toon/docs/guide/benchmarks.md

# Benchmarks

The benchmarks on this page measure TOON's performance across two key dimensions:

- **Retrieval Accuracy**: How well LLMs understand and extract information from different input formats.
- **Token Efficiency**: How many tokens each format requires to represent the same data.

Benchmarks are organized into two tracks to ensure fair comparisons:

- **Mixed-Structure Track**: Datasets with nested or semi-uniform structures (TOON vs JSON, YAML, XML). CSV excluded as it cannot properly represent these structures.
- **Flat-Only Track**: Datasets with flat tabular structures where CSV is applicable (CSV vs TOON vs JSON, YAML, XML).

## Retrieval Accuracy

<!-- automd:file src="../../benchmarks/results/retrieval-accuracy.md" -->

Benchmarks test LLM comprehension across different input formats using 209 data retrieval questions on 4 models.

<details>
<summary><strong>Show Dataset Catalog</strong></summary>

#### Dataset Catalog

| Dataset | Rows | Structure | CSV Support | Eligibility |
| ------- | ---- | --------- | ----------- | ----------- |
| Uniform employee records | 100 | uniform | ✓ | 100% |
| E-commerce orders with nested structures | 50 | nested | ✗ | 33% |
| Time-series analytics data | 60 | uniform | ✓ | 100% |
| Top 100 GitHub repositories | 100 | uniform | ✓ | 100% |
| Semi-uniform event logs | 75 | semi-uniform | ✗ | 50% |
| Deeply nested configuration | 11 | deep | ✗ | 0% |
| Valid complete dataset (control) | 20 | uniform | ✓ | 100% |
| Array truncated: 3 rows removed from end | 17 | uniform | ✓ | 100% |
| Extra rows added beyond declared length | 23 | uniform | ✓ | 100% |
| Inconsistent field count (missing salary in row 10) | 20 | uniform | ✓ | 100% |
| Missing required fields (no email in multiple rows) | 20 | uniform | ✓ | 100% |

**Structure classes:**
- **uniform**: All objects have identical fields with primitive values
- **semi-uniform**: Mix of uniform and non-uniform structures
- **nested**: Objects with nested structures (nested objects or arrays)
- **deep**: Highly nested with minimal tabular eligibility

**CSV Support:** ✓ (supported), ✗ (not supported – would require lossy flattening)

**Eligibility:** Percentage of arrays that qualify for TOON's tabular format (uniform objects with primitive values)

</details>

#### Efficiency Ranking (Accuracy per 1K Tokens)

Each format's overall performance, balancing accuracy against token cost:

```
TOON           ████████████████████   26.9  │  73.9% acc  │  2,744 tokens
JSON compact   █████████████████░░░   22.9  │  70.7% acc  │  3,081 tokens
YAML           ██████████████░░░░░░   18.6  │  69.0% acc  │  3,719 tokens
JSON           ███████████░░░░░░░░░   15.3  │  69.7% acc  │  4,545 tokens
XML            ██████████░░░░░░░░░░   13.0  │  67.1% acc  │  5,167 tokens
```

TOON achieves **73.9%** accuracy (vs JSON's 69.7%) while using **39.6% fewer tokens**.

**Note on CSV:** Excluded from ranking as it only supports 109 of 209 questions (flat tabular data only). While CSV is highly token-efficient for simple tabular data, it cannot represent nested structures that other formats handle.

