Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add donvito/skillsbento --skill social-media-analyzergit clone --depth 1 https://github.com/donvito/skillsbentoWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/donvito/skillsbento/social-media-analyzer)<a href="https://agentmods.dev/skills/donvito/skillsbento/social-media-analyzer"><img src="https://agentmods.dev/badge/skills/donvito/skillsbento/social-media-analyzer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/donvito/skillsbento/social-media-analyzer"><img src="https://agentmods.dev/badge/skills/donvito/skillsbento/social-media-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00081 | $0.00919 |
| Opus 5 | $0.00041 | $0.00460 |
| Sonnet 5 | $0.00016 | $0.00184 |
| Haiku 4.5 | $0.00008 | $0.00092 |
Grade A, and why
social-media-analytics scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- x-twitter-stats-analyzer — 97% identical, 6 lines differ
How it starts
The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Social Media Analytics
Analyze social media data to extract insights and generate strategic recommendations.
Analysis Framework
1. Data Ingestion & Validation
Read the uploaded file and identify available metrics. Common columns:
| Category | Metrics |
|---|---|
| Reach | Impressions, Reach, Views, Profile visits |
| Engagement | Likes, Comments, Replies, Shares, Reposts, Bookmarks, Saves |
| Growth | New followers, Unfollows, Net followers |
| Content | Posts created, Video views, Media views |
Validate data completeness. Note any missing or zero-value columns.
2. Calculate Key Performance Indicators
Engagement Rate = (Total Engagements / Total Impressions) × 100
Follow Conversion = (New Followers / Profile Visits) × 100
Net Growth = New Followers - Unfollows
Likes per Post = Total Likes / Posts Created
Impressions per Post = Total Impressions / Posts Created
3. Temporal Analysis
Identify patterns across time periods:
- Best performing days: Highest impressions, engagement rate, follower growth
- Worst performing days: Lowest metrics, potential issues
- Posting frequency correlation: Compare posts/day vs engagement/post
- Viral content detection: Days with 2x+ average performance
4. Engagement Composition Analysis
Break down total engagements by type:
- Likes (passive appreciation)
- Bookmarks/Saves (high-intent, reference value)
- Replies/Comments (active conversation)
- Shares/Reposts (amplification)
High bookmark rates suggest educational/reference content resonates. High reply rates indicate conversation-driving content.
5. Quality vs Quantity Assessment
Analyze the relationship between posting volume and performance:
for each day:
likes_per_post = likes / posts
impressions_per_post = impressions / posts
# Compare high-volume vs low-volume days
# Often: fewer high-quality posts > many low-quality posts
6. Growth Funnel Analysis
Track the conversion funnel:
Impressions → Engagements → Profile Visits → New Followers
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 119 lines · 81 tokens per session scan A eab6cdc31924
social-media-analytics is a skill published in the GitHub repository donvito/skillsbento (2 stars, last pushed 29d ago), licensed Apache-2.0. It adds 81 tokens to every session and 919 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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