Information Request List (IRL) Tracker skill for Datasite deal rooms. Use this skill whenever a deal team wants to compare VDR content against a buyer's information request list, track document delivery status, or build a due diligence tracker dashboard. Triggers include: "map the IRL", "track what's been provided", "check the information request list", "information gathering list", "IGL", "what have we delivered", "DD tracker", "due diligence tracker", "compare VDR against the request list", "what's still outstanding", "build a diligence dashboard", or any request to track document delivery against buyer requests. Use proactively whenever a buyer has submitted a request list and the deal team needs to manage and track responses. Do not use for overall data room structural gap analysis — use gap-analysis for that.
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Information Request List (IRL) Tracker skill for Datasite deal rooms. Use this skill whenever a deal team wants to compare VDR content against a buyer's information request list, track document delivery status, or build a due diligence tracker dashboard. Triggers include: "map the IRL", "track what's been provided", "check the information request list", "information gathering list", "IGL", "what have we delivered", "DD tracker", "due diligence tracker", "compare VDR against the request list", "what's still outstanding", "build a diligence dashboard", or any request to track document delivery against buyer requests. Use proactively whenever a buyer has submitted a request list and the deal team needs to manage and track responses. Do not use for overall data room structural gap analysis — use gap-analysis for that.
You are helping a deal team map their Datasite data room content against an Information Request List (IRL), assess how well each request is addressed, and produce a live tracking dashboard. The output is a single-file HTML dashboard with no backend required.
Terminology — fileroom vs. folder
Use these terms precisely when communicating with the user:
Fileroom — the single top-level container inside a Datasite project. A project typically has one buyer-facing fileroom. It is not a subject area — it is the container that holds all subject areas.
Folder — everything inside the fileroom: the subject areas (Financial, Legal, HR, Tax, IP, etc.) and all sub-levels beneath them. Always call these folders, never filerooms.
When in doubt: if it is not the single top-level container for the whole project, it is a folder.
Feature Requirements
Capability
Free
Requires Blueflame
Build document inventory from VDR
✅
—
Match IRL items to documents semantically
—
✅
Assess whether document content actually addresses each request
—
✅
Build HTML tracking dashboard
✅
—
Without Blueflame: The skill can build a document inventory and populate the dashboard structure, but cannot match IRL items to documents or assess content relevance. All items will show as Open. The core value of this skill requires Blueflame.
With Blueflame:searchDocuments semantically matches each IRL requirement to relevant passages in the data room, assigning Available / Partially Complete / Open status with source citations.
⚠️ Blueflame content guard — two-tier behavioursearchDocuments is the only permitted source of document content.
Do not use Claude's training knowledge, general M&A knowledge, or inference from file names for any findings.
Step 3a (filename and folder matching) is always free. Complete it across all IRL items first.
Steps 3b/3c (keyword and semantic content search) require searchDocuments. Before starting Step 3b, attempt one call. If it returns an activation link instead of results, do not discard Step 3a results. Present them first, then say:
"I've completed filename matching across your [N] IRL items — results above show what I could match by document name and location. To verify that those documents actually address each request (not just exist nearby), Blueflame AI search needs to be activated:
🔗 Activate Blueflame: [activation link]
With Blueflame: I'll read inside each document to confirm it covers the right year, entity, or clause — so 'Available' means genuinely addressed, not just 'a file with a matching name exists'. Some items shown as filename-matched may be downgraded or upgraded once content is verified.
Would you like to activate now to complete the content verification?"
All content findings must be sourced exclusively from tool results.
listFolderContents — efficient traversal
depth: 1 (default) — immediate children only. Use for targeted lookups.
depth: 5, foldersOnly: true (default when depth > 1) — full folder tree in one call, no documents. Use for structural checks.
depth: 5, foldersOnly: false — full folder tree including all document metadata in one call. Use when building a document inventory.
When depth > 1, the response is a flat list with depth and path columns — not a nested tree.
Step 1 — Load the IRL
The user provides a spreadsheet or document containing the IRL. Read it and extract for each item:
Item ID (e.g. 1.1, 2.3 — or assign sequentially if not present)
Stage — if the IRL has phases/stages (Stage 1 = initial, Stage 2 = follow-up, Stage 3 = confirmatory). If not present, assign Stage 1 to all items.
If category/section is not in the IRL, infer it from the requirement text using these groupings: Financial Performance, Tax, Legal & Regulatory, Commercial & Customers, HR & Employment, Intellectual Property & Technology, Operations, ESG, Corporate & Governance, Other.
Step 2 — Scan the VDR
Call getProjectOverview for deal context. Then call listFolderContents with depth: 5, foldersOnly: false to build the complete document inventory in a single call. The flat response gives you every document's name, metadata ID, path, file type, status, and page count:
Document name
Metadata ID
Full VDR path (folder index + folder name)
File size, page count
This is your document inventory. You'll reference it throughout the matching process.
Step 3 — Match each IRL item to VDR documents
For each IRL requirement, find the best matching document(s) in the VDR. Use a layered approach — don't rely on filename alone:
3a — File-name & folder matching (free, no Blueflame required)
Before any content search, check whether the document inventory already contains a file whose name or folder path clearly matches the requirement. This step costs zero Blueflame credits.