#### Per-Model Accuracy

Accuracy across 4 LLMs on 209 data retrieval questions:

```
claude-haiku-4-5-20251001
→ TOON           ████████████░░░░░░░░    59.8% (125/209)
  JSON           ███████████░░░░░░░░░    57.4% (120/209)
  YAML           ███████████░░░░░░░░░    56.0% (117/209)
  XML            ███████████░░░░░░░░░    55.5% (116/209)
  JSON compact   ███████████░░░░░░░░░    55.0% (115/209)
  CSV            ██████████░░░░░░░░░░    50.5% (55/109)

gemini-2.5-flash
→ TOON           ██████████████████░░    87.6% (183/209)
  CSV            █████████████████░░░    86.2% (94/109)
  JSON compact   ████████████████░░░░    82.3% (172/209)
  YAML           ████████████████░░░░    79.4% (166/209)
  XML            ████████████████░░░░    79.4% (166/209)
  JSON           ███████████████░░░░░    77.0% (161/209)

gpt-5-nano
→ TOON           ██████████████████░░    90.9% (190/209)
  JSON compact   ██████████████████░░    90.9% (190/209)
  JSON           ██████████████████░░    89.0% (186/209)
  CSV            ██████████████████░░    89.0% (97/109)
  YAML           █████████████████░░░    87.1% (182/209)
  XML            ████████████████░░░░    80.9% (169/209)

grok-4-fast-non-reasoning
→ TOON           ███████████░░░░░░░░░    57.4% (120/209)
  JSON           ███████████░░░░░░░░░    55.5% (116/209)
  JSON compact   ███████████░░░░░░░░░    54.5% (114/209)
  YAML           ███████████░░░░░░░░░    53.6% (112/209)
  XML            ███████████░░░░░░░░░    52.6% (110/209)
  CSV            ██████████░░░░░░░░░░    52.3% (57/109)
```

> [!TIP] Results Summary
> TOON achieves **73.9% accuracy** (vs JSON's 69.7%) while using **39.6% fewer tokens** on these datasets.

<details>
<summary><strong>Performance by dataset, model, and question type</strong></summary>

#### Performance by Question Type

| Question Type | TOON | JSON compact | JSON | CSV | YAML | XML |
| ------------- | ---- | ---- | ---- | ---- | ---- | ---- |
| Field Retrieval | 99.6% | 99.3% | 99.3% | 100.0% | 98.2% | 98.9% |
| Aggregation | 54.4% | 47.2% | 48.8% | 44.0% | 47.6% | 41.3% |
| Filtering | 56.3% | 57.3% | 50.5% | 49.1% | 51.0% | 47.9% |
| Structure Awareness | 88.0% | 83.0% | 83.0% | 85.9% | 80.0% | 80.0% |
| Structural Validation | 70.0% | 45.0% | 50.0% | 80.0% | 60.0% | 80.0% |

#### Performance by Dataset

##### Uniform employee records

| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `csv` | 72.0% | 2,352 | 118/164 |
| `toon` | 73.8% | 2,518 | 121/164 |
| `json-compact` | 69.5% | 3,953 | 114/164 |
| `yaml` | 68.3% | 4,982 | 112/164 |
| `json-pretty` | 68.3% | 6,360 | 112/164 |
| `xml` | 69.5% | 7,324 | 114/164 |

##### E-commerce orders with nested structures

| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `toon` | 81.1% | 7,232 | 133/164 |
| `json-compact` | 76.8% | 6,794 | 126/164 |
| `yaml` | 75.6% | 8,347 | 124/164 |
| `json-pretty` | 76.2% | 10,713 | 125/164 |
| `xml` | 74.4% | 12,023 | 122/164 |

##### Time-series analytics data

| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `csv` | 73.3% | 1,406 | 88/120 |
| `toon` | 72.5% | 1,548 | 87/120 |
| `json-compact` | 71.7% | 2,349 | 86/120 |
| `yaml` | 71.7% | 2,949 | 86/120 |
| `json-pretty` | 68.3% | 3,676 | 82/120 |
| `xml` | 68.3% | 4,384 | 82/120 |

##### Top 100 GitHub repositories

| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `toon` | 62.9% | 8,779 | 83/132 |
| `csv` | 61.4% | 8,527 | 81/132 |
| `yaml` | 59.8% | 13,141 | 79/132 |
| `json-compact` | 55.3% | 11,464 | 73/132 |
| `json-pretty` | 56.1% | 15,157 | 74/132 |
| `xml` | 48.5% | 17,105 | 64/132 |

##### Semi-uniform event logs

| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `json-compact` | 63.3% | 4,819 | 76/120 |
| `toon` | 57.5% | 5,799 | 69/120 |
| `json-pretty` | 59.2% | 6,797 | 71/120 |
| `yaml` | 48.3% | 5,827 | 58/120 |
| `xml` | 46.7% | 7,709 | 56/120 |

