dsh-excel-kit

The most reliable read-only Excel analysis toolkit for dsh (DeepSeek Harness).

One sentence of value: stream any xlsx, however large, into compact, useful analysis (describe / filter / pivot) — without ever loading the whole workbook into memory, and without ever writing or formatting a cell.

Version lock warning: this plugin is built and verified against DSH 0.1.0-rc.6. Do NOT npm install dsh-excel-kit@latest blindly — use the locked range >=0.1.0-rc.6 <0.2.0 (or whatever the host you run declares). The dsh plugin ABI may change between versions.

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Features (3–5 core)

1. Streaming, big-file safe — a home-grown xlsx reader built on yauzl (streaming zip) + sax (streaming XML). No XLSX.readFile, no whole-workbook load, no OOM on 100 MB+ files.
2. Three focused read tools — excel_describe, excel_filter, excel_pivot. They return compact JSON (aggregates, samples, matched rows) — never a firehose of raw rows.
3. Spill integration — oversized tool results are persisted through ctx.spillStore and replaced in the value with a compact summary + spilled locator.
4. Date-aware typing — numFmtId from xl/styles.xml is used to convert Excel serial dates into ISO strings (2024-01-01 / 2024-01-01T10:30:00.000Z).
5. Cancellable & concurrency-safe — every long scan forwards exec.signal (AbortSignal); read-only tools declare isConcurrencySafe.

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How it works

The plugin is a normal dsh plugin: entry exports name / inject / apply and registers tools onto ctx.tools. Under the hood it opens the xlsx as a zip, streams only the entries it needs, and parses rows one at a time.

Capability seams: dsh ⇄ this plugin

dsh capability	How this plugin adapts it
ctx.tools.register (dsh-tools)	Registers excel_describe / excel_filter / excel_pivot via a defineTool shape: parameters are MCP-style top-level JSON Schema ({ type: 'object', properties, required }), output.schema declares the { content, structuredContent } wrapper, output.render projects content, isConcurrencySafe, and async execute(args, exec) returns { content: ContentBlock[], structuredContent: <result> }.
exec.signal (AbortSignal)	Forwarded into the streaming reader (streamSheet, sharedStrings load); aborts stop decompression/parsing promptly.
ctx.spillStore (dsh-spill)	Results larger than the threshold (default 32 KB) are persisted via ctx.spillStore.saveText({owner, source, suggestedName, content}); the returned value becomes { spilled: { locator, bytes, retrievalHint }, tool, summary }. A same-interface in-memory fallback is used when no real spillStore exists.
dsh.bundle in package.json	Declares cordis.patch.yml so dsh plugin --profile <name> add dsh-excel-kit injects the excel-kit entry into the profile config.
ctx.excel (potential future service)	The XlsxStreamReader is exported so a future provider service can reuse the same streaming engine cross-plugin. (Not yet registered as a service.)

The streaming reader (the moat)

• yauzl opens the file with lazyEntries and reads the central directory — no whole-file decompression.
• xl/sharedStrings.xml (when present) is parsed into a chunked, spilled string array: strings accumulate into a chunk, and when a chunk crosses 8 MB it is flushed to a temp file; random access goes through a small LRU cache of on-disk chunks. Memory stays bounded even for huge shared-string tables.
• xl/worksheets/sheetN.xml is parsed with sax and rows are assembled one <row> at a time; cells handle inlineStr, s (SST index), numeric (incl. E scientific notation), t="str", t="b", t="d" and empty cells.
• xl/styles.xml numFmtId is used to detect date columns (built-in 14–22 / 45–47, and custom formats containing y/m/d); date cells are emitted as ISO strings.
• Row data is handed to a callback and never accumulated into a full-table array.

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Tools at a glance

Tool	Purpose	Key parameters	Returns
excel_describe	Profile a workbook/sheet	file_path (req), sheet, max_rows, sample (default 3)	sheets, rows, cols, per-column type distribution, non-empty count, empty rate, numeric min/max/mean, samples
excel_filter	Filter rows by conditions	file_path (req), sheet, conditions (req), columns, limit (default 100, cap 500)	matched/returned, projected rows
excel_pivot	Group-and-aggregate	file_path (req), sheet, rows (req), values (req), limit (default 50)	groups, nested keys like `dept

Condition operators: eq | ne | gt | gte | lt | lte | contains | in | between.

Aggregation operators: count | sum | mean | min | max.

Return contract: every tool's execute resolves to { content: [{type:'text', text}], structuredContent: <result> } (dsh-tools rc.6 registry shape). The JSON payloads below are the structuredContent values; content is the human-readable rendering shown by the model. When a result exceeds the spill threshold (32 KB default), structuredContent becomes { spilled: { locator, bytes, retrievalHint }, tool, summary }.

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Install

Requires a dsh installation (0.1.0-rc.6 compatible). From the plugin's package directory:

# 1) inside dsh, add the plugin to a profile
dsh plugin --profile <name> add dsh-excel-kit

# 2) or install manually then apply via dsh.bundle patch
npm install dsh-excel-kit

The dsh.bundle.patch (cordis.patch.yml) inserts:

- insert:
    - id: excel-kit
      name: 'dsh-excel-kit'

Do not install @latest blindly — pin to the compatible range (see the warning at the top).

