> ## Documentation Index
> Fetch the complete documentation index at: https://docs.eigenai.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Demo Samples
> A free 10-task sample of the APEX Agent dataset — management consulting cases across Project Terrace (Floor & Decor) and Project Roku (CTV platform analysis).
These demo samples are a free 10-task slice of the **APEX Agent** dataset. For the complete dataset — its scale, difficulty profile, and benchmark results — see the [Full Dataset](/products/eigendata-cli/datasets/apex-agent/overview) page.
The samples below showcase the management consulting cases — **Project Terrace** (Floor & Decor retail strategy) and **Project Roku** (CTV platform analysis) — where the agent must reason over store-level spreadsheets, SEC filings, industry research, customer surveys, and competitor benchmarks.
## Overview
| Property | Value |
| --------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Domains** | Investment Banking, Law, Management Consulting |
| **Demo domain** | Management Consulting |
| **Scenario** | Floor & Decor (FND) operational performance deep-dive — Project Terrace |
| **Tasks** | 10 consulting analysis tasks with rubric-based grading |
| **Task types** | Inventory risk analysis, competitor benchmarking, store productivity, installed sales economics, NPS computation, CTV market analysis, ad revenue modeling |
## Environment
The demo showcases two management consulting environments: **Project Terrace** (Floor & Decor retail optimization, 104 files) and **Project Roku** (CTV/streaming platform strategy). The sample below uses Project Terrace:
```
apex-agent/
├── project_terrace/
│ └── filesystem/
│ ├── 1. Project Admin/ # Engagement charter, timeline, team roster (DOCX)
│ ├── 2. Company Filings/ # SEC 10-K filings for 7 companies (PDF)
│ │ ├── Floor & Decor/
│ │ ├── Home Depot/
│ │ ├── LL Flooring/
│ │ ├── Lowes/
│ │ ├── Mohawk Industries/
│ │ ├── MSC Industrial/
│ │ └── Tile Shop Holdings/
│ ├── 3. Industry Research/ # BLS, Census, FRED, Harvard JCHS data (PDF/XLSX)
│ ├── 4. Analysis Models/ # 30+ FND analysis spreadsheets (XLSX/CSV)
│ ├── 5. Surveys & Research Notes/ # Analyst notes, customer surveys (DOCX/XLSX)
│ ├── 6. Deliverables/ # SteerCo deck, memos, playbooks (PPTX/DOCX)
│ └── 7. Reference & Frameworks/ # Methodology docs, glossary (DOCX)
└── project_roku/
└── filesystem/ # Roku CTV platform data (similar structure)
```
**Project Admin (5 DOCX):** Engagement charter, kickoff meeting notes, project timeline, scope & work plan, and team roster for Project Terrace.
**Company Filings (21 PDFs):** 10-K annual filings for 7 comparable companies — Floor & Decor (FND), Home Depot (HD), LL Flooring (LL), Lowe's (LOW), Mohawk Industries (MHK), MSC Industrial (MSM), and Tile Shop Holdings (TTS), covering 2021–2025.
**Industry Research (14 files):** Government and academic data sources — BLS employment, consumer expenditures, CPI shelter, JOLTS, occupational wage surveys (OEWS); Census construction spending, retail sales, e-commerce; FRED housing starts, retail sales; Harvard JCHS state housing reports.
**Analysis Models (30+ XLSX + 2 CSV):** Store-level and company-level analysis spreadsheets including:
* `FND_Installed_Sales_Economics.xlsx` — 250 stores with margin, callback, and revenue data
* `FND_Store_Performance_v3.xlsx` — store productivity metrics by region
* `FND_Inventory_Analysis.xlsx` — inventory turns, aging, slow-moving SKU analysis
* `FND_Competitor_Benchmarking.xlsx` — peer comparison across key metrics
* `FND_Customer_Cohort_Analysis.xlsx`, `FND_Gross_Margin_Bridge.xlsx`, `FND_Scenario_Sensitivity_Model.xlsx`, and 20+ more
* Company financial extracts for HD, LL, LOW, MHK, MSM, TTS
**Surveys & Research Notes (10 files):** Analyst notes for each peer company, customer survey data and column guide, expert call notes on the flooring industry.
**Deliverables (5 files):** Competitive landscape analysis (PPTX), executive summary memo, implementation playbook, SteerCo presentation, and store productivity deep dive.
