor gaps. Voice is third-person/imperative, so no person-penalty applies.

Completeness

Explicitly answers both what ('Core DQL syntax, pitfalls, query patterns, and query optimization... prevents syntax errors and makes queries faster...') and when ('Load to write, build, fix, or OPTIMIZE a DQL query' plus a dedicated Trigger: phrase list), with a negative boundary ('Do NOT use to explain an existing query or answer product questions'). This is exactly the 5 anchor pattern of concrete what + explicit when; not 4 because the when-clause is fully explicit rather than improvable.

Trigger Term Quality

The Trigger list gives natural phrasings a user would actually say ('write/build/fix a DQL query', 'optimize my DQL', 'make my query faster/cheaper', 'create a timeseries', 'query logs/spans/metrics'), matching the 4 anchor of good coverage with a few natural terms missing. Not 5: it omits the synonyms 'Dynatrace' and 'Dynatrace Query Language' — the vendor name never appears, so a user asking about a 'Dynatrace query' would not lexically match.

Distinctiveness Conflict Risk

Clear DQL-specific niche with minimal conflict risk, and it proactively disambiguates the nearest sibling skill: 'For MONITORING a tenant's ACTUAL query consumption/billing... use dt-platform-costs — this tunes the query text, not billing data.' Matches the 5 anchor (distinct triggers, explicit boundary); not 4 because overlap risk is addressed head-on rather than merely minor.