Analytics Dashboard

CommunityPopular

charlie947

analytics-dashboard

Turn a LinkedIn Analytics export into an interactive dark-themed React dashboard plus a written strategic analysis with 5 data-backed content recommendations. Reads every sheet in the export, builds charts for engagement trend, follower growth, post performance scatter, day-of-week heatmap, and audience breakdown. Use this skill whenever the user says "analyse my linkedin", "linkedin analytics", "build my dashboard", "review my performance", or uploads a LinkedIn Analytics export file. Requires the user's LinkedIn Analytics export (xlsx) as input.

Overview

Publisher	charlie947
Repository	`social-media-skills`
Skill name	`analytics-dashboard`
Stars	3.6K
Forks	836
Bundled files	Instructions only
License	MIT
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by charlie947 on GitHub. Read the source before you install it.

Installation

Install the Analytics Dashboard AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/charlie947/social-media-skills.git /tmp/social-media-skills
mkdir -p .claude/skills
cp -r /tmp/social-media-skills/skills/analytics-dashboard .claude/skills/analytics-dashboard

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Analytics Dashboard in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Analytics Dashboard on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Analytics Dashboard is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Analytics Dashboard

Codex and Claude runtime

Use this skill in Codex or Claude with the tools actually available in the current task. AskUserQuestion examples describe the questions, not a required API: use an available question tool within its limits, or ask in chat. Reuse answers and source material already supplied.
Work in the user-selected project. Read its about-me.md, voice.md and relevant brand files before personalised work. Confirm the intended author if files conflict or contain starter defaults. Ask for missing facts or run voice-builder; never inherit the maintainer's identity, accounts or private files.
Resolve bundled references/ relative to this skill folder. For an explicitly requested profile refresh, read and update the canonical about-me.md, voice.md or newsletter-voice.md in place, preserving unrelated user facts and rules. Consumers must reread those canonical files. Use a new filename only for new deliverables that would collide with unrelated existing files. Installation alone never starts an interview or writes files. Do not write persistent learnings unless requested.
Use supplied evidence first. Verify external claims through available search/source tools when needed. If a source or integration is unavailable, name the missing capability and offer supplied text/export input. Never invent facts, first-person experience, metrics or a successful tool run.
Connect only services needed for the chosen route through the user's existing account. Never print credentials or overwrite connections. Drafting, saving and reviewing do not authorise publishing, sending messages or changing accounts.

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. Confirm author, reporting window, units and actual column names before calculating. The following sheets are examples, not a guaranteed export schema:

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

Top-post tables are selected samples, not the account’s entire posting history. Keep their denominators separate from account-wide metrics; do not infer best posting times from daily aggregates. 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

Use a supported React artifact surface or the selected project’s existing React and Recharts setup. If neither is available, provide the computed analysis and React source with preview pending; do not silently install dependencies or claim an interactive dashboard is running. Preview and exercise chart tooltips/resizing before calling it verified. 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
Overall engagement rate (sum of engagements / sum of impressions, for the same reporting window). Zero or missing denominators are unavailable, not zero.
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

Day-of-week heatmap

Average impressions and engagements by day of week
Highlight the strongest days

Audience breakdown (bar charts)

Job titles
Industries
Seniority
Company size
Top locations

Formatting rules

Format numbers: 67K not 67000, 1.2M not 1200000
Total follower count prominent at the top
Responsive layout (works on laptop and large display)
Dark background, high contrast chart colours

Step 4. Written strategic analysis

Below the dashboard, write a concise analysis with these sections:

Performance Summary

Trajectory: growing, plateauing, or declining (use trendlines)
Current engagement rate; compare external benchmarks only with a verified dated source and matching metric definition

Audience-Content Fit

Who the core audience is, based on demographics
Which content topics and formats would resonate
Segments to lean into or away from

Growth Velocity

Average daily follower growth
30, 60, 90 day scenarios at current pace, labelled as extrapolations rather than forecasts
Acceleration or deceleration trends

Day and Timing Strategy

Best days for impressions
Best days for engagement
Optimal posting schedule based on the data

5 Specific Content Recommendations

Each one includes:

Content angle or topic
Why the data supports it
Which audience segment it targets
Evidence and a testable hypothesis, without guaranteed impact

Step 5. Offer the next move

After the analysis:

Want me to draft one of these 5 recommendations as a full post? Call the post-writer or post-formatter skill with the recommendation number.

Rules

Use numbers, not adjectives. "Engagement rate is 2.3%" beats "engagement is healthy".
Keep the analysis direct. No fluff, no filler.
Never invent metrics not present in the export.
Flag data quality issues (missing columns, odd date ranges) instead of silently working around them.
Never use em dashes.
British English unless voice.md specifies otherwise.
Recommend running this monthly. Patterns only surface over time.

Frequently asked questions

What does the Analytics Dashboard AI skill do?

Why use Analytics Dashboard on TypingMind?

Because you install it once and use it with any model. Analytics Dashboard is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Analytics Dashboard in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/charlie947/social-media-skills/tree/main/skills/analytics-dashboard. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Analytics Dashboard?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Analytics Dashboard?

As many as you like. As long as a model supports skills, you can use Analytics Dashboard with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Analytics Dashboard AI skill free?

Yes. It is published on GitHub by charlie947 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Daa Agent

ruvnet

CommunityPopular

Create and adapt Dynamic Agentic Architecture agents that learn and evolve

Data Scientist

Processes and analyzes data with resident-kernel engines (DuckDB, Polars) and one-shot tools. Use for CSV/parquet/JSON analysis, group-by/join/aggregation, time series, distributions, cleaning, or…

Archify

Create polished, validated architecture, workflow, sequence, data-flow, and lifecycle/state diagrams as explorable standalone HTML with inline SVG, dark/light themes, optional trace motion, and…

Ab Testing

When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test…

Analytics Dashboard

Overview