DataTally
Official domain: https://datatally.xyz
Help AI find the most valuable data.
帮助 AI 找到最值钱的数据。
DataTally records. AI judges.
For AI, "valuable" means worth the compute — the cost of using data is time and tokens, not money. DataTally helps AI find data worth using.
DataTally is a DeepSeek Harness plugin that aggregates public usage signals of data assets — downloads, citations, stars — into a single, verifiable profile.
What it does
Data has no intrinsic properties. A byte count says nothing about value. The only real signal of a data asset's value is how it has been used — by whom, how often, and with what results. DataTally collects those usage signals from public sources and presents them in one place:
| Tool | What it does |
|---|
search_assets | Search data asset profiles by keyword or domain |
get_asset_profile | Get the full usage profile of one asset: per-source metrics, timeline, citations |
compare_assets | Compare two assets across dimensions (within the same signal layer) |
Every metric carries its source and fetch time. Nothing is interpreted. Nothing is ranked. DataTally records; you judge.
DataTally never: computes value, weighs signals, ranks assets, recommends "which is better", sells data, or fabricates numbers. The record is DataTally's. The interpretation is the user's.
Signal layers
- Deep signals — actual use: downloads, citations, forks, commits
- Shallow signals — interest only: stars, likes ("bookmarked" ≠ "used")
Layers describe behavior versus interest — not a strength scale. Comparisons are only made within the same layer. A star is not a download. Missing provenance is marked explicitly — a single-source asset says so in its profile summary; a missing license surfaces as null, never a fabricated default.
Install
Requires DeepSeek Harness installed (dsh on your PATH) and Node.js ≥ 22.19.
dsh plugin --profile web add datatally
dsh web
From a checkout (development)
git clone https://github.com/DAAMAAO/datatally.git
cd datatally-plugin
npm install
npm run typecheck
npm test
Load it into a running dsh for end-to-end testing:
dsh web --patch ./cordis.patch.yml
# (replace the `name` in cordis.patch.yml with the absolute path to src/index.ts)
Use it
Ask your agent:
"Find datasets for sentiment analysis and compare the two most used ones."
The agent will call search_assets, then compare_assets, and answer with sourced usage data. An actual session transcript:
User：帮我找情感分析的数据集，然后对比两个使用最多的
Agent（调 search_assets("sentiment")）：
- imdb (stanfordnlp/imdb) | domain: nlp | deep: downloads 195,669 | shallow: likes 725 | fetched_at: 2026-09-08T14:35:50Z | snapshot: v2
- glue (nyu-mll/glue) | deep: downloads 826,859 … （共 8 个命中）
Agent（调 compare_assets("nyu-mll/glue", "stanfordnlp/imdb")）：
- downloads [deep]: 791,429 vs 197,595 | same_source: true | same_access: true
- forks [deep]: 178 vs 0 | same_source: false | same_access: true
- likes [shallow]: 741 vs 722 | same_source: true | same_access: true
note: cross-source metrics are not directly comparable
Agent 的最终回答只陈述记录（引用次数、来源、抓取时间），不下"哪个更好"的结论——判断留给用户。
Configuration
The plugin ships with defaults; override per row in your profile patch:
| Field | Default | Meaning |
|---|
snapshotPath | ./data/snapshot_v2.json | Local snapshot file. Relative paths resolve against the process cwd first, then against the package's bundled data/ seed. |
maxResults | 20 | Search result cap (the limit parameter is clamped to it). |
enableCitationFields | true | Include the citations field in profiles. |
Snapshot format
DataTally reads usage data from a local, self-owned JSON file (version 2). Replace the seed by pointing snapshotPath at your own file:
{
"version": "2",
"generated_at": "2026-09-06T12:00:00Z",
"assets": [
{
"asset_id": "stanfordnlp/imdb",
"id_type": "hf",
"name": "imdb",
"domain": "nlp",
"access": "open",
"license": "other",
"verification": "public_api",
"sources": [
{
"source": "huggingface",
"metrics": {
"downloads": { "value": 191564, "signal_type": "deep" },
"likes": { "value": 709, "signal_type": "shallow" }
},
"fetched_at": "2026-09-08T12:32:11.463Z"
}
],
"timeline": [],
"citations": []
}
]
}
Schema principles: structured from day one · every field has a source · evidence attributes are facts, not interpretations · snapshots are self-owned · machine-readable first. Unknown fields (including the legacy asset_class) are rejected loudly; v1 snapshots are rejected with a migration pointer.