Exact or near-exact name match (e.g. IRL asks for "Management Accounts" → file called "Management Accounts Q3 2025.xlsx" in the Finance folder) → provisionally mark Available (filename match) at low confidence pending content confirmation.
Folder-level match (e.g. IRL asks for "Employment Contracts" → HR/Employment Contracts folder exists with files) → provisionally mark Partially Complete (folder match).
No name or folder match → move to content search below.
If Blueflame is not available, stop here. Complete the filename-matching pass, then proceed to Step 5 to offer the dashboard. If the user confirms, produce it with filename-only matches — clearly label all statuses as "(filename only — unverified)" and note that content confirmation requires Blueflame.
3b — Keyword search (targeted, lower cost)
Run searchDocuments for specific terms in the requirement (dates, entity names, contract parties, regulation names). Keyword search is more targeted than semantic search and should run first to catch exact matches cheaply before triggering a full semantic pass.
3c — Semantic search (comprehensive, higher cost)
Run searchDocuments with the requirement text (or a distilled version of it) as the query, with decompose: true for complex multi-part requests. This returns text passages with document names and page numbers. The passage content confirms whether the document actually addresses the request — not just whether it exists nearby. Only run this step if 3a and 3b did not return a high-confidence match.
3d — Content analysis for scattered information
Some requests cannot be satisfied by a single document — the information is distributed. Examples:
"Customer revenue breakdown" → may require reading invoicing files and aggregating
"List of all subsidiaries" → may require reading multiple corporate documents
"Total headcount by location" → may require reading HR files across multiple folders
When this applies, note it explicitly in the source reference: "Information available through analysis of [Doc A] + [Doc B] — not available as a single file."
3e — Assess match quality and assign status
For each IRL item, assign one of three statuses based on how well the VDR content addresses the request:
Status
Meaning
Criteria
Available
Document fully addresses the request
You found a clear, directly responsive document and can cite the relevant passage/page
Partially Complete
Some but not all of the request is covered
e.g. one tax return found but request covers 3 years; one customer contract found but request asks for the top 10
Open
No responsive document found after searching
Neither semantic nor keyword search returned relevant content
Initial status in the dashboard is set by AI matching:
Available → displayed as "Provided (AI)" (AI found it; human must confirm to become Complete)
Partially Complete → displayed as "Provided (AI)" with lower confidence
Open → displayed as "Open"
Human can then transition:
Provided (AI) → Complete (click "✓ Confirm")
Provided (AI) → Open (click "↩ Reopen")
Complete → Provided (AI) (click "↩ Un-confirm")
Open → N/A (click "Mark N/A")
N/A → Open (click "↩ Reopen")
3f — Build the source reference
For each matched document record:
Filename
VDR index path (e.g. 3.1 Audited Financial Statements)
Page number(s) where relevant content was found
Confidence: high (clear direct match), med (probable match), low (partial or inferred)
Source type: ai_match
One IRL item can map to multiple documents. One document can satisfy multiple IRL items.
Step 4 — Compile the full mapping table
Produce a structured dataset with one row per IRL item:
{
id: "1.1",
requirement: "Audited financial statements for the last 3 years",
section: "Financial Performance",
stage: 1,
status: "Provided (AI)", // Open / Provided (AI) / Complete / N/A
ai_status: "Partially Complete", // Available / Partially Complete / Open
confidence: "med",
documents: [
{
filename: "Apex Ltd - Audited Accounts - FY2024.pdf",
vdr_path: "3.1 Audited Financial Statements",
page: 1,
source: "ai_match"
}
],
gap_note: "FY2023 and FY2022 not found in data room",
category: "Financial Performance",
date_matched: "2026-04-07"
}
Step 5 — Offer the dashboard
Before generating the dashboard, ask:
"I've completed the IRL mapping. Would you like me to generate the full interactive HTML tracking dashboard now? It includes status views, a gap report, and CSV/PDF export — but rendering it will use additional credits. Alternatively I can give you a plain text summary now."
Only build the dashboard if the user confirms. If they decline, go to Step 6 and deliver a plain text summary.
Dashboard specification (build only on user confirmation)
Generate a single-file, self-contained HTML artifact. No backend, no frameworks. All state via vanilla JS. Use a clean, professional style — white cards, dark navy headings, green/amber/red status colours, subtle borders and shadows.
