Evo Github Repo Analytics

Community

Zhang-Henry

evo-github-repo-analytics

Generates a community pulse report for a GitHub repository by querying the GitHub Search API for PRs and issues within a date range, computing merge statistics, identifying top contributors, classifying bug reports by label substring, and counting resolved bugs. Use when asked to produce a JSON summary of repository activity.

Overview

Publisher	Zhang-Henry
Repository	`CoEvoSkills`
Skill name	`evo-github-repo-analytics`
Stars	83
Forks	9
Bundled files	1
License	Apache-2.0
Links	Skill source Repository Homepage

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.
1 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by Zhang-Henry on GitHub. Read the source before you install it.

Installation

Install the Evo Github Repo Analytics 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.
https://github.com/Zhang-Henry/CoEvoSkills/tree/main/artifacts/skills/gh-repo-analytics/evo-github-repo-analytics
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/Zhang-Henry/CoEvoSkills/tree/main/artifacts/skills/gh-repo-analytics/evo-github-repo-analytics

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

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/Zhang-Henry/CoEvoSkills.git /tmp/CoEvoSkills
mkdir -p .claude/skills
cp -r /tmp/CoEvoSkills/artifacts/skills/gh-repo-analytics/evo-github-repo-analytics .claude/skills/evo-github-repo-analytics

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

Use it in TypingMind

Enable Evo Github Repo Analytics 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 Evo Github Repo Analytics 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 Evo Github Repo Analytics 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.

GitHub Repository Analytics Skill

This skill fetches PR and issue data from the GitHub Search API and produces a structured JSON report with PR counts, merge statistics, top contributors, bug report counts, and resolved bug counts.

Key Concepts

Cohort selection: PRs/issues are selected by created date range
PR states: merged (has merged_at) vs closed (state=closed, no merged_at) are tracked separately
Merge duration: mergedAt - createdAt in days, averaged and rounded to 1 decimal
Bug detection: Any issue with at least one label containing substring 'bug' (case-insensitive)
Resolved bugs: Bug issues whose closed_at falls within the specified period
Pagination: Handles multi-page results (100 per page)
Top contributor: By PR count, alphabetical tie-break

Usage

python
import sys
sys.path.insert(0, '/app/environment/skills/evo-github-repo-analytics/scripts')
from utils import generate_report, validate_report

# Generate the report
report = generate_report(
    repo='cli/cli',
    start_date='2024-12-01',
    end_date='2024-12-31',
    output_path='/app/report.json'
)

# Validate the output
validate_report('/app/report.json')

Output Format

json
{
  "pr": {
    "total": "<int> PRs created in period",
    "merged": "<int> PRs merged (as of now)",
    "closed": "<int> PRs closed without merge (as of now)",
    "avg_merge_days": "<float> average days from creation to merge, 1 decimal",
    "top_contributor": "<str> GitHub login with most PRs created"
  },
  "issue": {
    "total": "<int> issues created in period",
    "bug": "<int> issues with a label containing 'bug'",
    "resolved_bugs": "<int> bug issues closed during the period"
  }
}

Functions

fetch_all_search_results(repo, item_type, created_range) - Paginated GitHub search
analyze_prs(items) - Compute PR statistics from search results
analyze_issues(items, month_start, month_end) - Compute issue statistics
generate_report(repo, start_date, end_date, output_path) - End-to-end entry point
validate_report(report_path) - Validate output JSON structure and types

Bundled files

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

scripts/utils.py6 KB

Frequently asked questions

What does the Evo Github Repo Analytics AI skill do?

Why use Evo Github Repo Analytics on TypingMind?

Because you install it once and use it with any model. Evo Github Repo Analytics 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 Evo Github Repo Analytics in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Zhang-Henry/CoEvoSkills/tree/main/artifacts/skills/gh-repo-analytics/evo-github-repo-analytics. 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 Evo Github Repo Analytics?

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 Evo Github Repo Analytics?

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

Is the Evo Github Repo Analytics AI skill free?

Yes. It is published on GitHub by Zhang-Henry under the Apache-2.0 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

GitHub Repository Analytics Skill

Key Concepts

Usage

Output Format

Functions

Bundled files

Frequently asked questions

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