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".
Install with Tessl CLI
npx tessl i github:specstoryai/agent-skills --skill specstory-yak82
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
LLM summary before raw report
Summary before raw report
0%
100%
Verdict one-liner
0%
100%
Specific session callout
0%
100%
Pattern identification
33%
100%
Actionable advice or joke
33%
100%
Uses analyze.py script
0%
100%
3-5 sentence summary length
0%
0%
Data-driven summary
30%
100%
Without context: $0.6279 · 2m 55s · 29 turns · 28 in / 10,121 out tokens
With context: $0.6216 · 2m 4s · 30 turns · 279 in / 7,018 out tokens
Date range and JSON output argument translation
Correct --from date
0%
100%
Correct --to date
0%
100%
JSON flag used
0%
100%
Output saved to file
50%
100%
Output file is valid JSON
100%
100%
JSON has required fields
90%
100%
date_range reflects January
100%
100%
analyze.py invoked
0%
100%
LLM summary present
80%
0%
Without context: $0.2546 · 1m 29s · 8 turns · 9 in / 5,296 out tokens
With context: $0.5238 · 1m 33s · 28 turns · 62 in / 4,913 out tokens
Modification time filter and top-N argument translation
--by-mtime flag used
0%
100%
Correct --top value
0%
100%
Correct --days value
0%
100%
Output saved with -o flag
0%
100%
Output file is markdown
100%
100%
LLM summary before raw output
0%
0%
analyze.py invoked
0%
100%
Without context: $0.6275 · 3m 6s · 24 turns · 21 in / 11,202 out tokens
With context: $0.6221 · 2m 16s · 25 turns · 1,781 in / 7,620 out tokens
Table of Contents
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