Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/gabrielmoreira/agent-skills-mirrornpx agentmods add skills/gabrielmoreira/agent-skills-mirror/docxWrote 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/gabrielmoreira/agent-skills-mirror/docx)<a href="https://agentmods.dev/skills/gabrielmoreira/agent-skills-mirror/docx"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/docx/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/gabrielmoreira/agent-skills-mirror/docx"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/docx.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 577 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- high Prompt Injection · line 658 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00168 | $0.07944 |
| Opus 5 | $0.00084 | $0.03972 |
| Sonnet 5 | $0.00034 | $0.01589 |
| Haiku 4.5 | $0.00017 | $0.00794 |
Grade A, and why
docx 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 — 750 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DOCX creation, editing, and analysis
Overview
A .docx file is a ZIP archive containing XML files.
Generating Reports (Recommended for research reports)
For structured reports with cover page, TOC, tables, images, and references, use the pre-built template. The DOCX generator accepts the same JSON schema as the PDF generator, so one report_data.json can produce both formats.
Step 1: Copy the generator script
cp /builtin-skills/docx/scripts/generate_report.js ./generate_report.js
Step 2: Build report_data.json
Two phases: write section text files, then assemble into JSON.
Phase 1 — Write each section as a plain text file using write_file:
For each major section, read_file the relevant research data, then write_file the section content directly:
read_file("research_data/literature.md") # refresh data in context
write_file("sections/sec_01_intro.txt", "...") # write section content
write_file("sections/sec_02_mutations.txt", "...") # next section
...
Each section file should be 1,000-2,000+ words with specific data, citations, and analysis.
NEVER write a Python script that contains section text as string literals. The section content goes directly into .txt files via write_file, not into Python code. Do NOT write scripts named "generate_sections", "create_content", "build_report" etc. that embed text in Python strings. If a sandbox script fails twice, switch to direct write_file calls.
Writing style — academic research report (CRITICAL):
- Write continuous flowing prose. Each paragraph: 8–10 sentences following the pattern: topic sentence → supporting evidence with specific data → analysis/comparison → transition to next point.
- Use in-text citations [1], [2] when referencing data. These render as blue superscript in the DOCX. Do NOT add a "References" list at the end of each chapter — all references go in ONE final
referencessection. - Synthesize across sources: "Study A [1] reported X, while Study B [2] found Y, suggesting that Z."
- Use academic connectives: "Furthermore", "In contrast", "These findings indicate", "Notably", "Taken together".
- NEVER use numbered-point structure (e.g. "1. Topic Title\n\nParagraph. 2. Topic Title\n\nParagraph."). Instead, use
##subheadings for structure and prose paragraphs for content. The template'srenderTextcorrectly renders##/###as formatted subheadings. - Bullet lists: max 5% of section, only for short enumerations (e.g. 4-5 drug names).
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 · 750 lines · 168 tokens per session scan A 5cf50bdd3da4
docx is a skill published in the GitHub repository gabrielmoreira/agent-skills-mirror (17 stars, last pushed yesterday), licensed MIT. It adds 168 tokens to every session and 7,944 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
baoyu-youtube-transcript
A tool for downloading the written captions, subtitles, chapter information, speaker labels, and cover image from a YouTube video using its URL or ID.
orbit-notion
Open Orbit briefing skill — selected by the Orbit pipeline when Notion is the user's only connected connector, or when the user explicitly scopes their daily digest to Notion. Pulls the past 24 hours of document edits, comments, mentions, and database row changes from the user's authenticated Notion connection and…
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
feishu
Work with Feishu or Lark bots, docs, sheets, bitables, approval flows, and OpenAPI/MCP setup without hardcoding credentials.
read
Reads URLs and PDFs by fetching source content, defaulting to concise summaries for plain read requests and clean Markdown when asked to convert, save, quote, cite, or feed downstream work. Use when users ask in any language to read, fetch, check, summarize, quote, cite, convert, or save a URL or PDF. Not for local…
overleaf-sync
A two-way connection between a local paper folder and Overleaf, a web-based LaTeX editor for writing research papers. It lets you move changes between the local files and the shared Overleaf project.