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Agent skill

Algo Social Engagement

by asgard-ai-platform in asgard-ai-platform/skills

Calculate and benchmark social media engagement rates across platforms and variants.

MITAuto-check passedProduct & Project Management

Install Algo Social Engagement

skills CLI
$ npx skills add asgard-ai-platform/skills --skill algo-social-engagement -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install asgard-ai-platform/skills algo-social-engagement --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/algo-social-engagement .claude/skills/algo-social-engagement && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
algo-social-engagement
GitHub stars
242
Token cost
~1.1k tokens
SKILL.md length
370 words
Files
4 (incl. references)
Skills in repo
207
Repo updated
First seen
Licence
MIT

At a glance

Calculate and benchmark social media engagement rates across platforms and variants.

  • Works in 4 steps: Input Validation → Core Algorithm → Verification → …
  • The user needs to compute engagement metrics
  • SKILL.md covers Overview, When to Use, Algorithm and Output Format, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Algo Social Engagement is an agent skill from asgard-ai-platform/skills. Calculate and benchmark social media engagement rates across platforms and variants. Use this skill when the user needs to compute engagement metrics, compare performance across accounts or posts, or set engagement benchmarks — even if they say 'what is my engagement rate', 'benchmark engagement', or 'social media KPIs'.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `examples/sample_scenario.md`, `references/platform-benchmarks.md` and `references/weighted-engagement.md`).

It sits in Product & Project Management, covering Product metrics and OKRs and executive reporting. The repository describes itself as: 301 open-source coding agent skills across 22 domains — methodology, judgment & gotchas packaged as Claude Agent Skills for the Asgard AI Platform. The licence is MIT.

When your agent uses it

  • The user needs to compute engagement metrics
  • Compare performance across accounts
  • Set engagement benchmarks — even if they say what is my engagement rate
  • Benchmark engagement

Example prompts

  • “what is my engagement rate”
  • “benchmark engagement”
  • “social media KPIs”
  • “/algo-social-engagement”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Input Validation
  2. Core Algorithm
  3. Verification
  4. Output

What it can do on your machine

Read from SKILL.md and the folder at commit 4e7f4f8. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are json).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Algo Social Engagement loads about 1.1k tokens when it runs, and up to ~6k if it reads all its reference files. Until then it costs about 86 tokens; SKILL.md has 370 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~86
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 370 words, ~1,054 tokens.

Download SKILL.mdSave it as .claude/skills/algo-social-engagement/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
algo-social-engagement
description
Calculate and benchmark social media engagement rates across platforms and variants. Use this skill when the user needs to compute engagement metrics, compare performance across accounts or posts, or set engagement benchmarks — even if they say 'what is my engagement rate', 'benchmark engagement', or 'social media KPIs'.
metadata.category
WP-38 社群演算法
metadata.tags
social-media, engagement-rate, analytics, benchmarking

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
  1. Sum engagements per post: likes + comments + shares (+ saves, clicks if available)
  2. Weight engagements if desired: share=3×, comment=2×, like=1× (shares indicate higher commitment)
  3. Divide by chosen denominator (reach preferred, followers as fallback)
  4. 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.

Output Format

json
{
  "metrics": {"avg_er_by_reach": 3.2, "avg_er_by_followers": 1.8, "median_er": 2.9, "top_post_er": 8.5},
  "benchmark": {"platform": "instagram", "industry": "fashion", "benchmark_er": 2.5, "percentile": 72},
  "metadata": {"posts_analyzed": 30, "period": "2025-Q1", "denominator": "reach"}
}

Examples

Sample I/O

Input: Post: 150 likes, 20 comments, 5 shares, reach=5000 Expected: ER by reach = (150+20+5)/5000 × 100% = 3.5%

Show full SKILL.md (154 more words)Show less
Edge Cases
InputExpectedWhy
Reach = 0Undefined, skip postCan't divide by zero
Boosted/paid postSeparate from organicPaid reach inflates denominator, deflates ER
Viral outlier (10x avg)Flag, analyze separatelySkews averages

Gotchas

  • Platform algorithm changes: Instagram's algorithm shifts regularly. Historical ER benchmarks become outdated. Use rolling 90-day benchmarks.
  • Vanity metric trap: High ER doesn't mean business impact. 1000 likes on a meme ≠ 10 link clicks on a product post. Track meaningful engagements.
  • Story/Reel metrics differ: Story engagement (taps, replies) and Reel engagement (plays, shares) need different formulas than feed posts. Don't mix.
  • Follower-based ER is noisy: Not all followers see each post (reach < followers). ER by followers underestimates true engagement among those who saw the post.
  • Comparing across account sizes: Smaller accounts naturally have higher ER by followers. Normalize or segment by account size for fair comparison.