**Reference & Frameworks (7 DOCX):** Benchmarking methodology, connected customer analysis framework, industry glossary, pro customer economics, renovation ROI framework, SG\&A efficiency analysis, and store classification framework.
## Data
| File | Description |
| ------------------------ | --------------------------------------------------------------------------------------------- |
| `tasks_and_rubrics.json` | 10 tasks with prompts, gold responses, and rubric criteria |
| `trajectories.jsonl` | Agent trajectories with chain-of-thought reasoning and tool calls |
| `tool_schemas.json` | Tool definitions available to the agent (filesystem, spreadsheets, code execution, documents) |
## Sample
| Task | World | Description | Expected Output |
| ------------ | --------------- | ---------------------------------------------- | ---------------------------------------------------------- |
| `terrace_01` | Project Terrace | Inventory risk analysis by region | SE inventory \$46.5M, 16 problem stores, worst FND-187 |
| `terrace_02` | Project Terrace | Benchmark Floor & Decor against peers | FND growth 3.7%, LL Flooring -15.7%, Tile Shop GM 64.9% |
| `terrace_03` | Project Terrace | Store productivity and profitability by region | NE \$58.28/sqft, MW \$54.02/sqft, company avg \$56.27/sqft |
| `terrace_04` | Project Terrace | Rank stores by net installation margin | Best FND-109 at 12.0%, worst FND-180 at 5.0%, avg 8.7% |
| `terrace_05` | Project Terrace | Compute NPS from raw customer survey data | DIY NPS -43.1, Design-Assisted -43.8, Pro -47.5 |
| `roku_01` | Project Roku | FAST channel lineup economics | Top: Entertainment \$84.0M, News \$50.4M |
| `roku_02` | Project Roku | CTV platform market concentration (HHI) | HHI 1500, CR3 56% |
| `roku_03` | Project Roku | CTV viewer and advertiser NPS | Viewer -39.2, Advertiser -54.0, Overall -42.9 |
| `roku_04` | Project Roku | Quarterly roll-up of Roku Channel performance | Q2 2023 Ad Rev \$545.7M, margin 68.1% |
| `roku_05` | Project Roku | Cross-analysis of advertising categories | Top efficiency: Automotive (5.04), CPG/FMCG (4.80) |
**Prompt:**
> Which stores have the best and worst economics on installed sales? Rank all stores by net installation margin, then give me these key highlights: the #1 store and its margin, the lowest-margin store and its margin, the portfolio average net installation margin, and the gap in percentage points between the best and worst store. Also report the installed revenue for FND-194 and FND-144 specifically, and the callback rate for FND-307. Round percentages to one decimal place and revenue to the nearest dollar. Reply here.
**Expected output:**
* Top store: FND-109 with net installation margin of 12.0%
* Lowest store: FND-180 with net installation margin of 5.0%
* Portfolio avg net installation margin: 8.7%
* Gap between best and worst: 7.0pp
* FND-194 installed revenue: \$5,223,275
* FND-144 installed revenue: \$4,933,729
* FND-307 callback rate: 7.4%
**Rubric criteria:**
1. States that FND-109 is the top-ranked store with net installation margin of 12.0%
2. States that FND-180 is among the lowest net installation margin stores at 5.0%
3. States that portfolio average net installation margin is 8.7%
4. States that the gap between best and worst store is 7.0pp
5. States that FND-194 has installed revenue of \$5,223,275
6. States that FND-307 has a callback rate of 7.4%
7. States that FND-144 has installed revenue of \$4,933,729
Each assistant message includes `reasoning_content` (chain-of-thought) showing the agent's step-by-step analytical reasoning. Reasoning is shown in *italics* below.
**System:**
> You are an AI assistant with access to Code Execution, Excel/Spreadsheets, Filesystem, and Word/Documents. You MUST use tools to explore files and data before answering. NEVER assume. Keep full precision through intermediate calculations and only round final reported values.
**User:**
> Which stores have the best and worst economics on installed sales? Rank all stores by net installation margin, then give me these key highlights: the #1 store and its margin, the lowest-margin store and its margin, the portfolio average net installation margin, and the gap in percentage points between the best and worst store. Also report the installed revenue for FND-194 and FND-144 specifically, and the callback rate for FND-307. Round percentages to one decimal place and revenue to the nearest dollar. Reply here.
