name: specstory-yak
description: Analyze your SpecStory AI coding sessions in .specstory/history for yak shaving - when your initial goal got derailed into rabbit holes. Run when user says "analyze my yak shaving", "check for rabbit holes", "how distracted was I", or "yak shave score".
license: Apache-2.0
metadata:
author: specstory
version: "1.0.0"
argument-hint: "[days|date-range]"

Specstory Yak Shave Analyzer

Analyzes your .specstory/history to detect when coding sessions drifted off track from their original goal. Produces a "yak shave score" for each session.

How It Works

1. Parses specstory history files from a date range (or all recent sessions)
2. Extracts the initial user intent from the first message
3. Tracks domain shifts: file references, tool call patterns, goal changes
4. Scores each session from 0 (laser focused) to 100 (maximum yak shave)
5. Summarizes your worst offenders and patterns

What Is Yak Shaving?

"I need to deploy my app, but first I need to fix CI, but first I need to update Node, but first I need to fix my shell config..."

Yak shaving is when you start with Goal A but end up deep in unrelated Task Z. This skill detects that pattern in your AI coding sessions.

Usage

Slash Command

When invoked via /specstory-yak, interpret the user's natural language:

Direct Script Usage

python /path/to/skills/specstory-yak/scripts/analyze.py [options]

Arguments:
- --days N - Analyze last N days (default: 7)
- --from DATE - Start date (YYYY-MM-DD)
- --to DATE - End date (YYYY-MM-DD)
- --path PATH - Path to .specstory/history (auto-detects if not specified)
- --top N - Show top N worst yak shaves (default: 5)
- --json - Output as JSON
- --verbose - Show detailed analysis
- --by-mtime - Filter by file modification time instead of filename date
- -o, --output FILE - Write report to file (auto-adds .md or .json extension)

Examples:

# Analyze last 7 days
python scripts/analyze.py

# Analyze last 30 days, show top 10
python scripts/analyze.py --days 30 --top 10

# Analyze specific date range
python scripts/analyze.py --from 2026-01-01 --to 2026-01-28

# Filter by when files were modified (not session start time)
python scripts/analyze.py --days 7 --by-mtime

# JSON output for further processing
python scripts/analyze.py --days 14 --json

# Save report to a markdown file
python scripts/analyze.py --days 90 -o yak-report.md

# Save JSON to a file
python scripts/analyze.py --days 30 --json -o yak-data.json

Output

Yak Shave Report (2026-01-21 to 2026-01-28)
==========================================

Sessions analyzed: 23
Average yak shave score: 34/100

Top Yak Shaves:
---------------
1. [87/100] "fix button alignment" (2026-01-25)
   Started: CSS fix for button
   Ended up: Rewriting entire build system
   Domain shifts: 4 (ui -> build -> docker -> k8s)

2. [72/100] "add logout feature" (2026-01-23)
   Started: Add logout button
   Ended up: Refactoring auth system + session management
   Domain shifts: 3 (ui -> auth -> database)

3. [65/100] "update readme" (2026-01-22)
   Started: Documentation update
   Ended up: CI pipeline overhaul
   Domain shifts: 2 (docs -> ci -> testing)

Most Focused Sessions:
----------------------
1. [5/100] "explain auth flow" (2026-01-26) - Pure analysis, no drift
2. [8/100] "fix typo in config" (2026-01-24) - Quick surgical fix

Patterns Detected:
------------------
- You yak shave most on: UI tasks (avg 58/100)
- Safest task type: Code review/explanation (avg 12/100)
- Peak yak shave hours: 11pm-2am (avg 71/100)

Scoring Methodology

The yak shave score (0-100) is computed from:

Score interpretation:
- 0-20: Laser focused
- 21-40: Minor tangents
- 41-60: Moderate drift
- 61-80: Significant yak shaving
- 81-100: Epic rabbit hole

Present Results to User

IMPORTANT: After running the analyzer script, you MUST add a personalized LLM-generated summary at the very top of your response, BEFORE showing the raw report output.

LLM Summary Guidelines

Generate a 3-5 sentence personalized commentary that:

1. Opens with a verdict - A witty one-liner about the overall state (e.g., "Your coding sessions this week were... an adventure." or "Remarkably disciplined! Someone's been taking their focus vitamins.")

2. Calls out the highlight - Reference the most notable session specifically:

3. If high yak shave: "That January 25th button fix that somehow became a Kubernetes migration? Chef's kiss of scope creep."

4. If low yak shave: "Your January 26th auth flow explanation was surgical - in and out, no detours."

5. Identifies a pattern - Note any recurring theme:

6. "You seem to yak shave most when starting with UI tasks"
7. "Late night sessions are your danger zone"

8. "Your refactoring sessions tend to stay focused"

9. Ends with actionable advice or a joke - Either:

10. A practical tip: "Consider time-boxing those 'quick CSS fixes' - they have a 73% yak shave rate"
11. Or a joke: "At this rate, your next typo fix will result in a complete rewrite of the Linux kernel"

Example LLM Summary

## 🐃 Your Yak Shave Analysis

Well, well, well. You came to fix buttons and left having rewritten half the
infrastructure. Your average yak shave score of 47/100 puts you firmly in
"classic developer behavior" territory.

The standout? That January 25th session where a CSS alignment fix somehow
evolved into a full Kubernetes deployment overhaul. Four domain shifts later,
you probably forgot what a button even looks like.

Pattern I noticed: Your UI tasks have a 58% higher yak shave rate than your
code review sessions. Maybe start labeling those "quick UI fixes" as
"potential 3-hour adventures" in your calendar.

Here's the full breakdown:

Then show the raw report output below your summary.

What to Highlight

After your summary, when presenting the raw results:
1. The worst offenders with before/after comparison
2. Patterns in when/what causes yak shaving
3. Actionable insight - what task types to watch out for