References

  • For platform-specific benchmark data, see references/platform-benchmarks.md
  • For weighted engagement scoring models, see references/weighted-engagement.md

© asgard-ai-platform, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (references) in algo-social-engagement of asgard-ai-platform/skills.

  • SKILL.md
  • examples/sample_scenario.md
  • references/platform-benchmarks.md
  • references/weighted-engagement.md

Open the folder on GitHubat commit 4e7f4f8

Compare with similar skills

Algo Social Engagement next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Algo Social Engagement compared with similar skills
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Algo Social Engagement this skillasgard-ai-platform/skills242—~1.1kAutomated safety check: PassMIT
Analytics Productsickn33/agentic-awesome-skills47k2 repos~2.8kAutomated safety check: PassMIT
Metricsmenkesu/awesome-pm-skills434—~5kAutomated safety check: PassCustom licence
Kpi Tree Builderrevfactory/harness-1001.3k—~1.1kAutomated safety check: PassApache-2.0
Product Metrics Dashboard Designphuryn/pm-skills27k—~1.3kAutomated safety check: PassMIT
Prd V03 Outcome Definitionmattgierhart/PRD-driven-context-engineering180—~1.9kAutomated safety check: PassMIT

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Categories

Questions about Algo Social Engagement

What does Algo Social Engagement do?

Calculate and benchmark social media engagement rates across platforms and variants. Algo Social Engagement is an agent skill from asgard-ai-platform/skills. Calculate and benchmark social media engagement rates across platforms and variants.

When should I use Algo Social Engagement?

Algo Social Engagement fits situations like: the user needs to compute engagement metrics; compare performance across accounts; set engagement benchmarks — even if they say what is my engagement rate; benchmark engagement.

How do I install Algo Social Engagement in Claude Code?

Run `npx skills add asgard-ai-platform/skills --skill algo-social-engagement -a claude-code`. Or copy the skill folder (algo-social-engagement in asgard-ai-platform/skills) into .claude/skills/algo-social-engagement in your project. Claude Code loads it when a task matches its description.

How do I install Algo Social Engagement in Codex?

Run `npx skills add asgard-ai-platform/skills --skill algo-social-engagement -a codex`. Or copy the skill folder (algo-social-engagement in asgard-ai-platform/skills) into .agents/skills/algo-social-engagement in your project. Codex loads it when a task matches its description.

Can I use Algo Social Engagement in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add asgard-ai-platform/skills --skill algo-social-engagement -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/algo-social-engagement, .gemini/skills/algo-social-engagement, .github/skills/algo-social-engagement and .opencode/skills/algo-social-engagement in your project.

What does Algo Social Engagement need to run?

SKILL.md names no scripts, command-line tools or credentials: Algo Social Engagement is instructions for the agent only.

Does Algo Social Engagement access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Algo Social Engagement safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Algo Social Engagement use?

Algo Social Engagement is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Algo Social Engagement use?

About 1.1k tokens (SKILL.md is roughly 4.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.9k tokens, read only when the agent opens those files.

What are the alternatives to Algo Social Engagement?

Skills that share tags, products or a category with Algo Social Engagement: Analytics Product (sickn33/agentic-awesome-skills, 47k stars), Metrics (menkesu/awesome-pm-skills, 434 stars), Kpi Tree Builder (revfactory/harness-100, 1.3k stars) and Product Metrics Dashboard Design (phuryn/pm-skills, 27k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Algo Social Engagement?

asgard-ai-platform (a GitHub organization) maintains it in asgard-ai-platform/skills, which has 242 GitHub stars. The repository holds 207 skills in this directory. The repository was last updated on June 6, 2026.

Source: asgard-ai-platform/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.