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 ssmurfgg04-gif/context-m --skill xlsxgit clone --depth 1 https://github.com/ssmurfgg04-gif/context-mWrote 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/ssmurfgg04-gif/context-m/xlsx)<a href="https://agentmods.dev/skills/ssmurfgg04-gif/context-m/xlsx"><img src="https://agentmods.dev/badge/skills/ssmurfgg04-gif/context-m/xlsx/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/ssmurfgg04-gif/context-m/xlsx"><img src="https://agentmods.dev/badge/skills/ssmurfgg04-gif/context-m/xlsx.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 | $0.00152 | $0.02441 |
| Opus 5 | $0.00076 | $0.01221 |
| Sonnet 5 | $0.00030 | $0.00488 |
| Haiku 4.5 | $0.00015 | $0.00244 |
Grade A, and why
xlsx scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 231 lines — stays where its author put it; the contents beside it link to each section on GitHub.
XLSX — Scene-Driven Spreadsheet Workbench
Quick Setup
bash "$XLSX_SKILL_DIR/setup.sh" # Interactive environment check + install
Pre-Flight: Intent Gate
Before touching any code, confirm the user actually needs a spreadsheet:
- Report / analysis summary (述职, 调研报告) → docx skill
- Presentation (汇报, 演示, pitch deck) → pptx skill
- Formal print document (合同, 证书, "PDF") → pdf skill
- Charts only, no data table needed → charts skill
- User explicitly says a format → respect it
If confirmed xlsx → proceed to Scene Router below.
Request Decomposition (do this every time):
- Explicit needs: sheets, columns, formulas, metrics the user stated
- Implicit needs: business context, downstream use (filter? sort? input?)
- Multi-part requests: generate ALL parts — never silently drop a component
Multi-Intent Detection — some requests combine multiple scenes:
"Create a financial model with charts and export a PDF summary"
→ scenes/finance.md + engines/chart.md + (hand off PDF to pdf skill)
"Analyze this CSV, build a dashboard, and make it look professional"
→ scenes/analyze.md + engines/chart.md + engines/design.md
"Edit this budget file, add a new quarter column, and create a pivot"
→ scenes/edit.md + quality/pipeline.md (pivot command)
"Convert these 5 CSVs into one xlsx with a summary sheet"
→ scenes/convert.md + scenes/create.md (for summary)
When multiple intents detected, load all matching files and execute in logical order: data preparation → analysis → visualization → styling → QA.
Complexity Gate (evaluate BEFORE Scene Router)
Determine task complexity to control file loading depth:
User Request
│
├─ LITE (single aggregation, simple chart, direct conversion, QA-only)
│ → Load: SKILL.md + ONE scene file (lean version)
│ → Skip: engine files (use built-in knowledge for basic styles)
│ → QA: audit + validate only
│ → Target: ≤ 400 lines total context
│
└─ FULL (multi-dimensional analysis, financial model, dashboard, KANO, etc.)
→ Load: SKILL.md + scene + engines (chart.md / design.md) as needed
→ For code patterns: load recipes/templates files ON DEMAND (not upfront)
→ QA: full pipeline (recalc → audit → scan → chart-verify → validate)
→ Target: load recipes/templates only when stuck on implementation
What ships with it
20 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.
- engines/chart-templates.md 4.7 KB
- engines/chart.md 3.2 KB
- engines/design.md 20 KB
- engines/vba-templates.md 13 KB
- LICENSE.txt 745 B
- quality/pipeline.md 9.1 KB
- scenes/advanced.md 6.6 KB
- scenes/analyze-recipes.md 6.3 KB
- scenes/analyze.md 3.6 KB
- scenes/convert.md 3.8 KB
- scenes/create.md 3.0 KB
- scenes/edit-patterns.md 7.0 KB
- scenes/edit.md 6.7 KB
- scenes/finance_lite.md 5.1 KB
- scenes/finance.md 9.4 KB
- scenes/vba.md 7.7 KB
- setup.sh 4.4 KB runs code
- templates/base.py 21 KB runs code
- templates/palettes.py 16 KB runs code
- xlsx.py 51 KB runs code
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 · 231 lines · 152 tokens per session scan A 97cd35e37f42
xlsx is a skill published in the GitHub repository ssmurfgg04-gif/context-m (2 stars, last pushed yesterday), licensed Apache-2.0. It adds 152 tokens to every session and 2,441 once invoked, about $0.0008 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 2026-09-03.
Other skills, from other repositories
pptx
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excel-author
Create and edit Excel (.xlsx) workbooks with openpyxl. Supports formulas, charts, formatting, and data analysis.
liteparse
Parse documents into LLM-ready content entirely on the local machine — PDF / DOCX / XLSX / PPTX / images → Markdown, structured JSON (with bounding boxes), or page screenshots, via the lit CLI. No cloud, no LLM, works offline. Use whenever the user attaches or points to a document that must be read before reasoning…
data-analysis
Analyze Excel/CSV files with DuckDB SQL via bash.
google-workspace
Manage Gmail, Google Calendar, Drive, Sheets, Docs, Contacts, and Tasks via the gogcli CLI. TRIGGER: "gmail", "google calendar", "google drive", "google sheets". NOT FOR: outlook-assistant, xlsx, docx use cases.
xlsx
Spreadsheet creation, editing, and analysis (.xlsx, .xlsm, .csv, .tsv). TRIGGER: any request involving spreadsheets, Excel files, CSV data, formulas, or data visualization. DO NOT USE: for plain text tables (just use markdown).