Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add yipng05-max/-skills --skill text-to-excelgit clone --depth 1 https://github.com/yipng05-max/-skillsWrote 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/yipng05-max/-skills/text-to-excel)<a href="https://agentmods.dev/skills/yipng05-max/-skills/text-to-excel"><img src="https://agentmods.dev/badge/skills/yipng05-max/-skills/text-to-excel/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/yipng05-max/-skills/text-to-excel"><img src="https://agentmods.dev/badge/skills/yipng05-max/-skills/text-to-excel.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00224 | $0.01083 |
| Opus 5 | $0.00112 | $0.00541 |
| Sonnet 5 | $0.00045 | $0.00217 |
| Haiku 4.5 | $0.00022 | $0.00108 |
Grade A, and why
text-to-excel 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Text-to-Excel
将结构化文本转换为格式规整的Excel文件。
Workflow
Step 1: Parse User Input
Analyze user-provided structured text. Supported input formats:
- Markdown tables (
| col1 | col2 |) - Plaintext tables (tab/space-separated)
- CSV/TSV data
- Lists (bulleted, numbered)
- Key-value pairs
- JSON/dict-like structures
- Free-form text with identifiable tabular structure
- Descriptions of desired table content
Identify: headers, data rows, data types per column, and any special formatting requests.
Step 2: Ask for Output Path
Ask the user for the save path. Suggest a reasonable default filename based on the content (e.g. ~/Desktop/sales_data.xlsx).
Step 3: Build JSON Config
Construct a JSON config object following the schema in references/config_schema.md.
Minimal example:
{
"sheets": [{
"name": "Sheet1",
"headers": ["Name", "Age", "City"],
"data": [
["Alice", 30, "Beijing"],
["Bob", 25, "Shanghai"]
]
}]
}
Full-featured example with styles, charts, and validation:
{
"sheets": [{
"name": "Sales",
"title_row": {"text": "Q1 Sales Report", "style": {"font": {"bold": true, "size": 16}}},
"headers": ["Month", "Revenue", "Growth"],
"data": [
["January", 50000, 0.12],
["February", 62000, 0.24],
["March", 58000, 0.16]
],
"column_types": ["text", "currency_cny", "percent"],
"freeze_pane": "A2",
"auto_filter": true,
"charts": [{
"type": "bar",
"title": "Monthly Revenue",
"x_column": 1,
"y_columns": [2],
"position": "E2"
}],
"conditional_formats": [{
"type": "color_scale",
"range": "B2:B4"
}]
}]
}
Step 4: Generate Excel
- Write the JSON config to a temp file
- Run:
python3 scripts/generate_excel.py <config.json> <output_path> - Verify the file was created
- Report success with the file path
Style Defaults
The script applies professional defaults automatically:
- Headers: Blue background (#4472C4), white bold text, centered
- Data rows: Alternating gray/white bands, thin borders
- Column widths: Auto-calculated based on content length
- Title row (optional): Large bold text, merged across all columns
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 · 108 lines · 224 tokens per session scan A f55338121fa8
text-to-excel is a skill published in the GitHub repository yipng05-max/-skills (285 stars, last pushed 4mo ago), licensed MIT. It adds 224 tokens to every session and 1,083 once invoked, about $0.0011 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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