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 skills add Luohaothu/everything-codex --skill document-processinggit clone --depth 1 https://github.com/Luohaothu/everything-codexWrote 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/luohaothu/everything-codex/document-processing)<a href="https://agentmods.dev/skills/luohaothu/everything-codex/document-processing"><img src="https://agentmods.dev/badge/skills/luohaothu/everything-codex/document-processing/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/luohaothu/everything-codex/document-processing"><img src="https://agentmods.dev/badge/skills/luohaothu/everything-codex/document-processing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00021 | $0.00952 |
| Opus 5.5 | $0.00008 | $0.00381 |
| Sonnet 5.5 | $0.00004 | $0.00190 |
| Haiku 4.5 | $0.00002 | $0.00095 |
Grade A, and why
document-processing 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 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.
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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Document Processing
Use this skill when you need to turn unstructured or semi-structured documents (PDFs, scans, images, HTML, emails, DOCX) into structured outputs (JSON/CSV) with validation, provenance, and clear error handling.
First Questions (Fail Fast)
Before proposing an approach, clarify:
- Document types: invoices, contracts, medical records, policies, resumes, reports, etc.
- Input format: native PDF vs scanned images; single vs multi-file batches; average pages.
- Extraction goal: which fields, tables, entities, and relationships matter.
- Output contract: JSON schema or CSV columns; required vs optional fields; nullability rules.
- Quality bar: acceptable error rate; whether human review is required; SLA and throughput.
- Privacy constraints: PII/PHI handling, retention limits, and redaction requirements.
If any of these are unknown, state assumptions explicitly and propose options.
Output Contract (Recommended)
Prefer an explicit schema and provenance so consumers can trust the results.
Suggested JSON shape
{
"document_id": "string",
"document_type": "string",
"source": {
"filename": "string",
"page_count": 12
},
"extracted": {
"fields": {
"vendor_name": "string",
"invoice_number": "string",
"total_amount": 123.45,
"currency": "USD"
},
"tables": [
{
"name": "line_items",
"rows": [
{ "description": "string", "quantity": 1, "unit_price": 1.23, "amount": 1.23 }
]
}
]
},
"provenance": [
{
"path": "extracted.fields.total_amount",
"evidence": "snippet or normalized text",
"location": { "page": 2, "bbox": [0, 0, 0, 0] },
"confidence": 0.92
}
],
"validation": {
"status": "pass",
"errors": []
}
}
Rules:
- Never invent missing facts. Use
nullor omit optional fields, and add a validation error. - Normalize units and formats (dates, currencies, decimals) and record the normalization.
- Carry provenance (page/section reference) for high-impact fields.
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 · 113 lines · 21 tokens per session scan A 660097549141
document-processing is a skill published in the GitHub repository Luohaothu/everything-codex (25 stars, last pushed 2mo ago), licensed MIT. It adds 21 tokens to every session and 952 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-03.
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