charlie947/social-media-skills is a collection of markdown instructions that gives AI agents specialized workflows for creating and managing social-media content. It supports a content system spanning LinkedIn, Instagram, Substack, X, and YouTube, with shared voice and context files guiding the individual skills.
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 charlie947/social-media-skills --skill analytics-dashboardgit clone --depth 1 https://github.com/charlie947/social-media-skillsWrote 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/charlie947/social-media-skills/analytics-dashboard)<a href="https://agentmods.dev/skills/charlie947/social-media-skills/analytics-dashboard"><img src="https://agentmods.dev/badge/skills/charlie947/social-media-skills/analytics-dashboard/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/charlie947/social-media-skills/analytics-dashboard"><img src="https://agentmods.dev/badge/skills/charlie947/social-media-skills/analytics-dashboard.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00107 | $0.01096 |
| Opus 5 | $0.00053 | $0.00548 |
| Sonnet 5 | $0.00021 | $0.00219 |
| Haiku 4.5 | $0.00011 | $0.00110 |
Grade A, and why
analytics-dashboard 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 13d 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:
- analytics-dashboard — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analytics Dashboard
CRITICAL: Auto-start on load
When this skill triggers, go straight to Step 1.
Step 1. Get the export file
Ask:
Upload your LinkedIn Analytics export file (xlsx).
Not sure how to get it? Go to LinkedIn Analytics, set your date range (30, 60, or 90 days works well), and click Export in the top right.
Wait for the file upload.
Step 2. Parse the data
Read every sheet in the file. Expect these sheets:
- DISCOVERY: overall impressions and reach
- ENGAGEMENT: daily impressions and engagements over time
- TOP POSTS: top 50 posts, ranked by engagements and by impressions (two tables to merge)
- FOLLOWERS: daily new followers plus total count
- DEMOGRAPHICS: job titles, locations, industries, seniority, company size, top companies
Clean any messy headers. Merge the two TOP POSTS tables (by engagements and by impressions) into one unified dataset per post. De-duplicate.
Step 3. Build the interactive dashboard
Create a single React artifact. Dark theme (background #0f1117), accent colours for charts. Use Recharts for all visualisations.
Include these panels in this order:
Headline metrics (top row cards)
- Total impressions
- Total reach
- Total new followers
- Average daily impressions
- Average daily engagements
- Average engagement rate (engagements / impressions)
- Total posts tracked
Engagement trend (line chart)
- Daily impressions (left y-axis) and engagements (right y-axis) over the full date range
- Highlight the top 3 spike days with markers
Follower growth (area chart)
- Daily new followers
- 7-day moving average trendline overlaid
- Cumulative follower gain
Post performance scatter
- X axis: impressions. Y axis: engagements
- Colour-code posts into four quadrants:
- Stars: high reach + high engagement
- Viral but shallow: high reach + low engagement
- Niche gold: low reach + high engagement
- Underperformers: low reach + low engagement
- Hoverable dots showing post URL and date
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.
- 13d ago First seen · 136 lines · 107 tokens per session scan A 5000eec75f32
analytics-dashboard is a skill published in the GitHub repository charlie947/social-media-skills (3,385 stars, last pushed 13d ago), licensed MIT. It adds 107 tokens to every session and 1,096 once invoked, about $0.0005 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 2026-08-30.
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