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 SpillwaveSolutions/automating-mac-apps-plugin --skill automating-wordgit clone --depth 1 https://github.com/SpillwaveSolutions/automating-mac-apps-pluginWrote 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/spillwavesolutions/automating-mac-apps-plugin/automating-word)<a href="https://agentmods.dev/skills/spillwavesolutions/automating-mac-apps-plugin/automating-word"><img src="https://agentmods.dev/badge/skills/spillwavesolutions/automating-mac-apps-plugin/automating-word/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/spillwavesolutions/automating-mac-apps-plugin/automating-word"><img src="https://agentmods.dev/badge/skills/spillwavesolutions/automating-mac-apps-plugin/automating-word.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 | $0.00067 | $0.00865 |
| Opus 5.5 | $0.00027 | $0.00346 |
| Sonnet 5.5 | $0.00013 | $0.00173 |
| Haiku 4.5 | $0.00007 | $0.00086 |
Grade A, and why
automating-word 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 1mo 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Automating Word (JXA-first, AppleScript discovery)
Relationship to the macOS automation skill
- Standalone for Word, aligned with
automating-mac-appspatterns. - Use
automating-mac-appsskill for permissions, shell execution, and UI scripting guidance. - PyXA Installation: To use PyXA examples in this skill, see the installation instructions in
automating-mac-appsskill (PyXA Installation section).
Core framing
- Word dictionary is AppleScript-first; discover there.
- JXA provides logic, data handling, and ObjC bridge access.
- Objects are specifiers; read via methods, write via assignments.
- Handle errors from Word operations using try/catch blocks and Application error checking.
Implementation Workflow
- Discover AppleScript Dictionary: Open Script Editor, browse Word's AppleScript dictionary to understand available objects and methods.
- Translate to JXA: Use discovered AppleScript syntax as reference for JXA equivalents, consulting the dictionary translation table.
- Set Up JXA Script: Initialize Word application object and document references.
- Implement Operations: Apply find/replace, table manipulation, or export using JXA methods.
- Test and Validate: Run script and verify document changes match expectations.
Quick Examples
Document opening:
// JXA
const word = Application('Microsoft Word');
word.documents.open('/path/to/document.docx');
# PyXA (Recommended)
import PyXA
word = PyXA.Word()
word.documents().open("/path/to/document.docx")
Find and replace:
// JXA
const range = word.activeDocument.content;
range.find.text = 'old text';
range.find.replacement.text = 'new text';
range.find.execute({replace: 'all'});
# PyXA
doc = word.active_document()
find_obj = doc.content().find()
find_obj.text = 'old text'
find_obj.replacement.text = 'new text'
find_obj.execute(replace='all')
Table creation:
// JXA
const table = word.activeDocument.tables.add(word.activeDocument.content, 3, 4);
table.cell(1, 1).range.text = 'Header';
# PyXA
table = doc.tables().add(doc.content(), 3, 4)
table.cell(1, 1).range().text = 'Header'
What ships with it
5 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.
- 1mo ago First seen · 95 lines · 67 tokens per session scan A 0a870da5e776
automating-word is a skill published in the GitHub repository SpillwaveSolutions/automating-mac-apps-plugin (46 stars, last pushed 2mo ago), licensed MIT. It adds 67 tokens to every session and 865 once invoked, about $0.0003 per session on Opus 5.5. 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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