**Assistant:**
**Step 1** — Explore directory tree:
*The user is asking about installed sales economics across stores — specifically ranking all stores by net installation margin, identifying best/worst, computing the portfolio average, finding the gap, plus specific revenue figures for FND-194 and FND-144, and the callback rate for FND-307. I need to locate the relevant data file(s) first...*
> I'll start by exploring the filesystem to find relevant files, then thoroughly analyze the data.
```json theme={null}
filesystem_get_directory_tree({"path": "/", "max_depth": 3})
```
**Tool result:**
```
/
├── 1. Project Admin/
│ ├── Engagement Charter - Project Terrace.docx
│ ├── Meeting Notes - Week 1 Kickoff.docx
│ └── ...
├── 2. Company Filings/
│ ├── Floor & Decor/ (FND 10-K 2023–2025)
│ ├── Home Depot/ ...
│ └── ...
├── 3. Industry Research/ ...
├── 4. Analysis Models/
│ ├── FND_Installed_Sales_Economics.xlsx
│ ├── FND_Store_Performance_v3.xlsx
│ └── ... (30+ analysis files)
├── 5. Surveys & Research Notes/ ...
├── 6. Deliverables/ ...
└── 7. Reference & Frameworks/ ...
```
**Step 2** — List spreadsheet tabs:
*The directory tree reveals a file that's an exact match for the user's question: `/4. Analysis Models/FND_Installed_Sales_Economics.xlsx`. "Installed Sales Economics" maps directly to the request for net installation margins, callback rates, and installed revenue by store...*
> The key file is `FND_Installed_Sales_Economics.xlsx`. Let me explore it.
```json theme={null}
excel_list_tabs_in_spreadsheet({"file_path": "/4. Analysis Models/FND_Installed_Sales_Economics.xlsx"})
```
**Tool result:**
```json theme={null}
{"worksheets": [
{"name": "Installed_Sales_Econ", "index": 0, "row_count": 251, "column_count": 11}
]}
```
**Step 3** — Read the full spreadsheet:
*Single tab called 'Installed\_Sales\_Econ' with 251 rows and 11 columns. That's 1 header row + 250 data rows — a manageable size to read in full. With 11 columns, the data likely has all the metrics I need in one consolidated table...*
```json theme={null}
excel_read_tab({"file_path": "/4. Analysis Models/FND_Installed_Sales_Economics.xlsx", "tab_index": 0})
```
**Tool result:**
```
Store_ID Installed_Revenue Product_Margin_% Installation_Markup_%
Subcontractor_Cost Net_Installation_Margin_% Callback_Rate_%
Callback_Cost_per_Job Active_Subcontractors Avg_Project_Size Projects_per_Month
FND-101 3,283,341 0.442 0.273 1,608,686 0.097 0.119 403 9 5,932 74
FND-102 3,001,568 0.439 0.348 1,395,241 0.058 0.110 263 23 2,525 58
FND-103 2,195,965 0.403 0.329 949,084 0.088 0.083 161 21 7,996 26
... (250 stores total — FND-101 through FND-350)
FND-350 2,165,799 0.462 0.393 878,750 0.116 0.041 303 17 7,852 67
```
**Step 4** — Execute Python computation:
*Now I have the full dataset: 250 stores (FND-101 through FND-350) with 11 columns. The columns relevant to the user's question are: column B (Installed\_Revenue), column F (Net\_Installation\_Margin\_%), and column G (Callback\_Rate\_%). I need to rank all 250 stores by column F...*
> Now I have all the data. Let me run a Python script to compute the rankings and extract the specific values requested.
```json theme={null}
code_execution_code_exec({"request": {"code": "# Rank stores by net installation margin, extract highlights ..."}})
```
**Tool result:**
```
Total stores: 250
#1 (Best) Store: FND-109 — Net Installation Margin: 12.0%
Worst Store: FND-293 — Net Installation Margin: 5.0%
Portfolio Average Net Installation Margin: 8.7%
Gap (Best - Worst): 7.0 percentage points
FND-144 Installed Revenue: $4,933,729
FND-194 Installed Revenue: $5,223,275
FND-307 Callback Rate: 7.4%
--- Checking for ties ---
Stores tied at top (0.12): ['FND-109', 'FND-144', 'FND-153']
Stores tied at bottom (0.05): ['FND-180', 'FND-293']
--- Full Ranking (Top 10 and Bottom 10) ---
Rank 1: FND-109 — 12.0%
Rank 2: FND-144 — 12.0%
Rank 3: FND-153 — 12.0%
...
