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 ahacker-1/cre-agent-skills --skill cre-document-ingestiongit clone --depth 1 https://github.com/ahacker-1/cre-agent-skillsWrote 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/ahacker-1/cre-agent-skills/cre-document-ingestion)<a href="https://agentmods.dev/skills/ahacker-1/cre-agent-skills/cre-document-ingestion"><img src="https://agentmods.dev/badge/skills/ahacker-1/cre-agent-skills/cre-document-ingestion/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/ahacker-1/cre-agent-skills/cre-document-ingestion"><img src="https://agentmods.dev/badge/skills/ahacker-1/cre-agent-skills/cre-document-ingestion.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 | $0.00040 | $0.00971 |
| Opus 5 | $0.00020 | $0.00485 |
| Sonnet 5 | $0.00008 | $0.00194 |
| Haiku 4.5 | $0.00004 | $0.00097 |
Grade A, and why
cre-document-ingestion 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 7d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- cre-document-ingestion — 100% identical, 17 lines differ
How it starts
The opening of the file, as written. The whole thing — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CRE Document Ingestion Suite
You have access to 4 specialist document processing skills for commercial real estate deal packages.
Available Skills
| Skill | File | Use When |
|---|---|---|
| Document Classifier | skills/document-classifier.md |
User provides one or more deal documents and needs them identified by type (rent roll, T-12, offering memo, lease, survey, etc.) |
| Rent Roll Parser | skills/rent-roll-parser.md |
User provides a rent roll file and needs structured data extracted — unit numbers, tenants, lease dates, rents, deposits, status |
| Financials Parser | skills/financials-parser.md |
User provides a T-12 or operating statement and needs structured extraction — income lines, expense categories, monthly trends |
| Offering Memo Parser | skills/offering-memo-parser.md |
User provides an offering memorandum and needs key data extracted — property details, financial projections, market data, investment highlights |
How to Use
- If the user provides documents without specifying what they are, start with the Document Classifier
- Once document types are identified, load the appropriate parser skill
- Follow the Strategy steps in the loaded skill exactly
- Produce structured output in the format specified by the skill
- Run Quality Checks before delivering results
Recommended workflow for a full deal package:
Read skills/document-classifier.md→ classify all documents- For each rent roll:
Read skills/rent-roll-parser.md→ extract - For each T-12/financial:
Read skills/financials-parser.md→ extract - For each offering memo:
Read skills/offering-memo-parser.md→ extract
If the user says "$ARGUMENTS", use that to determine which skill to load.
Quick Reference
Document Classifier — Identifies: rent rolls, T-12/T-3 operating statements, offering memoranda, leases, title commitments, surveys, Phase I ESAs, appraisals, insurance certificates, tax returns, entity documents. Outputs: document type, confidence level, extractable data fields.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago Changed · +17 lines 800664bea40c
- 11d ago First seen · 45 lines · 40 tokens per session scan A 537061f6e167
cre-document-ingestion is a skill published in the GitHub repository ahacker-1/cre-agent-skills (92 stars, last pushed 9d ago), licensed Apache-2.0. It adds 40 tokens to every session and 971 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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cre-document-ingestion
CRE Document Ingestion suite — 4 specialist skills for classifying and extracting structured data from deal documents including rent rolls, T-12 financials, and offering memoranda.
cre-industrial
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cre-financing
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