##### Deeply nested configuration

| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `json-compact` | 92.2% | 574 | 107/116 |
| `toon` | 95.7% | 666 | 111/116 |
| `yaml` | 91.4% | 686 | 106/116 |
| `json-pretty` | 94.0% | 932 | 109/116 |
| `xml` | 92.2% | 1,018 | 107/116 |

##### Valid complete dataset (control)

| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `toon` | 100.0% | 544 | 4/4 |
| `json-compact` | 100.0% | 795 | 4/4 |
| `yaml` | 100.0% | 1,003 | 4/4 |
| `json-pretty` | 100.0% | 1,282 | 4/4 |
| `csv` | 25.0% | 492 | 1/4 |
| `xml` | 0.0% | 1,467 | 0/4 |

##### Array truncated: 3 rows removed from end

| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `csv` | 100.0% | 425 | 4/4 |
| `xml` | 100.0% | 1,251 | 4/4 |
| `toon` | 0.0% | 474 | 0/4 |
| `json-compact` | 0.0% | 681 | 0/4 |
| `json-pretty` | 0.0% | 1,096 | 0/4 |
| `yaml` | 0.0% | 859 | 0/4 |

##### Extra rows added beyond declared length

| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `csv` | 100.0% | 566 | 4/4 |
| `toon` | 75.0% | 621 | 3/4 |
| `xml` | 100.0% | 1,692 | 4/4 |
| `yaml` | 75.0% | 1,157 | 3/4 |
| `json-compact` | 50.0% | 917 | 2/4 |
| `json-pretty` | 50.0% | 1,476 | 2/4 |

##### Inconsistent field count (missing salary in row 10)

| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `csv` | 75.0% | 489 | 3/4 |
| `yaml` | 100.0% | 996 | 4/4 |
| `toon` | 100.0% | 1,019 | 4/4 |
| `json-compact` | 75.0% | 790 | 3/4 |
| `xml` | 100.0% | 1,458 | 4/4 |
| `json-pretty` | 75.0% | 1,274 | 3/4 |

##### Missing required fields (no email in multiple rows)

| Format | Accuracy | Tokens | Correct/Total |
| ------ | -------- | ------ | ------------- |
| `csv` | 100.0% | 329 | 4/4 |
| `xml` | 100.0% | 1,411 | 4/4 |
| `toon` | 75.0% | 983 | 3/4 |
| `yaml` | 25.0% | 960 | 1/4 |
| `json-pretty` | 25.0% | 1,230 | 1/4 |
| `json-compact` | 0.0% | 755 | 0/4 |

#### Performance by Model

##### claude-haiku-4-5-20251001

| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
| `toon` | 59.8% | 125/209 |
| `json-pretty` | 57.4% | 120/209 |
| `yaml` | 56.0% | 117/209 |
| `xml` | 55.5% | 116/209 |
| `json-compact` | 55.0% | 115/209 |
| `csv` | 50.5% | 55/109 |

##### gemini-2.5-flash

| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
| `toon` | 87.6% | 183/209 |
| `csv` | 86.2% | 94/109 |
| `json-compact` | 82.3% | 172/209 |
| `yaml` | 79.4% | 166/209 |
| `xml` | 79.4% | 166/209 |
| `json-pretty` | 77.0% | 161/209 |

##### gpt-5-nano

| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
| `toon` | 90.9% | 190/209 |
| `json-compact` | 90.9% | 190/209 |
| `json-pretty` | 89.0% | 186/209 |
| `csv` | 89.0% | 97/109 |
| `yaml` | 87.1% | 182/209 |
| `xml` | 80.9% | 169/209 |

##### grok-4-fast-non-reasoning

| Format | Accuracy | Correct/Total |
| ------ | -------- | ------------- |
| `toon` | 57.4% | 120/209 |
| `json-pretty` | 55.5% | 116/209 |
| `json-compact` | 54.5% | 114/209 |
| `yaml` | 53.6% | 112/209 |
| `xml` | 52.6% | 110/209 |
| `csv` | 52.3% | 57/109 |

</details>

#### What's Being Measured

This benchmark tests **LLM comprehension and data retrieval accuracy** across different input formats. Each LLM receives formatted data and must answer questions about it. This does **not** test the model's ability to generate TOON output – only to read and understand it.

