Dataroom Prep — AI-powered M&A Dataroom Builder

Turn a messy folder of documents into a structured M&A dataroom in 10 minutes instead of 2 days — intelligent classification, deduplication, version detection, multilingual folder structure, and contextual missing-docs checklist. Installed end-to-end as a Claude Code skill.

Every M&A advisor, lawyer, notary, banker or founder doing a deal runs the same loop: collect 200-1000 messy documents from the seller, rename them all to match a clean convention, dedupe Word/PDF clones from OneDrive sync hell, organize them across 11 due diligence categories with sub-folders, then build an Excel mapping for the buyer side. 2-3 days of junior associate work, every deal.

This Claude Code skill automates the entire loop:

1. Asks every question upfront, in one single pass — source folder, destination, project name, folder/Excel language, country + sector (for the DD checklist), and whether to also run the optional enriched checklist — never split across a first pass and a follow-up
2. Extracts text from every PDF/DOCX/XLSX/PPTX, with automatic OCR fallback for scanned PDFs (PyMuPDF + Tesseract, installed by the skill itself on first run if missing — nothing to set up)
3. Classifies using parent_folder + filename + content heuristics into 11 standard DD folders × topical sub-folders
4. Detects exact duplicates via SHA-256 hashing → archives them separately
5. Detects obsolete versions by clustering filename bases (v1/v2/final/signed/OneDrive copies) → archives only the obsolete ones, keeps the most recent
6. Renames with a short, content-derived title (never the raw filename, never the folder theme) following the pattern Project Name - Short Title - yyyymmdd.ext
7. Anonymizes automatically — the target company's real name, brands, and affiliated entities never appear in filenames, the Excel, or the conversation
8. Generates an Excel report (multilingual) showing Original → Renamed for every file, with dedup status, final path, and a Top-50-missing-documents tab (offline, from your firm's own DD reference lists — see skill/data_sources/README.md)
9. Enriched checklist (optional, opt-in) — the only step that reaches the internet: scrapes the company's public website + legal sources to refine the missing-documents checklist by location + sector

Built for: M&A boutiques, investment banks, corporate lawyers, transactional notaries, accounting firms, family office advisors, solo dealmakers preparing the pre-VDR stage.

Built by: Hectelion SA — independent M&A advisory, Lausanne (Switzerland).

Installation in 3 minutes — driven by Claude Code

No copy-paste of code, no PowerShell typing. You paste one prompt into Claude Code, click "Allow" to approve each step, answer 5 config questions, done.

How it works

1. Have Claude Code installed and signed in (claude.com/claude-code).
2. Open a terminal in any folder. Run claude.
3. Copy-paste the prompt from CLAUDE_INSTALL_PROMPT.md into the conversation.
4. Send. Claude Code clones the repo, copies the skill to ~/.claude/skills/dataroom-prep/, installs Python dependencies, asks 5 config questions, writes config.yaml.
5. From then on, in any Claude Code project, type /dataroom-prep to launch the workflow and build a new dataroom.

Prerequisites

• Windows 10/11, macOS, or Linux
• Python 3.9+ on PATH (python.org)
• Claude Code installed + active Anthropic plan
• (Optional) Firecrawl MCP for the enriched checklist's website scraping step

Manual install (without Claude Code prompt)

git clone https://github.com/Hectelion-SA/claude-dataroom-prep.git ~/Github/dataroom-prep
cd ~/Github/dataroom-prep
# Windows
./install.ps1
# macOS / Linux
bash install.sh

What you get

The conversation — every question asked upfront, in one pass

No "come back later for more questions": everything below is asked together, before any file is touched, including the enriched-checklist sub-questions if you say yes to it.

The output structure — 2-level folder tree (multilingual)

Project Acme/
├── 01_General Information/
├── 02_Finance/
│   ├── Annual Financial Statements/
│   ├── Business Plan/
│   ├── Cap Table/
│   ├── Commercial Invoicing/
│   └── Funding Financials/
├── 03_Legal/
│   ├── NDA & Confidentiality Agreements/
│   ├── Patents/
│   ├── Trademarks/
│   ├── Articles of Association/
│   ├── General Meetings/
│   ├── Share Transfers/
│   ├── Term Sheets/
│   ├── Beneficial Owners/
│   ├── KBIS/
│   └── ...
├── 04_Tax/
│   └── Tax Return Package/
├── 05_IT/
│   └── IT Certifications/
├── 06_Real Estate/
├── 07_Insurance/
├── 08_HR/
├── 09_Operations/
│   ├── Commercial - Customer A/
│   ├── Commercial - Customer B/
│   ├── Product Certifications/
│   └── Marketing & Commercial/
├── 10_Processes/
├── 11_To Sort/                       ← unreadable scans, LOW confidence files
├── _98_Exact Duplicates/              ← SHA-256 hash matched
├── _99_Old Versions/                  ← obsolete versions, organized by N1/N2
│   ├── 03_Legal/
│   │   └── NDA & Confidentiality Agreements/
│   └── ...
├── _Dataprep Report.xlsx              ← see below (always generated)
└── _Liste documents à demander.xlsx   ← only if you opted into the enriched checklist

The Excel — _Dataprep Report.xlsx (5 tabs, multilingual headers, always generated)

Tab 1 — Dashboard — KPIs : docs processed, HIGH/LOW confidence split, duplicates archived, version clusters detected

Tab 2 — Mapping — 9 columns showing the Original → Renamed mapping for every file:

# | Original Name | Source Path | Renamed File | Folder L1 | Sub-folder L2 | Confidence | Dedup Status | Final Path

Colored cells: confidence (green/orange/red), dedup status (green/orange/red).

