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.
git clone --depth 1 https://github.com/eric861129/SKILLS_All-in-oneWrote 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/commands/eric861129/skills_all-in-one/ocr)<a href="https://agentmods.dev/commands/eric861129/skills_all-in-one/ocr"><img src="https://agentmods.dev/badge/commands/eric861129/skills_all-in-one/ocr/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/commands/eric861129/skills_all-in-one/ocr"><img src="https://agentmods.dev/badge/commands/eric861129/skills_all-in-one/ocr.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 | $0.00010 | $0.00544 |
| Opus 5.5 | $0.00004 | $0.00218 |
| Sonnet 5.5 | $0.00002 | $0.00109 |
| Haiku 4.5 | $0.00001 | $0.00054 |
Grade A, and why
ocr scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s -X POST "https://api.deapi.ai/api/v1/client/img2txt" \ Copies of this mod
1 near-identical copy found in the catalogue:
- ocr — 100% identical, 0 lines differ
What it actually says
OCR (Image to Text) via deAPI
Extract text from image: $ARGUMENTS
Step 1: Validate input
Verify $ARGUMENTS is a valid image file path or URL:
- Supported formats:
.png,.jpg,.jpeg,.webp,.gif,.bmp - If URL provided, download the file first
Step 2: Send request
Note: This endpoint requires multipart/form-data with file upload.
curl -s -X POST "https://api.deapi.ai/api/v1/client/img2txt" \
-H "Authorization: Bearer $DEAPI_API_KEY" \
-F "image=@{local_file_path}" \
-F "model=Nanonets_Ocr_S_F16"
If user provides a URL, first download the image:
curl -s -o /tmp/ocr_image.png "{image_url}"
Then use /tmp/ocr_image.png as the file path.
Step 3: Poll status (feedback loop)
Extract request_id from response, then poll every 10 seconds:
curl -s "https://api.deapi.ai/api/v1/client/request-status/{request_id}" \
-H "Authorization: Bearer $DEAPI_API_KEY"
Status handling:
processing→ wait 10s, poll againdone→ proceed to Step 4failed→ report error message to user, STOP
Step 4: Fetch and present result
When status = "done":
- Get extracted text from response or
result_url - Present text in clean, formatted manner
- Preserve original structure (paragraphs, lists) where possible
Step 5: Offer follow-up
Ask user:
- "Would you like me to summarize this text?"
- "Should I translate this to another language?"
- "Would you like to extract text from another image?"
Error handling
| Error | Action |
|---|---|
| 401 Unauthorized | Check if $DEAPI_API_KEY is set correctly |
| 429 Rate Limited | Wait 60s and retry |
| 500 Server Error | Wait 30s and retry once |
| Invalid URL | Ask user to verify the image URL |
| No text found | Inform user the image may not contain readable text |
| Image too large | Suggest resizing or cropping the image |
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 · 72 lines · 10 tokens per session scan A 4fa0d4ed3158
ocr is a command published in the GitHub repository eric861129/SKILLS_All-in-one (52 stars, last pushed 6mo ago), licensed MIT. It adds 10 tokens to every session and 544 once invoked, about $0.0000 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other commands, from other repositories
report
Generer des rapports QA Recette a partir des donnees de session.
bib-filter
Filter a LaTeX project's bibliography to the entries actually cited, with Overleaf-source resolution, temporary staging, validation, approval-gated canonical replacement, and compilation. Use before submission or after large revisions.
latex-polish
Inspect a cleanly compiling LaTeX document for source pathologies and rendered visual defects by linting and viewing selected PDF pages. Use when compilation succeeds but title pages, floats, tables, figures, or layout still need publication-quality review. Not for basic compilation health; use $latex-health-check.
split-pdf
Download, split, and deeply read an academic PDF that is not available through Paperpile. Use when a long external PDF needs page-wise ingestion. For Paperpile items, use the Paperpile text-extraction route instead.
telegram-to-notebook
A command that imports a Telegram chat export into NotebookLM, Google's tool for asking questions about uploaded sources. It can organize forum chats by discussion topic and keep text, code, PDFs, and documents while filtering media such as stickers and GIFs.
create-doc
Generate a professional Word document (.docx) using Python and python-docx.