#### Datasets Tested

Eleven datasets designed to test different structural patterns and validation capabilities:

**Primary datasets:**

1. **Tabular** (100 employee records): Uniform objects with identical fields – optimal for TOON's tabular format.
2. **Nested** (50 e-commerce orders): Complex structures with nested customer objects and item arrays.
3. **Analytics** (60 days of metrics): Time-series data with dates and numeric values.
4. **GitHub** (100 repositories): Real-world data from top GitHub repos by stars.
5. **Event Logs** (75 logs): Semi-uniform data with ~50% flat logs and ~50% with nested error objects.
6. **Nested Config** (1 configuration): Deeply nested configuration with minimal tabular eligibility.

**Structural validation datasets:**

7. **Control**: Valid complete dataset (baseline for validation)
8. **Truncated**: Array with 3 rows removed from end (tests `[N]` length detection)
9. **Extra rows**: Array with 3 additional rows beyond declared length
10. **Width mismatch**: Inconsistent field count (missing salary in row 10)
11. **Missing fields**: Systematic field omissions (no email in multiple rows)

#### Question Types

209 questions are generated dynamically across five categories:

- **Field retrieval (33%)**: Direct value lookups or values that can be read straight off a record (including booleans and simple counts such as array lengths)
  - Example: "What is Alice's salary?" → `75000`
  - Example: "How many items are in order ORD-0042?" → `3`
  - Example: "What is the customer name for order ORD-0042?" → `John Doe`

- **Aggregation (30%)**: Dataset-level totals and averages plus single-condition filters (counts, sums, min/max comparisons)
  - Example: "How many employees work in Engineering?" → `17`
  - Example: "What is the total revenue across all orders?" → `45123.50`
  - Example: "How many employees have salary > 80000?" → `23`

- **Filtering (23%)**: Multi-condition queries requiring compound logic (AND constraints across fields)
  - Example: "How many employees in Sales have salary > 80000?" → `5`
  - Example: "How many active employees have more than 10 years of experience?" → `8`

- **Structure awareness (12%)**: Tests format-native structural affordances (TOON's `[N]` count and `{fields}`, CSV's header row)
  - Example: "How many employees are in the dataset?" → `100`
  - Example: "List the field names for employees" → `id, name, email, department, salary, yearsExperience, active`
  - Example: "What is the department of the last employee?" → `Sales`

- **Structural validation (2%)**: Tests ability to detect incomplete, truncated, or corrupted data using structural metadata
  - Example: "Is this data complete and valid?" → `YES` (control dataset) or `NO` (corrupted datasets)
  - Tests TOON's `[N]` length validation and `{fields}` consistency checking
  - Demonstrates CSV's lack of structural validation capabilities

#### Evaluation Process

1. **Format conversion**: Each dataset is converted to all 6 formats (TOON, JSON compact, JSON, CSV, YAML, XML).
2. **Query LLM**: Each model receives formatted data + question in a prompt and extracts the answer.
3. **Validate deterministically**: Answers are validated using type-aware comparison (e.g., `50000` = `$50,000`, `Engineering` = `engineering`, `2025-01-01` = `January 1, 2025`) without requiring an LLM judge.

#### Models & Configuration

- **Models tested**: `claude-haiku-4-5-20251001`, `gemini-2.5-flash`, `gpt-5-nano`, `grok-4-fast-non-reasoning`
- **Token counting**: Using `gpt-tokenizer` with `o200k_base` encoding (GPT-5 tokenizer)
- **Temperature**: Not set (models use their defaults)
- **Total evaluations**: 209 questions × 6 formats × 4 models = 5,016 LLM calls

<!-- /automd -->

## Token Efficiency

Token counts are measured using the GPT-5 `o200k_base` tokenizer via [`gpt-tokenizer`](https://github.com/niieani/gpt-tokenizer). Savings are calculated against formatted JSON (2-space indentation) as the primary baseline, with additional comparisons to compact JSON (minified), YAML, and XML. Actual savings vary by model and tokenizer.

