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/h4vzz/awesome-ai-agent-skillsnpx agentmods add skills/h4vzz/awesome-ai-agent-skills/file-organizationWrote 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/h4vzz/awesome-ai-agent-skills/file-organization)<a href="https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/file-organization"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/file-organization/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/h4vzz/awesome-ai-agent-skills/file-organization"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/file-organization.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 | $0.00042 | $0.02307 |
| Opus 5 | $0.00021 | $0.01154 |
| Sonnet 5 | $0.00008 | $0.00461 |
| Haiku 4.5 | $0.00004 | $0.00231 |
Grade A, and why
File Organization 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 12d 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 file-organization — 4 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 — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
File Organization
This skill enables an AI agent to bring order to cluttered directories. Given a target path, the agent scans all files, classifies them using a configurable rule set, and moves them into a well-structured directory tree. It supports organization by file type, modification date, project association, or priority level. Advanced features include duplicate detection via content hashing, consistent naming conventions, dry-run previews, and automated archival of stale files.
Workflow
-
Scan the Target Directory Recursively enumerate all files in the specified directory. Collect metadata for each file: name, extension, size, creation date, modification date, and content hash (SHA-256, computed lazily for duplicate detection). Skip hidden files and system files (e.g.,
.DS_Store,Thumbs.db) by default, but allow the user to include them via configuration. -
Classify Files by Rule Set Apply the active organization strategy to assign each file to a destination folder. The default strategy groups by file type using a built-in extension map (e.g.,
.pdf→documents/,.png→images/,.mp3→audio/). Alternative strategies include: group by modification date (2025/01/,2025/02/), group by project name inferred from path prefixes or filename tags, or group by a priority label embedded in the filename (e.g.,URGENT-report.pdf→priority-high/). Users can supply a custom rule file in YAML or JSON to override or extend any strategy. -
Detect and Handle Duplicates Compare content hashes across all scanned files. When duplicates are found, keep the most recently modified copy in the target location and move older copies to a
_duplicates/staging folder. Present a summary of duplicates to the user for review before permanent deletion. Optionally, replace duplicates with symbolic links to the canonical copy to save disk space while preserving path references. -
Apply Naming Conventions Normalize filenames according to the configured convention. Options include: kebab-case (
quarterly-report-2025.pdf), snake_case (quarterly_report_2025.pdf), or date-prefixed (2025-01-15_quarterly-report.pdf). Strip special characters, collapse whitespace, and transliterate Unicode to ASCII when requested. Preserve original extensions. Log every rename so the operation is reversible.
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.
- 12d ago First seen · 192 lines · 42 tokens per session scan A 6661e1aebf56
File Organization is a skill published in the GitHub repository h4vzz/awesome-ai-agent-skills (34 stars, last pushed today), licensed MIT. It adds 42 tokens to every session and 2,307 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to file-organization, differing in 4 lines, and is treated as a copy.
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