dsh-data-quality

• 1024 store channel: npm i -g dsh1024 once, then dsh1024 plugin --profile web add dsh-data-quality (counts toward the deepseek1024.com install ranking).

Deterministic data profiling, cleaning, and verification for DeepSeek Harness.

All computation is plain TypeScript in the harness process — the model never does the math. A ctx.dataQuality capability seam (Service Definition / local Provider / tool Consumers) exposes three model tools plus a frozen cross-plugin citation-checking contract.

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⭐ 如果它帮到了你

这个插件是 DSH 插件家族的一员（40+ 个，全部 Apache-2.0）。如果你在用，给个 star —— 它不会解锁任何功能，但会让下一个人在搜索里更容易找到它。

English: part of a 40+ plugin family for DeepSeek Harness. If it is useful, a star helps the next person find it — nothing is gated behind it.

Compatibility

Component	Version
DeepSeek Harness	dsh-v0.1.7-rc.2 (adapted 2026-09-24): the peer range now admits the alpha.2 line; there Session.append's third parameter exists only for surface-eligible types and is a SurfaceIntent, so the audit gate still skips and the storage-domain report stays the durable copy. Verified 2026-09-24 (dual typecheck rulers + full test suite green).
Node.js	^22.19.0 || >=24.0.0
Package manager	[email protected]
Platform	Windows / macOS / Linux (host-only plugin)

What you get

• ctx.dataQuality service — a Cordis service other plugins may optionally consume (inject = ['dataQuality']). Besides the three dataset operations behind the tools, it implements the frozen verifyCitations(request) contract: verify that numbers/strings cited in a document match a dataset snapshot, with relative-tolerance numeric comparison and verified / mismatch / not-found / unverifiable statuses.
• data_profile tool — dataset profiling: row/column counts, inferred column types (number/date/boolean/string/empty/mixed), missing rates, unique counts, numeric distributions (min/max/mean/median/p25/p75), IQR outlier counts, mixed-type suspicion notes, and full-table sha256 content-hash duplicate detection with the duplicate rate and a bounded sample of duplicate row indexes. Adds a deterministic DAMA six-dimension scorecard (completeness, uniqueness, validity, consistency, timeliness, accuracy — accuracy is reported undetermined without a declared schema, never fabricated). Optional deterministic systematic sampling for large files.
• data_clean tool — ordered declarative cleaning rules: dedupe (by column group), fill-missing (constant/mean/median/forward), coerce-type (number/date/boolean; failures counted and set to missing), normalize-unit (e.g. 万/亿 suffixes to base units), trim, map-values (enum mapping). Returns a per-rule audit log, a pre-delivery contract validation summary (dedupe before/after, uniqueness, non-null and type regressions), and a bounded preview; writes the cleaned dataset only when outputPath is given, and never overwrites the source.
• data_verify tool — declarative verification rules: not-null, unique, range, regex, enum, cross-column (e.g. startDate < endDate), freshness (date column within N days of a reference date). Per-rule pass/fail with capped failing-row evidence; an overall failure is a normal passed: false result, not a tool erro

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