The benchmarks test datasets across different structural patterns (uniform, semi-uniform, nested, deeply nested) to show where TOON excels and where other formats may be better.

<!-- automd:file src="../../benchmarks/results/token-efficiency.md" -->

#### Mixed-Structure Track

Datasets with nested or semi-uniform structures. CSV excluded as it cannot properly represent these structures.

```
🛒 E-commerce orders with nested structures  ┊  Tabular: 33%
   │
   TOON                █████████████░░░░░░░    72,771 tokens
   ├─ vs JSON          (−33.1%)               108,806 tokens
   ├─ vs JSON compact  (+5.5%)                 68,975 tokens
   ├─ vs YAML          (−14.2%)                84,780 tokens
   └─ vs XML           (−40.5%)               122,406 tokens

🧾 Semi-uniform event logs  ┊  Tabular: 50%
   │
   TOON                █████████████████░░░   153,211 tokens
   ├─ vs JSON          (−15.0%)               180,176 tokens
   ├─ vs JSON compact  (+19.9%)               127,731 tokens
   ├─ vs YAML          (−0.8%)                154,505 tokens
   └─ vs XML           (−25.2%)               204,777 tokens

🧩 Deeply nested configuration  ┊  Tabular: 0%
   │
   TOON                ██████████████░░░░░░       631 tokens
   ├─ vs JSON          (−31.3%)                   919 tokens
   ├─ vs JSON compact  (+11.9%)                   564 tokens
   ├─ vs YAML          (−6.2%)                    673 tokens
   └─ vs XML           (−37.4%)                 1,008 tokens

──────────────────────────────────── Total ────────────────────────────────────
   TOON                ████████████████░░░░   226,613 tokens
   ├─ vs JSON          (−21.8%)               289,901 tokens
   ├─ vs JSON compact  (+14.9%)               197,270 tokens
   ├─ vs YAML          (−5.6%)                239,958 tokens
   └─ vs XML           (−31.0%)               328,191 tokens
```

#### Flat-Only Track

Datasets with flat tabular structures where CSV is applicable.

```
👥 Uniform employee records  ┊  Tabular: 100%
   │
   CSV                 ███████████████████░    46,954 tokens
   TOON                ████████████████████    49,831 tokens   (+6.1% vs CSV)
   ├─ vs JSON          (−60.7%)               126,860 tokens
   ├─ vs JSON compact  (−36.8%)                78,856 tokens
   ├─ vs YAML          (−50.0%)                99,706 tokens
   └─ vs XML           (−66.0%)               146,444 tokens

📈 Time-series analytics data  ┊  Tabular: 100%
   │
   CSV                 ██████████████████░░     8,388 tokens
   TOON                ████████████████████     9,120 tokens   (+8.7% vs CSV)
   ├─ vs JSON          (−59.0%)                22,250 tokens
   ├─ vs JSON compact  (−35.8%)                14,216 tokens
   ├─ vs YAML          (−48.9%)                17,863 tokens
   └─ vs XML           (−65.7%)                26,621 tokens

⭐ Top 100 GitHub repositories  ┊  Tabular: 100%
   │
   CSV                 ███████████████████░     8,513 tokens
   TOON                ████████████████████     8,745 tokens   (+2.7% vs CSV)
   ├─ vs JSON          (−42.3%)                15,145 tokens
   ├─ vs JSON compact  (−23.7%)                11,455 tokens
   ├─ vs YAML          (−33.4%)                13,129 tokens
   └─ vs XML           (−48.8%)                17,095 tokens

──────────────────────────────────── Total ────────────────────────────────────
   CSV                 ███████████████████░    63,855 tokens
   TOON                ████████████████████    67,696 tokens   (+6.0% vs CSV)
   ├─ vs JSON          (−58.8%)               164,255 tokens
   ├─ vs JSON compact  (−35.2%)               104,527 tokens
   ├─ vs YAML          (−48.2%)               130,698 tokens
   └─ vs XML           (−64.4%)               190,160 tokens
```

