> ## Documentation Index
> Fetch the complete documentation index at: https://docs.akta.pro/llms.txt
> Use this file to discover all available pages before exploring further.

# Use cases

### Recent posts snapshot

Fetch a company's most recent social posts with their content type and top-line engagement.

<CodeGroup>
  ```bash cURL theme={null}
  curl -G "https://api.akta.pro/api/v1/company/posts/" \
    --data-urlencode "company=https://canva.com" \
    --data-urlencode "limit=10" \
    -H "x-api-key: your api key"
  ```

  ```python Python theme={null}
  import requests

  url = "https://api.akta.pro/api/v1/company/posts"

  headers = {"x-api-key": "your api key"}
  params = {"company": "https://canva.com", "limit": 10}

  response = requests.get(url, headers=headers, params=params)
  print(response.text)
  ```
</CodeGroup>

**Sample response:**

```json expandable theme={null}
{
  "count": 406,
  "limit": 10,
  "offset": 0,
  "data": {
    "uuid": "00000jw-canva",
    "posts": [
      {
        "id": "WXFWCQ1N6K",
        "content_type": "image",
        "link": "https://www.linkedin.com/feed/update/urn:li:activity:7478764514239315969/",
        "posted_date": "2026-07-03 11:00:08.920000+00:00",
        "is_paid": false,
        "reposted": false,
        "engagement_metrics": {
          "total_reaction_count": 1315,
          "like_count": 1029,
          "empathy_count": 214,
          "praise_count": 16,
          "appreciation_count": 0,
          "interest_count": 2,
          "funny_count": 40,
          "comments_count": 0,
          "reposts_Count": 65
        },
        "text_content": "The growth? We love to see it. 🤩 What did your early designs look like?",
        "post_classification": "Other",
        "author": {},
        "mentions": []
      }
    ]
  },
  "credits_consumed": 1
}
```

### Content mix by classification and format

Break down what a company posts about (`post_classification`) and in what format (`content_type`) to profile its content strategy.

```python theme={null}
import requests
from collections import Counter

HEADERS = {"x-api-key": "<YOUR_API_KEY>"}

resp = requests.get(
    "https://api.akta.pro/api/v1/company/posts",
    headers=HEADERS,
    params={"company": "canva.com", "limit": 100}
).json()

posts = resp["data"]["posts"]

by_theme  = Counter(p["post_classification"] for p in posts)
by_format = Counter(p["content_type"] for p in posts)

print("Content by theme:")
for theme, n in by_theme.most_common():
    print(f"  {theme:<24} {n}")

print("\nContent by format:")
for fmt, n in by_format.most_common():
    print(f"  {fmt:<18} {n}")
```

### Engagement performance analysis

Rank posts by engagement to find what resonates, and compare average engagement across themes or formats.

```python theme={null}
import requests
from collections import defaultdict

HEADERS = {"x-api-key": "<YOUR_API_KEY>"}

resp = requests.get(
    "https://api.akta.pro/api/v1/company/posts",
    headers=HEADERS,
    params={"company": "canva.com", "limit": 100}
).json()

posts = resp["data"]["posts"]

def total_engagement(p):
    m = p["engagement_metrics"]
    return m["total_reaction_count"] + m["comments_count"] + m["reposts_Count"]

# Top posts overall
print("Top posts by engagement:")
for p in sorted(posts, key=total_engagement, reverse=True)[:5]:
    print(f"  {total_engagement(p):>6}  [{p['content_type']}]  {p['text_content'][:50]}")

# Average engagement per theme
sums, counts = defaultdict(int), defaultdict(int)
for p in posts:
    sums[p["post_classification"]] += total_engagement(p)
    counts[p["post_classification"]] += 1

print("\nAverage engagement by theme:")
for theme in sorted(sums, key=lambda t: sums[t]/counts[t], reverse=True):
    print(f"  {theme:<24} {sums[theme]/counts[theme]:,.0f}")
```

### Reaction sentiment mix

Beyond raw reaction counts, the breakdown by reaction type hints at *how* an audience responds — celebratory (`praise`), supportive (`empathy`), amused (`funny`), or simply approving (`like`).

```python theme={null}
import requests

HEADERS = {"x-api-key": "<YOUR_API_KEY>"}

resp = requests.get(
    "https://api.akta.pro/api/v1/company/posts",
    headers=HEADERS,
    params={"company": "canva.com", "limit": 50}
).json()

for p in resp["data"]["posts"][:5]:
    m = p["engagement_metrics"]
    total = m["total_reaction_count"] or 1
    funny_share = m["funny_count"] / total * 100
    print(f"{p['text_content'][:45]:<48} funny: {funny_share:4.1f}%  total: {m['total_reaction_count']}")
```

### Posting cadence over time

Use `posted_date` to measure how frequently a company posts — a proxy for marketing activity and consistency.

```python theme={null}
import requests
from collections import Counter

HEADERS = {"x-api-key": "<YOUR_API_KEY>"}

resp = requests.get(
    "https://api.akta.pro/api/v1/company/posts",
    headers=HEADERS,
    params={"company": "canva.com", "limit": 100}
).json()

posts = resp["data"]["posts"]

# Posts per month (YYYY-MM from the ISO date)
by_month = Counter(p["posted_date"][:7] for p in posts)

print(f"{'Month':<10} {'Posts'}")
print("-" * 18)
for month in sorted(by_month):
    print(f"{month:<10} {by_month[month]}")
```