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 MassLab-SII/open-agent-skills --skill desktop_analysisgit clone --depth 1 https://github.com/MassLab-SII/open-agent-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/masslab-sii/open-agent-skills/desktop_analysis)<a href="https://agentmods.dev/skills/masslab-sii/open-agent-skills/desktop_analysis"><img src="https://agentmods.dev/badge/skills/masslab-sii/open-agent-skills/desktop_analysis/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/masslab-sii/open-agent-skills/desktop_analysis"><img src="https://agentmods.dev/badge/skills/masslab-sii/open-agent-skills/desktop_analysis.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 | $0.00029 | $0.01585 |
| Opus 5.5 | $0.00012 | $0.00634 |
| Sonnet 5.5 | $0.00006 | $0.00317 |
| Haiku 4.5 | $0.00003 | $0.00159 |
Grade A, and why
desktop-analysis 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.
This is a copy
88% identical to advanced-file-management — 107 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 — 273 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Desktop Analysis Skill
This skill provides data analysis and reporting tools:
- Music analysis: Generate popularity reports from music data
- File statistics: Count files, folders, and calculate total size
- List all files: Recursively list all files under a directory
Important Notes
- Do not use other bash commands: Do not attempt to use general bash commands or shell operations like cat, ls.
- Use relative paths: Use paths relative to the working directory (e.g.,
./folder/file.txtorfolder/file.txt).
I. Skills
1. Music Analysis Report
Analyzes music data from multiple artists, calculates popularity scores using a weighted formula, and generates a detailed analysis report.
Features
- Reads song data from multiple artist directories
- Supports CSV and TXT file formats
- Calculates popularity scores using configurable weights:
popularity_score = (rating × W1) + (play_count_normalized × W2) + (year_factor × W3)- Default weights: W1=0.4, W2=0.4, W3=0.2
- Sorts songs by popularity
Parameters
| Parameter | Default | Description |
|---|---|---|
--output |
music_analysis_report.txt |
Output report filename |
--rating-weight |
0.4 |
Weight for rating score |
--play-count-weight |
0.4 |
Weight for normalized play count |
--year-weight |
0.2 |
Weight for year factor |
Example
# Generate music analysis report with default weights (0.4, 0.4, 0.2)
python music_report.py ./music
# Use a custom output filename
python music_report.py ./music --output my_report.txt
# Use custom weights for the popularity formula
python music_report.py ./music --rating-weight 0.5 --play-count-weight 0.3 --year-weight 0.2
2. File Statistics
Generate file statistics for a directory: total files, folders, and size.
Features
- Count total files (excluding .DS_Store)
- Count total folders
- Calculate total size in bytes (includes .DS_Store for size only)
What ships with it
4 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 · 273 lines · 29 tokens per session scan A 43e2adc2b23a
desktop-analysis is a skill published in the GitHub repository MassLab-SII/open-agent-skills (134 stars, last pushed 10mo ago), licensed Apache-2.0. It adds 29 tokens to every session and 1,585 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 88% identical to advanced-file-management, differing in 107 lines, and is treated as a copy.
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