session-skill-discovery

Name: session-skill-discovery
Availability: InStock
Author: ManifoldLogic

Analyze Claude Code session transcript logs to identify reusable skill candidates.

Updated Nov 9, 2025

Search Tags:#pattern recognition #skill discovery #session analysis #skill validation #transcript parsing

Ready to run

npx skills add https://github.com/manifoldlogic/claude-code-plugins --skill session-skill-discovery

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: session-skill-discovery
Source: https://github.com/manifoldlogic/claude-code-plugins/tree/main/plugins/sdd/skills/session-skill-discovery
Command: npx skills add https://github.com/manifoldlogic/claude-code-plugins --skill session-skill-discovery

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires json, re, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps analyze session transcript logs to identify reusable skill candidates across multiple tickets and sessions, reducing the need for repetitive tasks and improving efficiency.

Core Features & Use Cases

Transcript Analysis: Analyze session transcript logs to identify patterns and reusable skill candidates.
Cross-Ticket Patterns: Identify patterns that span multiple tickets and projects.
Quality Gates: Apply quality gates to ensure only high-quality skill candidates are identified.
Skill Validation: Validate candidates against existing skill criteria and documentation.
Skill Proposal: Prepare skill proposals for further development and inclusion.

Quick Start

Use the session-skill-discovery skill to analyze session transcript logs and identify reusable skill candidates.

Frequently Asked Questions about session-skill-discovery

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I identify reusable skill candidates from session transcript logs?▼

You can identify reusable skill candidates from session transcript logs by parsing JSONL session data with Python scripts to extract procedural patterns across multiple tickets and sessions, then applying quality gates to validate the results.

What is session analysis for skill discovery in Claude Code?▼

Session analysis for skill discovery is the process of examining post-completion Claude Code session transcript logs to detect repetitive procedural patterns that can be extracted and validated into reusable skill candidates.

Do I need Python 3 to parse JSONL transcript logs for pattern recognition?▼

Yes, you need Python 3 along with standard libraries like json and re to parse JSONL transcript logs and execute the pattern recognition scripts required for identifying reusable skill candidates.

How do I validate extracted skill candidates against existing skill criteria?▼

You validate extracted skill candidates by applying built-in quality gates that check the identified procedural patterns against existing skill documentation and criteria to ensure only high-quality candidates are proposed.

Can I analyze patterns across multiple tickets and projects in session transcripts?▼

Yes, you can analyze cross-ticket patterns by running the Python scripts against multiple session transcript logs simultaneously, allowing you to identify procedural patterns that span multiple projects and tickets.

What are the limitations of using regex and JSON parsing for skill discovery?▼

Using regex and JSON parsing for skill discovery is limited to structured post-completion analysis of transcript logs, meaning it cannot perform real-time session monitoring and relies entirely on the procedural patterns captured within the JSONL data.