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 agentmods add skills/yjwong/lark-cli/sheetsnpx skills add yjwong/lark-cli --skill sheetsgit clone --depth 1 https://github.com/yjwong/lark-cliWhat 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 | $0.00051 | $0.01907 |
| Opus 5 | $0.00026 | $0.00954 |
| Sonnet 5 | $0.00010 | $0.00381 |
| Haiku 4.5 | $0.00005 | $0.00191 |
Grade A, and why
sheets 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 2d 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 — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lark Sheets Skill
Read and query Lark Sheets (spreadsheets) via the lark CLI.
Tool Location
tools/bin/lark
Run from the vault root directory with required env var:
LARK_CONFIG_DIR=tools/lark/.lark tools/bin/lark sheet <command>
Commands Reference
List Sheets in a Spreadsheet
tools/bin/lark sheet list <spreadsheet_token>
Lists all sheets (tabs) within a Lark spreadsheet. Returns sheet IDs, titles, dimensions, and hidden status.
Output:
{
"spreadsheet_token": "T4mHsrFyzhXrj0tVzRslUGx8gkA",
"sheets": [
{
"sheet_id": "abc123",
"title": "Sheet1",
"index": 0,
"row_count": 100,
"column_count": 10
},
{
"sheet_id": "def456",
"title": "Sheet2",
"index": 1,
"hidden": true,
"row_count": 50,
"column_count": 5
}
],
"count": 2
}
Fields:
sheet_id: The unique ID of the sheet (use this with--sheetflag)title: Display name of the sheetindex: Position of the sheet (0-indexed)hidden: Whether the sheet is hidden in the UIrow_count/column_count: Dimensions of the sheet
Read Sheet Data
tools/bin/lark sheet read <spreadsheet_token> [--sheet <sheet_id>] [--range A1:Z100]
Reads cell values from a Lark spreadsheet.
Options:
--sheet: Sheet ID to read from (default: first sheet by index)--range: Cell range to read (e.g.,A1:Z100). Default: all data up to 1000 rows
Output:
{
"spreadsheet_token": "T4mHsrFyzhXrj0tVzRslUGx8gkA",
"sheet_id": "abc123",
"range": "abc123!A1:D10",
"row_count": 10,
"column_count": 4,
"values": [
["Header1", "Header2", "Header3", "Header4"],
["Value1", "Value2", 123, true],
["Row2Val1", null, 456, false]
]
}
Note: Cell values preserve their types (string, number, boolean). Empty cells may appear as null or be omitted from rows. Some cells with rich formatting may return structured objects instead of plain values.
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.
- 2d ago First seen · 246 lines · 51 tokens per session scan A 5cf9e6158118
sheets is a skill published in the GitHub repository yjwong/lark-cli (53 stars, last pushed 4mo ago), licensed MIT. It adds 51 tokens to every session and 1,907 once invoked, about $0.0003 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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