Rank 249: FND-180 — 5.0%
Rank 250: FND-293 — 5.0%
```
**Step 5** — Verify tied stores and callback rate (6 parallel reads):
*The code found a three-way tie at the top: FND-109, FND-144, and FND-153 all at 0.12 (12.0%), and a two-way tie at the bottom: FND-180 and FND-293 both at 0.05 (5.0%). But these margin values were stored as rounded decimals. Let me verify the exact stored values to confirm tie-breaking...*
> There's a three-way tie at the top (12.0%) and a two-way tie at the bottom (5.0%). Let me verify the exact stored values to confirm the tie-breaking and also double-check the callback rate for FND-307.
```json theme={null}
excel_read_tab({"file_path": "...", "cell_range": "A10:K10"}) // FND-109
excel_read_tab({"file_path": "...", "cell_range": "A45:K45"}) // FND-144
excel_read_tab({"file_path": "...", "cell_range": "A54:K54"}) // FND-153
excel_read_tab({"file_path": "...", "cell_range": "A81:K81"}) // FND-180
excel_read_tab({"file_path": "...", "cell_range": "A194:K194"}) // FND-293
excel_read_tab({"file_path": "...", "cell_range": "A208:K208"}) // FND-307
```
**Tool results:**
```
FND-109 1,930,484 0.399 0.265 907,674 0.12 0.073 428 16 7,071 31
FND-144 4,933,729 0.452 0.332 2,111,506 0.12 0.049 263 24 6,241 35
FND-153 1,510,841 0.440 0.398 539,545 0.12 0.120 472 21 3,865 54
FND-180 3,081,065 0.390 0.373 1,266,627 0.05 0.060 375 8 6,243 77
FND-293 2,238,899 0.470 0.367 784,513 0.05 0.052 417 12 3,170 53
FND-307 2,781,939 0.473 0.326 1,277,247 0.051 0.074 264 9 6,298 70
```
**Step 6** — Final answer:
*All six verification reads confirm the stored values exactly match the computation. The three top-margin stores all show F = 0.12 with no hidden precision differences — FND-109, FND-144, and FND-153 are genuinely tied. The two bottom stores both show F = 0.05 — FND-180 and FND-293 are also a true tie.*
> ## Installed Sales Economics — Store Ranking Highlights
>
> | Metric | Value |
> | ----------------------------------------- | --------------------------------------------------- |
> | **#1 Store (Best Margin)** | **FND-109 — 12.0%** (tied with FND-144 and FND-153) |
> | **Lowest-Margin Store** | **FND-180 — 5.0%** (tied with FND-293) |
> | **Portfolio Avg Net Installation Margin** | **8.7%** |
> | **Gap (Best vs. Worst)** | **7.0 percentage points** |
>
> | Store | Metric | Value |
> | ----------- | ----------------- | --------------- |
> | **FND-194** | Installed Revenue | **\$5,223,275** |
> | **FND-144** | Installed Revenue | **\$4,933,729** |
> | **FND-307** | Callback Rate | **7.4%** |
>
> The **7.0 pp spread** from best to worst signals meaningful variation in subcontractor cost management and installation markup discipline across the portfolio, suggesting room for margin improvement at the bottom of the distribution. **FND-307** ranks near the bottom of the margin table (5.1%) and carries a 7.4% callback rate, which is roughly in line with the portfolio but paired with weak margin economics.
```json theme={null}
{
"task_id": "terrace_04",
"rubric_score": 1.0,
"strict_pass": true,
"scoring_values": {
"passed_count": 7,
"failed_count": 0,
"total_count": 7,
"grade_score_percentage": 100.0
},
"steps": 6,
"tool_calls": 10,
"grading_time_seconds": 18.2
}
```
Each rubric criterion is independently verified with a detailed rationale:
```json theme={null}
{
"verifier_id": "ver_terrace_04_01",
"score": 1.0,
"grade": "pass",
"rationale": "## Evidence\n\n... detailed analysis ...\n\n## Assessment\n\n... MEETS criterion ..."
}
```
## Download
```bash theme={null}
# Download APEX Agent data
hf download jindidi/eigendata-demo-data --repo-type dataset --include "apex_agent/*"
```
View APEX Agent files
For the complete APEX Agent corpus — its difficulty profile, benchmark results, and commercial licensing — see the [Full Dataset](/products/eigendata-cli/datasets/apex-agent/overview) page.