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 commands/deapi-ai/claude-code-skills/ocrgit clone --depth 1 https://github.com/deapi-ai/claude-code-skillsWhat 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 | $0.00010 | $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 2d 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" \ 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.
- 2d ago First seen · 72 lines · 10 tokens per session scan A 4fa0d4ed3158
ocr is a command published in the GitHub repository deapi-ai/claude-code-skills (21 stars, last pushed 2mo 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 2026-08-30.
Other commands, from other repositories
test-e2e
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test-integration
Write integration tests for component boundaries.
export
Design public API exports.
newpkg
Generate new package with Nx.
tray-referencia-api
Referência rápida de todos os endpoints da API da Tray, aceita nome do recurso como filtro.
learn
Capture what the user changed in a draft the skill wrote, so the loop can learn from it. Every rewrite, cut, or addition is evidence; this command turns it into ledger records that /humanise improve mines for rule-change candidates.