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 leecyno1/boutique-skills --skill anthropic-fs-financial-analysis-clean-data-xlsgit clone --depth 1 https://github.com/leecyno1/boutique-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/leecyno1/boutique-skills/anthropic-fs-financial-analysis-clean-data-xls)<a href="https://agentmods.dev/skills/leecyno1/boutique-skills/anthropic-fs-financial-analysis-clean-data-xls"><img src="https://agentmods.dev/badge/skills/leecyno1/boutique-skills/anthropic-fs-financial-analysis-clean-data-xls/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/leecyno1/boutique-skills/anthropic-fs-financial-analysis-clean-data-xls"><img src="https://agentmods.dev/badge/skills/leecyno1/boutique-skills/anthropic-fs-financial-analysis-clean-data-xls.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 | $0.00091 | $0.00709 |
| Opus 5 | $0.00046 | $0.00354 |
| Sonnet 5 | $0.00018 | $0.00142 |
| Haiku 4.5 | $0.00009 | $0.00071 |
Grade A, and why
clean-data-xls 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 9d 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.
This is a copy
100% identical to clean-data-xls — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Clean Data
Clean messy data in the active sheet or a specified range.
Environment
- If running inside Excel (Office Add-in / Office JS): Use Office JS directly (
Excel.run(async (context) => {...})). Read viarange.values, write helper-column formulas viarange.formulas = [["=TRIM(A2)"]]. The in-place vs helper-column decision still applies. - If operating on a standalone .xlsx file: Use Python/openpyxl.
Workflow
Step 1: Scope
- If a range is given (e.g.
A1:F200), use it - Otherwise use the full used range of the active sheet
- Profile each column: detect its dominant type (text / number / date) and identify outliers
Step 2: Detect issues
| Issue | What to look for |
|---|---|
| Whitespace | leading/trailing spaces, double spaces |
| Casing | inconsistent casing in categorical columns (usa / USA / Usa) |
| Number-as-text | numeric values stored as text; stray $, ,, % in number cells |
| Dates | mixed formats in the same column (3/8/26, 2026-03-08, March 8 2026) |
| Duplicates | exact-duplicate rows and near-duplicates (case/whitespace differences) |
| Blanks | empty cells in otherwise-populated columns |
| Mixed types | a column that's 98% numbers but has 3 text entries |
| Encoding | mojibake (é, ’), non-printing characters |
| Errors | #REF!, #N/A, #VALUE!, #DIV/0! |
Step 3: Propose fixes
Show a summary table before changing anything:
| Column | Issue | Count | Proposed Fix |
|---|
Step 4: Apply
- Prefer formulas over hardcoded cleaned values — where the cleaned output can be expressed as a formula (e.g.
=TRIM(A2),=VALUE(SUBSTITUTE(B2,"$","")),=UPPER(C2),=DATEVALUE(D2)), write the formula in an adjacent helper column rather than computing the result in Python and overwriting the original. This keeps the transformation transparent and auditable. - Only overwrite in place with computed values when the user explicitly asks for it, or when no sensible formula equivalent exists (e.g. encoding/mojibake repair)
- For destructive operations (removing duplicates, filling blanks, overwriting originals), confirm with the user first
- After each category of fix (whitespace → casing → number conversion → dates → dedup), show the user a sample of what changed and get confirmation before moving to the next category
- Report a before/after summary of what changed
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.
- 9d ago First seen · 51 lines · 91 tokens per session scan A f0dfceb01532
clean-data-xls is a skill published in the GitHub repository leecyno1/boutique-skills (5 stars, last pushed today), licensed MIT. It adds 91 tokens to every session and 709 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to clean-data-xls, differing in 0 lines, and is treated as a copy.
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