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.svg" alt="Measured on agentmods" 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 | $0.00005 | $0.00272 |
| Sonnet 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 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.
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.
- 3d 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 4mo ago), licensed MIT. It adds 10 tokens to every session and 544 once invoked, about $0.0001 per session on Opus 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
doc-audit
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list
Inspect available Tink harnesses and recent usage signals.
init
Bootstrap .claude/ configuration for another project — interactive wizard covering bundles, skills, hooks, CLAUDE.md, and .ck.json. Also: --show to print current config, --reset to wipe it.
constraint-modeler
Model world constraints with assumption validation, dependency mapping, and scenario boundary definition.
clean-branches
Command "clean-branches" from qdhenry/Claude-Command-Suite, covering clean branches command, instructions, clean up all merged branches except protected ones, interactive cleanup with confirmation and batch delete remote branches.
pac-validate
Validate Product as Code project structure and files for specification compliance.