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/julianobarbosa/claude-code-skillsnpx agentmods add skills/julianobarbosa/claude-code-skills/file-intelWrote 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/julianobarbosa/claude-code-skills/file-intel)<a href="https://agentmods.dev/skills/julianobarbosa/claude-code-skills/file-intel"><img src="https://agentmods.dev/badge/skills/julianobarbosa/claude-code-skills/file-intel/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/julianobarbosa/claude-code-skills/file-intel"><img src="https://agentmods.dev/badge/skills/julianobarbosa/claude-code-skills/file-intel.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 | $0.00076 | $0.00803 |
| Opus 5 | $0.00038 | $0.00402 |
| Sonnet 5 | $0.00015 | $0.00161 |
| Haiku 4.5 | $0.00008 | $0.00080 |
Grade A, and why
file-intel 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 8d 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
File Intel — Gemini File Processor
Runs scripts/process_files_with_gemini.py on a folder of files and produces Obsidian-ready summaries.
Step 1: Get the folder
Use AskUserQuestion:
Question: "Which folder should I process?"
Options:
1. "This vault's inbox/" — process the inbox folder
2. "Custom path" — user specifies a folder
If the user selects option 2, they'll type the path in the "Other" input.
Step 2: Run the script
Run via Bash from the vault root:
python scripts/process_files_with_gemini.py <folder_path>
- If inbox/:
python scripts/process_files_with_gemini.py inbox/ - If custom path: pass it as the argument
Show the terminal output as it runs so the user can see files being processed live.
Step 3: Open the output
After the script completes, open the output folder:
open "outputs/file_summaries/YYYY-MM-DD/"
Replace YYYY-MM-DD with today's date from the script output.
Step 4: Report back
Tell the user:
- How many files were processed
- Where the summaries landed
- Point them to
MASTER_SUMMARY.mdas the single-file digest of everything - Suggest: "Open Claude Code and say: Sort everything in inbox/ into the right folders"
Notes
- Supported formats: PDF, PPTX, XLSX, DOCX, CSV, JSON, XML, MD, TXT, PY, JS, HTML, CSS
- Output:
outputs/file_summaries/YYYY-MM-DD/ - Each file gets its own
*_summary.md MASTER_SUMMARY.mdcombines all summaries into one digest- Summaries are context-aware: deliverables (invoices, reports) vs reference files (code, config) get different formats
Gotchas
- Encoding detection is best-effort, not deterministic: Files saved as Windows-1252 or Latin-1 may be processed as garbled UTF-8 instead of failing loudly. Spot-check the first summary of any batch from unknown sources — if accented characters render as mojibake, the source encoding was misdetected.
- Password-protected and encrypted PDFs return blank summaries: Gemini cannot extract text from locked PDFs but the script does not flag them as failures. Check the file size of each
*_summary.md— anything under ~200 bytes is suspect. - Scanned-image PDFs depend on OCR confidence: Low-DPI scans, handwriting, or rotated pages produce summaries with hallucinated content rather than honest "could not read." Verify scanned documents against the original before trusting downstream decisions.
- XLSX files with multiple sheets summarize only the active sheet: The processor reads what the workbook opens to by default; other sheets are skipped silently. For multi-sheet financials, split into separate files or expect partial coverage.
- MASTER_SUMMARY.md grows linearly and exceeds context on large folders: A 200-file inbox produces a digest too large to feed back into another LLM call without truncation. For batches over ~50 files, work from the per-file summaries instead of the master.
- Re-running on the same folder writes to a new
YYYY-MM-DD/subdirectory: Two runs on the same day overwrite each other; runs on different days produce duplicates without cross-reference. Clear or archive prior output before re-processing.
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.
- 8d ago First seen · 72 lines · 76 tokens per session scan A 57495862e16c
file-intel is a skill published in the GitHub repository julianobarbosa/claude-code-skills (10 stars, last pushed 13d ago), licensed MIT. It adds 76 tokens to every session and 803 once invoked, about $0.0004 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 2026-08-31.
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