Tab 3 — Structure — Tree of the generated dataroom with doc count per N1/N2

Tab 4 — Duplicates & versions — full audit trail of what was archived and why

Tab 5 — Documents à demander (Top 50) — offline gap analysis against your firm's DD reference list for the chosen country/sector (see skill/data_sources/README.md — these lists are firm methodology and are not bundled in this public repo; without them, this tab is skipped and everything else still runs)

Optional — _Liste documents à demander.xlsx (enriched checklist)

The skill cross-references your dataroom against:

• 60+ base M&A checklist items (bilans, statuts, PV AG, top clients, IP, contentieux, RGPD, etc.)
• Location-specific obligations:
  • France: DUERP, Index égalité, Sapin II, PV CSE
  • Switzerland: AVS/LPP/SUVA, LAA, OFM permits, RC cantonal
  • Belgium: Bilan social BNB, précomptes
  • Luxembourg: RCS LU bilans, IRC/ICC
• Sector-specific compliance:
  • MedTech: marquage CE MDR (Règlement 2017/745), ISO 13485, DMR, PMS
  • SaaS: DPA, hébergement HDS, audit pentest, MRR/ARR/churn cohorts
  • Real Estate: DPE, amiante, plomb, Carnet d'entretien, Loi Carrez
  • Industrial: ICPE, études environnementales
• Website scraping (via Firecrawl) to auto-detect sector and add bespoke items

Output: 80–120 line Excel with columns:

# | Section | Document/Information requested | Criticality | Status (Present / To request) | Rationale | Source (Base / Location / Sector)

Real-world benchmark

Tested on a 4076-file project folder:

Equivalent manual work: 2-3 days of junior associate at 600 CHF/day = 1500-1800 CHF of value per dataroom.

Supported languages

Headless / scripted usage

For batch processing (CI, automation):

# 1. Extract only (produces extracted.json for content-based titling)
python ~/.claude/skills/dataroom-prep/scripts/run_pipeline.py \
  --source "/path/to/messy/folder" \
  --destination "/path/to/output" \
  --project-name "Project Acme" \
  --extract-only "/path/to/output/extracted.json"

# 2. Build, with content-derived titles/classification (decisions.json), anonymization,
#    and the offline Top-50 gap analysis for a given country/sector
python ~/.claude/skills/dataroom-prep/scripts/run_pipeline.py \
  --destination "/path/to/output" \
  --project-name "Project Acme" \
  --folder-lang en \
  --excel-lang en \
  --decisions-json "/path/to/output/decisions.json" \
  --scrub-file "/path/to/scrub_names.txt" \
  --country FR \
  --sector "Tech" \
  --make-zip

# 3. Optional — enriched checklist (website + legal sources, the only network step)
python ~/.claude/skills/dataroom-prep/scripts/enrich_checklist.py \
  --dataroom "/path/to/output/Project Acme" \
  --extracted-json "/path/to/output/extracted.json" \
  --location "France" \
  --sector "MedTech"

Scanned PDFs are OCR'd automatically (ensure_ocr_stack() installs pytesseract + Tesseract + French/English language data on first real run if missing — safe to run offline afterwards).

Privacy & Security

• 🔒 Local processing only — extraction, classification, dedup, OCR, and the Top-50 gap analysis all run 100% offline on your machine; no document content ever leaves it
• 🔒 No third-party uploads by default — the only network call in the whole pipeline is the opt-in enriched checklist (website + legal sources), and only the public URL and sector/legal queries are sent — never document content, and always confirmed with you first
• 🔒 Automatic anonymization — the target company's real name, brands, and affiliated entities are detected from the extracted content and scrubbed from every generated filename, folder, and Excel cell (case-insensitive, word-boundary matching) — on by default, never asked as a question
• 🔒 GDPR-friendly architecture — no persistent storage, files read but never sent externally
• 🔒 Audit trail — every file movement documented in _Dataprep Report.xlsx
• 🔒 Original files untouched — the skill copies, never moves or modifies source files

Roadmap

• Phase 1: extract + classify + dedup + build N1/N2 dataroom + Excel mapping
• Phase 2: contextual checklist (location + sector + website)
• V2: Légifrance API integration (FR legal references in checklist rationale)
• V2: Fedlex (Swiss federal law) integration
• V2: Auto-redaction of PII (regex + LLM for contextual names) for pre-NDA sharing
• V2: Document summaries + Q&A pre-generated per key document (buyer experience)
• V2: Email gateway ([email protected]) for auto-classification on email forward
• V3: Direct VDR push to Ansarada, Datasite, Drooms (API integration)
• V3: Watermarking dynamique per recipient (forensic anti-leak)

About Hectelion SA

Hectelion SA is an independent Franco-Swiss M&A advisory firm based in Lausanne, focused on mid-cap transactions (CHF 2M to CHF 500M):

• Sell-side & buy-side mandates for SMEs and family-owned businesses
• Company valuations (DCF, comparable transactions, comparable trading, intangibles)
• Due diligence (financial, legal, operational) and dataroom preparation
• Transaction structuring (share deal vs asset deal, MBO/LBO, earn-out, ratchets)

For tailored DD checklist templates by industry, enterprise deployment, or integration with your existing VDR — book a 30-min discovery call.

Founder: Aristide Ruot — [email protected]

License

MIT — see LICENSE. Use it, fork it, modify it, deploy it for your firm. Attribution appreciated but not required.

Keywords (for GitHub search)

m-and-a · mergers-and-acquisitions · due-diligence · dataroom · virtual-data-room · vdr · vdr-preparation · document-classification · ai-document-organization · pdf-classification · claude-code-skill · claude-skill · legal-tech · m-and-a-tools · dd-prep · dataroom-automation · notary · lawyer-tools · corporate-finance · private-equity · investment-banking · transactional-tools · data-deduplication · version-control-documents · franco-swiss · hectelion