<details>
<summary><strong>Show detailed examples</strong></summary>

#### 📈 Time-series analytics data

**Savings:** 13,130 tokens (59.0% reduction vs JSON)

**JSON** (22,250 tokens):

```json
{
  "metrics": [
    {
      "date": "2025-01-01",
      "views": 5715,
      "clicks": 211,
      "conversions": 28,
      "revenue": 7976.46,
      "bounceRate": 0.47
    },
    {
      "date": "2025-01-02",
      "views": 7103,
      "clicks": 393,
      "conversions": 28,
      "revenue": 8360.53,
      "bounceRate": 0.32
    },
    {
      "date": "2025-01-03",
      "views": 7248,
      "clicks": 378,
      "conversions": 24,
      "revenue": 3212.57,
      "bounceRate": 0.5
    },
    {
      "date": "2025-01-04",
      "views": 2927,
      "clicks": 77,
      "conversions": 11,
      "revenue": 1211.69,
      "bounceRate": 0.62
    },
    {
      "date": "2025-01-05",
      "views": 3530,
      "clicks": 82,
      "conversions": 8,
      "revenue": 462.77,
      "bounceRate": 0.56
    }
  ]
}
```

**TOON** (9,120 tokens):

```
metrics[5]{date,views,clicks,conversions,revenue,bounceRate}:
  2025-01-01,5715,211,28,7976.46,0.47
  2025-01-02,7103,393,28,8360.53,0.32
  2025-01-03,7248,378,24,3212.57,0.5
  2025-01-04,2927,77,11,1211.69,0.62
  2025-01-05,3530,82,8,462.77,0.56
```

---

#### ⭐ Top 100 GitHub repositories

**Savings:** 6,400 tokens (42.3% reduction vs JSON)

**JSON** (15,145 tokens):

```json
{
  "repositories": [
    {
      "id": 28457823,
      "name": "freeCodeCamp",
      "repo": "freeCodeCamp/freeCodeCamp",
      "description": "freeCodeCamp.org's open-source codebase and curriculum. Learn math, programming,…",
      "createdAt": "2014-12-24T17:49:19Z",
      "updatedAt": "2025-10-28T11:58:08Z",
      "pushedAt": "2025-10-28T10:17:16Z",
      "stars": 430886,
      "watchers": 8583,
      "forks": 42146,
      "defaultBranch": "main"
    },
    {
      "id": 132750724,
      "name": "build-your-own-x",
      "repo": "codecrafters-io/build-your-own-x",
      "description": "Master programming by recreating your favorite technologies from scratch.",
      "createdAt": "2018-05-09T12:03:18Z",
      "updatedAt": "2025-10-28T12:37:11Z",
      "pushedAt": "2025-10-10T18:45:01Z",
      "stars": 430877,
      "watchers": 6332,
      "forks": 40453,
      "defaultBranch": "master"
    },
    {
      "id": 21737465,
      "name": "awesome",
      "repo": "sindresorhus/awesome",
      "description": "😎 Awesome lists about all kinds of interesting topics",
      "createdAt": "2014-07-11T13:42:37Z",
      "updatedAt": "2025-10-28T12:40:21Z",
      "pushedAt": "2025-10-27T17:57:31Z",
      "stars": 410052,
      "watchers": 8017,
      "forks": 32029,
      "defaultBranch": "main"
    }
  ]
}
```

**TOON** (8,745 tokens):

```
repositories[3]{id,name,repo,description,createdAt,updatedAt,pushedAt,stars,watchers,forks,defaultBranch}:
  28457823,freeCodeCamp,freeCodeCamp/freeCodeCamp,"freeCodeCamp.org's open-source codebase and curriculum. Learn math, programming,…","2014-12-24T17:49:19Z","2025-10-28T11:58:08Z","2025-10-28T10:17:16Z",430886,8583,42146,main
  132750724,build-your-own-x,codecrafters-io/build-your-own-x,Master programming by recreating your favorite technologies from scratch.,"2018-05-09T12:03:18Z","2025-10-28T12:37:11Z","2025-10-10T18:45:01Z",430877,6332,40453,master
  21737465,awesome,sindresorhus/awesome,😎 Awesome lists about all kinds of interesting topics,"2014-07-11T13:42:37Z","2025-10-28T12:40:21Z","2025-10-27T17:57:31Z",410052,8017,32029,main
```

</details>

<!-- /automd -->