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 charlieviettq/awesome-agent-skill --skill algo-social-engagementgit clone --depth 1 https://github.com/charlieviettq/awesome-agent-skillWrote 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/charlieviettq/awesome-agent-skill/algo-social-engagement)<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/algo-social-engagement"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/algo-social-engagement/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/charlieviettq/awesome-agent-skill/algo-social-engagement"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/algo-social-engagement.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 | $0.00066 | $0.00952 |
| Opus 5 | $0.00033 | $0.00476 |
| Sonnet 5 | $0.00013 | $0.00190 |
| Haiku 4.5 | $0.00007 | $0.00095 |
Grade A, and why
"algo-social-engagement" 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 9d 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.
This is a copy
94% identical to algo-social-engagement — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Engagement Rate Calculation
Overview
Engagement rate measures audience interaction relative to reach or audience size. Formula: (reactions + comments + shares) / denominator × 100%. The denominator choice (reach, impressions, followers) significantly affects the result. Computes in O(n) per post set.
When to Use
Trigger conditions:
- Computing engagement metrics for social media reporting
- Benchmarking account or post performance against industry averages
- Comparing content performance across posts or accounts
When NOT to use:
- When evaluating influence holistically (use influence measurement)
- When modeling content spread dynamics (use virality models)
Algorithm
IRON LAW: Engagement Rate Denominator MATTERS
By reach, by impressions, and by followers produce DIFFERENT numbers:
- ER by Reach = engagements / reach × 100% (most accurate, requires analytics access)
- ER by Impressions = engagements / impressions × 100% (always lower than by reach)
- ER by Followers = engagements / followers × 100% (public data, but inflated by non-reaching followers)
ALWAYS specify which variant when reporting or comparing.
Phase 1: Input Validation
Collect per post: likes, comments, shares/retweets, saves (platform-specific), reach or impressions or follower count. Gate: Consistent denominator across all posts being compared.
Phase 2: Core Algorithm
- Sum engagements per post: likes + comments + shares (+ saves, clicks if available)
- Weight engagements if desired: share=3×, comment=2×, like=1× (shares indicate higher commitment)
- Divide by chosen denominator (reach preferred, followers as fallback)
- Compute: per-post ER, average ER across posts, median ER, ER trend over time
Phase 3: Verification
Compare against platform benchmarks. Flag anomalies (ER > 20% likely data error or viral outlier). Gate: Results within plausible range for platform.
Phase 4: Output
Return engagement metrics with benchmarking context.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 87 lines · 66 tokens per session scan A b8ff8df1fbb2
"algo-social-engagement" is a skill published in the GitHub repository charlieviettq/awesome-agent-skill (25 stars, last pushed 1mo ago), licensed MIT. It adds 66 tokens to every session and 952 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to algo-social-engagement, differing in 8 lines, and is treated as a copy.
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