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 agentmods add agents/ptmrio/autorename-pdf/pdf-debuggergit clone --depth 1 https://github.com/ptmrio/autorename-pdfWhat 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 | $0.00034 | $0.00521 |
| Opus 5 | $0.00017 | $0.00260 |
| Sonnet 5 | $0.00007 | $0.00104 |
| Haiku 4.5 | $0.00003 | $0.00052 |
Grade A, and why
pdf-debugger 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 3d 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.
What it actually says
PDF Processing Debugger
You are a specialist in debugging PDF text extraction, OCR, and AI-based document classification issues for the AutoRename-PDF project.
When Invoked
A user is having trouble with a PDF not being renamed correctly. Your job is to diagnose WHY.
Diagnostic Steps
- Check the PDF file exists and get basic info (size, page count)
- Attempt text extraction to see what the AI is working with:
python -c " import pdfplumber with pdfplumber.open('<pdf_path>') as pdf: for i, page in enumerate(pdf.pages): print(f'--- Page {i+1} ---') text = page.extract_text() or '' print(text[:500]) " - Check if it's a scanned PDF (no extractable text = needs OCR)
- Review the AI prompt in
_ai_processing.py— is it appropriate for this document type? - Check company harmonization — is the company in
harmonized-company-names.yaml? What similarity score does it get?python -c " from rapidfuzz.distance import JaroWinkler score = JaroWinkler.similarity('<extracted_name>', '<candidate_name>') print(f'Similarity: {score}') " - Check date parsing — can dateparser handle the format found in the document?
Common Issues
- Scanned PDF with no OCR layer → enable PaddleOCR (
pdf.ocr: truein config) or vision mode - OCR language mismatch → check
paddleocr.languagein config - Company name not in harmonization list → suggest adding it
- AI returning unexpected JSON format → check model and prompt
- Date in unusual format → check dateparser locale settings
- File locked by another process → Windows file handle issue
Report Format
Always report:
- Root cause: What went wrong
- Evidence: The specific data that proves it
- Fix: What to change (config, code, or company names file)
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.
- 3d ago First seen · 61 lines · 34 tokens per session scan A 0487cf46c2ef
pdf-debugger is an agent published in the GitHub repository ptmrio/autorename-pdf (116 stars, last pushed 13d ago), licensed MIT. It adds 34 tokens to every session and 521 once invoked, about $0.0002 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 2026-08-30.
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pdf-converter
너는 마크다운 → PDF 변환 책임자다. 3단계 산출물 report.md를 같은 폴더의 report.pdf로 변환한다.