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Configuration

The plugin follows the dsh name / inject / apply contract and ships no plugin-level Config (a plain, non-schemastery Config object breaks the cordis loader, which calls Config.validate()). Behavior is tuned by constants in source and documented in this README:

Tuning knob	Default	Meaning
spillThreshold	32 * 1024 (32 KB)	Result serialization threshold (bytes) above which ctx.spillStore is used.
filter limit	100 (hard cap 500)	Max rows returned by excel_filter.
pivot limit	50	Max groups returned by excel_pivot.

Per-profile overrides belong in the profile's cordis.patch.yml layer (e.g. disabling the plugin entry), not in a plugin Config.

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Usage examples

1) excel_describe

{
  "tool": "excel_describe",
  "arguments": {
    "file_path": "/data/reports/2026-08-sales.xlsx",
    "sheet": "Sheet1",
    "sample": 3
  }
}

Compact result (truncated):

{
  "file_path": "/data/reports/2026-08-sales.xlsx",
  "sheet": "Sheet1",
  "sheets": ["Sheet1", "Sheet2"],
  "total_rows": 10001,
  "total_cols": 6,
  "columns": [
    { "col": 0, "header": "id", "total": 10001, "non_empty": 10000, "empty_rate": 0.0001,
      "types": { "number": 10000 }, "numeric": { "min": 1, "max": 10000, "mean": 5000.5, "sum": 50005000 },
      "samples": [1, 2, 3] },
    { "col": 4, "header": "order_date", "total": 10001, "non_empty": 10000, "empty_rate": 0.0001,
      "types": { "date": 10000 }, "samples": ["2026-08-01", "2026-08-02", "2026-08-03"] }
  ]
}

2) excel_filter

{
  "tool": "excel_filter",
  "arguments": {
    "file_path": "/data/reports/2026-08-sales.xlsx",
    "conditions": [
      { "column": "region", "op": "in", "values": ["华东", "华南"] },
      { "column": "amount", "op": "gte", "value": 5000 }
    ],
    "columns": ["id", "region", "amount"],
    "limit": 100
  }
}

Result shape:

{
  "file_path": "/data/reports/2026-08-sales.xlsx",
  "sheet": "Sheet1",
  "columns": ["id", "region", "amount"],
  "matched": 823,
  "returned": 100,
  "truncated": true,
  "rows": [
    { "row": 4, "values": { "id": 3, "region": "华东", "amount": 12800 } }
  ]
}

3) excel_pivot

{
  "tool": "excel_pivot",
  "arguments": {
    "file_path": "/data/reports/2026-08-sales.xlsx",
    "rows": ["region", "channel"],
    "values": [
      { "column": "amount", "agg": "sum" },
      { "column": "amount", "agg": "mean" },
      { "column": "id", "agg": "count" }
    ]
  }
}

Result shape:

{
  "file_path": "/data/reports/2026-08-sales.xlsx",
  "sheet": "Sheet1",
  "rows": ["region", "channel"],
  "values": [{ "column": "amount", "agg": "sum" }, { "column": "amount", "agg": "mean" }, { "column": "id", "agg": "count" }],
  "groups": 8,
  "truncated": false,
  "data": [
    { "key": "华东|线上", "group": { "region": "华东", "channel": "线上" },
      "values": { "sum:amount": 1234567.89, "mean:amount": 5234.56, "count:id": 236 } }
  ]
}

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Verification record

Check	Command	Assertion target	Result
Build	npm run build	tsc strict passes	PASS (fixed 2026-08-18): npx tsc --noEmit 0 errors; noEmitOnError:true added to tsconfig to prevent emitting on error
Unit	npm test	20+ assertions: date conversion, condition matching, describe/filter/pivot correctness, SST/date/empty cells, SheetJS cross-check	PASS 19/19 (tsx --test, ~9.3s)
Fixtures	npm run gen:fixtures	small.xlsx ~1 MB + large.xlsx ~100 MB generated streaming	PASS small.xlsx 1,725,588 B (~1.65 MiB); large.xlsx 139,715,025 B (~133.2 MiB)
Integration	npm run test:integration	1 MB vs 100 MB timing; peak RSS delta < 800 MB; three tools usable on 100 MB; compact JSON results	PASS 6/6. describe large 26.9s / RSS +153.2 MB / JSON 2215 B; filter large 20.9s / +63.6 MB (matched 63,228); pivot large 21.8s / +36.2 MB; SST-chunk spill case 34.3s / +5.9 MB

Numbers recorded by QA (2026-08-18) from independent verification runs.

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Known limitations

• Read-only by design: v0.1.0 strictly forbids writing or formatting. No set_cell, no style mutation.
• SheetJS CE is a dependency for test cross-validation only; the production reader never calls XLSX.readFile.
• excel_filter and excel_pivot reference columns by header name (first row). Empty header cells fall back to colN (1-based).
• mean/sum/min/max operate on numeric values (numbers or numeric strings); non-numeric cells are ignored for numeric aggs. count counts non-empty values of the column within a group.
• Group keys in excel_pivot join values with |; a value containing | may collide with a nested key (rare in practice).
• Only the first sheet's xlsx (.xlsx, zip-based) format is supported — not legacy .xls, .xlsb, or password-encrypted files.
• Streams are bounded, but excel_describe with default settings scans every row (that's the point — it is streaming, not free).

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License

MIT — see LICENSE.