Status badge colours:
Open → red
Provided (AI) → amber
Complete → green
N/A → grey
Layout
Fixed header: project name, search bar (filters across all requirements), Export CSV button, PDF Report button
Fixed left sidebar: navigation links to each of the 7 views, section list with open-item counts
Main content area: right of sidebar, scrollable
Completion calculation
Overall % = (Complete + N/A) / Total × 100
"Provided (AI)" does NOT count toward completion — only human-confirmed items do
View 1 — Status Dashboard (default)
Donut/ring chart showing overall completion %
4 status pills: Open (count), Provided AI (count), Complete (count), N/A (count)
Stage overview row: 3 cards for Stage 1 / 2 / 3, each with count, progress bar, completion %
Section cards grid: one card per section with stacked progress bar (complete + provided + n/a + open) and counts
"Confirm All" button per section — marks all Provided (AI) items in that section as Complete in one click
View 2 — Master Tracker
Full table of all requirements with sortable columns:
ID | Requirement | Category | Stage | Status (badge) | Documents Provided (filenames with confidence dots) | Date Matched
Click column headers to sort ascending/descending
Filter bar: Status dropdown, Section dropdown, Stage dropdown, free-text search
Shows "X of Y requirements" count
"Confirm All" button — marks all currently visible Provided (AI) items as Complete
Each row expandable to show full document list with VDR paths and page numbers
View 3 — Coverage Heatmap
Grid of section tiles, colour-coded by completion %:
≥80% → green | 50–79% → amber | <50% → red
Background fill height = % provided (including AI-matched)
Integrated Gap Report below the grid: grouped by Stage, listing every Open item with ID, requirement text, and section
View 4 — IRL by Section
Section card grid → click to drill into a section
Section detail view: section header with item count, filter bar, and all requirements as document item cards
Each card shows: ID, requirement text, status badge, matched documents with confidence dots, gap note if Partially Complete
Upload zone per card: drag-drop to add a document manually (stores filename + metadata only, not binary)
"Confirm All" button per section
View 5 — IRL by Stage
3 stage tabs at top (Stage 1 / Stage 2 / Stage 3) with counts
Filter bar + document item cards for the selected stage
"Confirm All" button per stage — marks all Provided (AI) items in the stage as Complete
View 6 — Gap Analysis
3 gap cards: Critical (Stage 1 Open), Moderate (Stage 2 Open), Low (Stage 3 Open) — with counts
Section-by-section rows: progress bar, completion %, stage breakdown badges, open count
Drill-down per section showing individual open items with requirement text and gap note
View 7 — Document Index
Full list of all VDR documents scanned, with:
VDR index number
Document name
IRL item ID(s) it addresses (can be multiple)
Status of those IRL items
Sorted by VDR index. Filterable by section and status.
Export: CSV
Button in header → downloads DD_Tracker_[ProjectName]_[YYYY-MM-DD].csv with columns:
Item ID, Requirement, Category, Stage, Status, Files Uploaded, Confidence, Date Matched
Export: PDF Report
Button in header → opens new window with print-ready HTML, triggers window.print():
Header: "Due Diligence Tracker Report" + deal name + generation date
Executive summary: 4 status cards (Open / Provided AI / Complete / N/A) + overall completion %
Print CSS: @page { size: A4; margin: 20mm }, hide sidebar/header, show only report content
Step 6 — Deliver to the user
After rendering the dashboard, summarise:
"I've mapped [N] IRL items against the data room. [X] are Available (matched with high/med confidence), [Y] are Partially Complete, and [Z] are Open with no document found. Overall AI-assisted coverage: [%].
Use ✓ Confirm to validate AI matches and move items to Complete. The dashboard tracks completion in real time — only human-confirmed items count toward overall progress."
Operating principles
Content beats filename. A document called Q4_Report.pdf in the Finance folder might satisfy an IRL request for management accounts — or it might not. Always use searchDocuments to read the content before marking as Available.
One document, many requests. The same audited accounts file might satisfy the request for "annual financials", "revenue figures", "EBITDA history", and "depreciation policy" simultaneously. Map it to all relevant items.
Partial is honest. If a request asks for 3 years of tax returns and you found 2, mark it Partially Complete and note the gap. Don't mark it Available — the buyer will notice.
AI matches are provisional. Every item starts as "Provided (AI)" at best. The deal team's confirmation step is what makes it Complete. This distinction is important — it protects the deal team from inadvertently representing incomplete coverage as confirmed.
Store metadata, not binaries. The dashboard stores filenames, paths, confidence scores, and timestamps — not the actual file content. This keeps the HTML lightweight and shareable.
Performance Notes
Accurate status is more valuable than high completion percentages. Mark items Partially Complete or Open rather than stretching a weak match to Available.
Content beats filename — always use searchDocuments to confirm a document actually addresses the request before marking Available.
AI matches are provisional by design. The deal team's confirmation step is what makes an item Complete.
Common Issues
getProjectOverview fails or returns the wrong project
Check that the Datasite MCP connector is connected (Settings → Extensions → Datasite should show "Connected"). If you have multiple projects open, confirm with the user which project to use.
listFolderContents returns no results
The fileroom may be empty or unpublished. Re-run listFolderContents without a metadataId to list all filerooms from the root. If a fileroom exists but shows 0 documents, the content may not yet be published — note this to the user and proceed with what is available.
searchDocuments returns an activation link instead of results
Blueflame AI search is not yet active on this project. Follow the Blueflame prompt in the skill instructions above. Do not attempt to answer using Claude's training knowledge.
MCP disconnects mid-workflow
Reconnect via Settings → Extensions → Datasite. Resume from the last completed step — results already gathered do not need to be re-fetched.
updateContent or createContent returns a permissions error
The user's Datasite account may not have Editor permissions on this project. Ask them to check their role in Datasite project settings.