CLI
The same core, in a terminal (the thin-wrapper form over the plugin core):
datatally profile stanfordnlp/imdb
datatally search sentiment --domain nlp --limit 5
datatally compare HuggingFaceFW/fineweb allenai/c4
datatally export nyu-mll/glue # AI-BOM fact entry: pure facts, no conclusions
datatally catalog # per-sector distributions (multi-source rate, model-use density)
# snapshot location: --snapshot <path> or DATATALLY_SNAPSHOT env
The CLI is read-side only: every command reads a snapshot file. Rebuilding a snapshot (the four-source refresh pipeline) is a maintainer operation in the private workspace; the pipeline never ships in this package or the public repository.
Development
src/
├── index.ts # plugin entry: Config + apply + tool registration
├── core/ # pure query logic (no harness deps) + text rendering
├── tools/ # one defineTool per file: schema + execute + render + UI cards
├── snapshot/ # loader (strict validation, fail-loud), types, AssetId brand
├── schema/ # strict snapshot validator
cli/main.ts # read-side CLI (profile/search/compare/export/catalog)
test/ # read-side tests: 42 unit + keyless drive + goldens
data/snapshot_v2.json # seed snapshot (126 real datasets, 9 sectors)
cordis.patch.yml # bundle patch (installed) / dev patch (checkout)
The maintainer's local checkout additionally holds the private refresh pipeline (src/snapshot/fetchers/ and its tests): excluded from this repository by .gitignore plus a hard deny-gate in the push script, and never present in npm tarballs (the files allowlist has no matching glob).
npm run typecheck — strict TypeScript, no errorsnpm test — builds, then runs the read-side suite (42 tests) including keyless drive tests through the real ctx.tools.execute pipeline- Peer packages (
@deepseek-ai/cordis, dsh-tools, dsh-llm) are provided by the DeepSeek Harness deployment, exactly like the official dsh tool plugins.
Data provenance
The seed snapshot carries real public usage data for 126 open Hugging Face datasets across 9 industry sectors — core (famous benchmarks + sentiment classics), protein, agri-commodity, auto-sales, aviation, insurance-weather, carbon, power, and logistics — aggregated from up to four public sources (fetched 2026-09-08). Each asset carries an optional sector label, searchable like any other field:
| Source | Signals |
|---|
| Hugging Face Hub | downloads, likes, model uses (deep / shallow / deep) |
| ModelScope | downloads, likes (mirrored datasets, probed by short name) |
| DataCite | citation counts (when the dataset card carries a DOI) |
| GitHub | stars (shallow), forks/commits (deep) — only for curated dataset→repo mappings whose repo is the dataset's canonical release home (see data/curated.json) |
Provenance discipline: every metric carries its source + fetched_at; model_uses is exact below the scan cap and recorded as model_uses_min (an honest lower bound) at the cap; single-source assets are marked explicitly ("single source only — multi-source aggregation not met"); missing provenance is never fabricated. Hugging Face numbers were fetched through the hf-mirror.com mirror (counts are the mirror's index, which can differ from hf.co's counters); the maintainer-private refresh reads DATATALLY_HF_BASE (default https://huggingface.co) to switch channels.
On calibers, stated neutrally: a platform's own statistics are one caliber among several. Each caliber is recorded separately with its source and fetch time, and a profile that aggregates several calibers is more complete than one that does not — no platform's statistics are attacked or preferred; the record simply states what each caliber shows.
The seed snapshot is produced by the maintainer's private four-source refresh pipeline. The public package and repository are read-side only: they ship and read snapshots, they never fetch. To get a fresh snapshot, ask the maintainer to run the private pipeline and ship the updated data/snapshot_v2.json.
License
MIT
Contributing
Issues and pull requests welcome. Please keep contributions within the stated scope: recording usage signals, not interpreting them.
