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Analyze Performance

Organization
techwolf-ai
analyze-performance

Analyze engagement patterns across published posts to identify what works. Use when asked to review performance, find successful patterns, or optimize future content.

Overview

Publishertechwolf-ai
Repositoryai-first-toolkit
Skill nameanalyze-performance
Stars
138
Forks
7
Bundled files
Instructions only
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • 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 techwolf-ai on GitHub. Read the source before you install it.

Installation

Install the Analyze Performance 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/techwolf-ai/ai-first-toolkit/tree/main/plugins/content-studio/skills/analyze-performance
  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/techwolf-ai/ai-first-toolkit.git /tmp/ai-first-toolkit
mkdir -p .claude/skills
cp -r /tmp/ai-first-toolkit/plugins/content-studio/skills/analyze-performance .claude/skills/analyze-performance
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Analyze Performance 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 Analyze Performance 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 Analyze Performance 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.

Analyze Content Performance

Identify patterns in high-performing posts to inform future content strategy.

Process

  1. Run ./scripts/print-published.sh linkedin-post to read all published LinkedIn posts
  2. Extract posts that have engagement data (engagement.reactions, engagement.views, etc.)
  3. Analyze patterns across high-performing vs low-performing posts

Analysis Dimensions

Hook Analysis

  • What hook styles correlate with higher engagement?
  • Personal anecdote vs company experience vs surprising data vs news hook?
  • First 210 characters (LinkedIn cutoff) - what patterns work?

Content Characteristics

  • Word count vs engagement correlation
  • Use of concrete examples vs abstract concepts
  • Presence of frameworks or mental models
  • Use of lists/structure vs flowing narrative

Topic Analysis

  • Which tags correlate with higher engagement?
  • Which themes resonate most?
  • Timing patterns (if publishedDate available)

Structural Patterns

  • Opening style (question, statement, story)
  • Closing style (call-to-action, reflection, question)
  • Paragraph length and density

Performance Tiers

Categorize posts by reaction count:

  • High performers: 100+ reactions
  • Medium performers: 30-99 reactions
  • Lower performers: <30 reactions

Output Format

Provide:

  1. Summary statistics - Total posts analyzed, average engagement by tier
  2. Top performers - List highest-engagement posts with their key characteristics
  3. Pattern insights - What distinguishes high vs lower performers?
  4. Recommendations - Actionable suggestions for future content

Example Analysis Output

## Performance Summary
- Posts analyzed: 12 (with engagement data)
- High performers (100+): 3 posts
- Medium performers (30-99): 5 posts
- Lower performers (<30): 4 posts

## Top Performers
1. "Title" - 245 reactions
   - Hook: Personal anecdote
   - Topic: AI productivity
   - Word count: 180

## Key Patterns
- Personal anecdotes in the first sentence correlate with 2x higher engagement
- Posts with concrete examples outperform abstract posts by 40%
- Optimal word count appears to be 150-200 words

## Recommendations
1. Lead with personal or company-specific openings
2. Include at least one specific example or data point
3. Keep total length under 220 words

Notes

  • Only analyze posts with engagement data (skip posts without metrics)
  • Correlation is not causation - note patterns but don't overclaim
  • Consider recency bias - newer posts may still be accumulating engagement

Frequently asked questions

What does the Analyze Performance AI skill do?

Analyze engagement patterns across published posts to identify what works. Use when asked to review performance, find successful patterns, or optimize future content.

Why use Analyze Performance on TypingMind?

Because you install it once and use it with any model. Analyze Performance 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 Analyze Performance in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/techwolf-ai/ai-first-toolkit/tree/main/plugins/content-studio/skills/analyze-performance. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Analyze Performance?

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 Analyze Performance?

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

Is the Analyze Performance AI skill free?

Yes. It is published on GitHub by techwolf-ai 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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