Borrowing it
Nothing to install: this file belongs to gaotiexinqu/OneResearchClaw. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/gaotiexinqu/OneResearchClaw/main/.cursor/skills/table-grounding/SKILL.mdgit clone --depth 1 https://github.com/gaotiexinqu/OneResearchClawWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/gaotiexinqu/oneresearchclaw/table-grounding)<a href="https://agentmods.dev/skills/gaotiexinqu/oneresearchclaw/table-grounding"><img src="https://agentmods.dev/badge/skills/gaotiexinqu/oneresearchclaw/table-grounding/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/gaotiexinqu/oneresearchclaw/table-grounding"><img src="https://agentmods.dev/badge/skills/gaotiexinqu/oneresearchclaw/table-grounding.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00024 | $0.01624 |
| Opus 5 | $0.00012 | $0.00812 |
| Sonnet 5 | $0.00005 | $0.00325 |
| Haiku 4.5 | $0.00002 | $0.00162 |
Grade A, and why
table-grounding scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 262 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Table Grounding
Convert a table file into a structured table-grounding bundle.
This skill is for table grounding, not a polished final report. It should produce a stable intermediate bundle that is easy for downstream skills and agents to use.
When to Use
Use this skill when:
- the input is a
.xlsxor.csvfile - the file mainly contains tabular data
- you need a structured grounding note before downstream follow-up work
- you want schema, preview rows, summary statistics, and simple charts before writing conclusions
Do not use this skill when:
- the input is a PDF, DOCX, Markdown, or plain text document
- the goal is a polished final report
- the input is not primarily tabular
Input
A single table file:
.xlsx.csv
For .xlsx, the default behavior is to use the first sheet.
If a specific sheet is provided, use that sheet instead.
Do not silently merge multiple sheets in the first version.
The table may contain:
- numeric columns
- categorical columns
- date/time columns
- missing values
- duplicated rows
- messy column names
- mixed types
- derived or computed columns
Output Bundle
Write outputs under:
data/grounded_notes/<type>-<table_id>/
If a specific xlsx sheet is selected, the bundle directory may include a sheet suffix.
Examples:
data/grounded_notes/xlsx-sales_q1/data/grounded_notes/xlsx-sales_q1-sheet-Summary/data/grounded_notes/csv-benchmark_results/
The bundle should contain:
<bundle_dir>/
├─ extracted.md
├─ extracted_meta.json
├─ schema.json
├─ summary_stats.json
├─ asset_index.json
└─ assets/
├─ previews/
│ ├─ head.csv
│ ├─ sampled_rows.csv
│ └─ column_summary.md
└─ charts/
├─ chart_001.png
├─ chart_002.png
└─ ...
Important:
- The script stage must not generate a placeholder
grounded.md. - The agent must read the bundle and then write a real
grounded.md.
Required Workflow
When using this skill, you must follow this workflow:
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 262 lines · 24 tokens per session scan A b3b0a1038284
table-grounding is a skill published in the GitHub repository gaotiexinqu/OneResearchClaw (446 stars, last pushed 4mo ago), licensed MIT. It adds 24 tokens to every session and 1,624 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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