session-analyzer

name	session-analyzer
description	Analyzes a session summary and extracts reusable patterns as proposed SKILL.md drafts. Trigger: /analyze-session, or user says "extract patterns", "make this a skill", "what can we reuse from this session".
metadata	{"author":"javi-ai","version":"1.0","tags":["learning","skills","patterns","meta"],"category":"workflow"}
allowed-tools	Read, Write, mcp__plugin_engram_engram__mem_search, mcp__plugin_engram_engram__mem_get_observation

Purpose

After a productive session, extract repeatable patterns and propose them as new SKILL.md files. The user reviews and approves; approved skills are saved to ~/.claude/skills/ via javi-ai propose approve.

---

Extraction Algorithm

Step 1: Load session context

If triggered post-session, load the session summary from Engram:

mem_search(query: "session summary", project: "{project}")

If triggered manually, ask the user: "Share the session summary or describe what pattern you want to capture."

Step 2: Identify patterns

Look for:

• Repeated solutions — same approach used 3+ times in the session
• Non-obvious conventions — decisions that required research or deliberation
• Tool combinations — specific sequences of tools that solved a class of problem
• Anti-patterns avoided — things explicitly NOT done and why

Step 3: For each pattern found, draft a SKILL.md

Use this structure:

---
name: {kebab-case-name}
description: >
  {one-line description of when to use this skill}
  Trigger: {trigger phrases}
metadata:
  author: javi-ai
  version: "0.1-proposed"
  tags: [{relevant tags}]
  category: {workflow|testing|architecture|debugging}
allowed-tools: {tools the skill needs}
---

## Purpose
{why this skill exists — the problem it solves}

## Steps
{numbered steps}

## Rules
1. {critical constraint}
2. {critical constraint}

Step 4: Propose via javi-ai

Save each draft to ~/.javi-ai/proposed/{skill-name}.md and report:

Extracted {N} patterns from session:

1. {skill-name} — {one-line description}
   → Save proposal: javi-ai propose approve {skill-name}
   → Discard: javi-ai propose reject {skill-name}

2. ...

---

Rules

1. Quality over quantity — propose 1-3 high-quality skills, not 10 mediocre ones
2. Specificity wins — "how to configure Biome for Ink projects" beats "linting"
3. Include trigger phrases — a skill with no trigger is never loaded
4. Version as 0.1-proposed — marks it as unreviewed
5. Don't propose duplicates — check existing skills first with mem_search