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

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

User says	Script args
`/specstory-yak`	`--days 7` (default)
`/specstory-yak last 30 days`	`--days 30`
`/specstory-yak this week`	`--days 7`
`/specstory-yak top 10`	`--top 10`
`/specstory-yak january`	`--from 2026-01-01 --to 2026-01-31`
`/specstory-yak from jan 15 to jan 20`	`--from 2026-01-15 --to 2026-01-20`
`/specstory-yak by modification time`	`--by-mtime`
`/specstory-yak last 14 days as json`	`--days 14 --json`
`/specstory-yak save to yak-report.md`	`-o yak-report.md`
`/specstory-yak last 90 days output to report`	`--days 90 -o report.md`

Direct Script Usage

bash

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:

bash

# 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:

Factor	Weight	Description
Domain shifts	40%	How many times file references jumped domains
Goal completion	25%	Did the original stated goal get completed?
Session length ratio	20%	Length vs. complexity of original ask
Tool type cascade	15%	Read->Search->Edit->Create->Deploy escalation

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:

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.")
Calls out the highlight - Reference the most notable session specifically:
- If high yak shave: "That January 25th button fix that somehow became a Kubernetes migration? Chef's kiss of scope creep."
- If low yak shave: "Your January 26th auth flow explanation was surgical - in and out, no detours."
Identifies a pattern - Note any recurring theme:
- "You seem to yak shave most when starting with UI tasks"
- "Late night sessions are your danger zone"
- "Your refactoring sessions tend to stay focused"
Ends with actionable advice or a joke - Either:
- A practical tip: "Consider time-boxing those 'quick CSS fixes' - they have a 73% yak shave rate"
- 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:

The worst offenders with before/after comparison
Patterns in when/what causes yak shaving
Actionable insight - what task types to watch out for

specstory-yak

NPX Install

Tags

SKILL.md Content