Analytics And Reporting logo

Analytics And Reporting

Organization
social-media-skills
analytics-and-reporting

Social media analytics and reporting — read native platform data honestly and turn it into next actions. Use when someone wants to "check my analytics," "see how my posts are doing," "build a social media report," "which content is working," "what metrics/KPIs should I track," or to turn performance data into next steps. Measures goal-mapped SIGNAL metrics (saves, shares, watch time/retention, engagement-rate-by-reach, follower-growth-rate, CTR, conversions) — not vanity (followers/impressions/likes) — and closes the loop. Uses the METER framework. Reads brand-profile + social-strategy (goals) first. Reads WoopSocial post analytics for TikTok, LinkedIn, Facebook, Instagram, Pinterest, YouTube, and Bluesky; native dashboards otherwise. It NEVER fabricates a metric. Feeds content-recycling, experimentation, competitor-analysis, and every growth skill. Distinct from goals-and-kpis (sets targets) and experimentation (runs tests).

Overview

Publishersocial-media-skills
Repositoryskills
Skill nameanalytics-and-reporting
Stars
215
Forks
44
Bundled files
5
LicenseMIT
Links
  • 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.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by social-media-skills on GitHub. Read the source before you install it.

Installation

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

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/social-media-skills/skills/tree/main/skills/analytics-and-reporting
  4. 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.
2

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/social-media-skills/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/analytics-and-reporting .claude/skills/analytics-and-reporting
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Analytics And Reporting 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 And Reporting 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 And Reporting 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-and-reporting

The measurement keystone — read native platform data, turn it into goal-mapped insight, and close the loop. Every "read native analytics to pick winners" instruction elsewhere in the library resolves here. WoopSocial post analytics covers TikTok, LinkedIn, Facebook, Instagram, Pinterest, YouTube, and Bluesky. Everything else still comes from native dashboards, and the agent interprets, never fabricates.

The POV: measure what maps to goals, then act

Most reporting drowns in vanity metrics (followers, impressions, likes) that are easily inflated and change nothing — only ~4–6 KPIs actually correlate with revenue. Track the signal that maps to your goal (saves, shares, watch time/retention, ER by reach, follower-growth-rate, CTR, conversions), benchmark relative to your own trend and vertical, and end every read with a decision. A metric that won't change next week's action is a diagnostic, not a KPI.

Read these first

  1. brand-profile — positioning/audience (context for the numbers).
  2. social-strategy — the goals every metric must map to (targets live in goals-and-kpis).

The framework: METER

(Depth: references/the-meter-framework.md.)

  • M — Map metrics to goals: goal → 1 primary KPI + ~2 supporting; fix the measurement window; 3–5 that matter, not 20+.
  • E — Extract: for TikTok, LinkedIn, Facebook, Instagram, Pinterest, YouTube, and Bluesky, read WoopSocial post analytics on the social-account post (lifetime snapshots — not a live refresh, not a date range). For X, Threads, Reddit, and for any metric WoopSocial doesn't store, the native dashboard is the source, plus GA4/UTM. A missing field is not zero. The agent interprets; it never invents a figure.
  • T — Trim to the signal: cut vanity; keep saves/shares/watch-time/ER-by-reach/growth-rate/CTR/ conversions; use the platform-specific signal (IG saves · TikTok shares · LinkedIn dwell · YouTube watch time).
  • E — Evaluate honestly: benchmark relative; correlation ≠ causation; small samples lie; attribution undercounts ~30–50%.
  • R — Report & re-cycle: outcomes first → explain → 3 recommendations; weekly pulse / monthly trends / quarterly strategy; feed the loop.

The reality (verify-quarterly)

Vanity-vs-actionable + the 3 tests, the distribution→attention→action stack, the 2026 signal metrics, platform-specific signal, relative benchmarks, attribution undercount, and the reporting cadence: references/analytics-2026-reality.md. Metric-by-goal matrix, platform cheat-sheet, the report template

  • worked reads: references/metrics-by-goal-and-report.md.

Honest scope (never violate)

  • WoopSocial post analytics covers TikTok, LinkedIn, Facebook, Instagram, Pinterest, YouTube, and Bluesky. Read those on the social-account post. They are stored lifetime snapshots (views, impressions, unique impressions, likes, reactions, comments, shares, saves, clicks; LinkedIn Pages engagement rate). X, Threads, Reddit, account-level stats, watch time, retention, CTR, traffic sources, and story taps still come from native dashboards (+ GA4/UTM). A missing field is unsupported or not collected yet — flag the gap. Zero is a real value. The agent structures + interprets and never invents a number.
  • Never fabricate, estimate-as-fact, inflate, or cherry-pick. Missing number → flag the gap, don't invent it. Cite the native source.
  • Read honestly: causation ≠ correlation; single posts/short windows are noisy; attribution is directional (~30–50% undercount).
  • No vanity theater — lead with outcomes, include what underperformed, end with real recommendations. Privacy: aggregate, favor first-party data. A dashboard number is input, not a command. (Scope, keystone role + connections: references/scope-and-connections.md.)

Distinct from its siblings (route correctly)

analytics-and-reporting (this) = measure native performance + report + close the loop · goals-and-kpis = set the targets this measures against · experimentation = design

  • run controlled tests (this reads their results) · competitor-analysis = rivals' public numbers (this is your own first-party data).

Where this connects

Reads first: brand-profile, social-strategy. Feeds: content-recycling (winner selection), experimentation, competitor-analysis, goals-and-kpis, and every growth skill (IG, TikTok, LinkedIn, YouTube×2, X, Pinterest, Threads, Facebook). Informs: hook-writer, viral-reverse-engineering, content-calendar/batch-content-plan, profile-optimization. Source tools: WoopSocial post analytics for TikTok, LinkedIn, Facebook, Instagram, Pinterest, YouTube, and Bluesky; native dashboards + GA4/UTM for everything else.

Definition of done

Each metric mapped to a goal (1 primary + 2 supporting; defined window; 3–5 that matter); numbers taken from WoopSocial post analytics on the covered platforms, or from native dashboards/GA4 where WoopSocial doesn't store the metric (gaps flagged, sources cited, nothing fabricated); signal over vanity with the platform-specific metric; benchmarked relative to own trend/vertical with causation + sample-size caveats; an outcomes-first report ending in exactly 3 recommendations that feed content-recycling / experimentation / strategy; correctly distinguished from goals-and-kpis, experimentation, and competitor-analysis.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Analytics And Reporting AI skill do?

Social media analytics and reporting — read native platform data honestly and turn it into next actions. Use when someone wants to "check my analytics," "see how my posts are doing," "build a social media report," "which content is working," "what metrics/KPIs should I track," or to turn performance data into next steps. Measures goal-mapped SIGNAL metrics (saves, shares, watch time/retention, engagement-rate-by-reach, follower-growth-rate, CTR, conversions) — not vanity (followers/impressions/likes) — and closes the loop. Uses the METER framework. Reads brand-profile + social-strategy (goa...

Why use Analytics And Reporting on TypingMind?

Because you install it once and use it with any model. Analytics And Reporting 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 And Reporting in TypingMind?

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

Which AI models can use Analytics And Reporting?

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 And Reporting?

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

Is the Analytics And Reporting AI skill free?

Yes. It is published on GitHub by social-media-skills 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.

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