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u/Super_Birthday_4097

r/letscodecommunity r/CLine r/AIcodingProfessionals r/codex
▲ 8 r/CLine+1 crossposts

I created a token-diet skill that cuts the usage by ~31% on average (54% best case)

I tried caveman skill the other day and I didn't see promised 65% reduction. Turns out the number was purely on the output tokens in chat. On average (of my sessions) it saved 7-8% in total. Since limits have recently become a big problem for most of us, I've decided to create a skill that cuts the tokens not just in chat, but all the unnecessary crap. Here is what it does:

  • Docs / memory / hand-offs / plans / comments - all with minimum words that still say everything
  • Grep before you read, read only the lines you need, never whole files, batch reads, fewer turns, never re-read a just-edited file
  • Batch independent tool calls and stop the moment you have enough to act
  • Less tests. Only key + critical/edge paths. ≤10 per session, never skip money/auth/data-loss
  • Extremely concise replies
  • Build only what's asked (YAGNI). Concise but idiomatic

Repo: https://github.com/Kulaxyz/token-diet

Results:

Session type Output Tokens Bill
Output-heavy (advice, planning, explanation) −81% −54%
Code change + tests (nestjs/nest, 1673 files) −49% −22%
Read-heavy comprehension −30% −17%
Average −53% −31%
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u/Super_Birthday_4097 — 2 days ago
▲ 31 r/letscodecommunity+2 crossposts

I build a self-learning skill for Codex

For context, I spent a good chunk of my career as a senior engineer at Amazon, and one habit that stuck with me is turning anything I do more than twice into something repeatable. When I started using Claude Code/Codex heavily I wrote skills by hand for the workflows it kept fumbling.

But as agents get more autonomous, I'm not babysitting each step anymore, I just hand off a task and come back to a result. The painful patterns still happen, and AI still rediscovers them session after session, but I'm no longer sitting there to catch them and turn them into a skill.

So I built a self-learning skill. Instead of me noticing the pattern and writing the skill, the agent does it itself: when a session involves a hard debugging path, a rediscovered procedure, or a multi-step process it had to figure out, it captures the working approach and the dead-ends it ruled out into a new skill.

Repo (MIT): https://github.com/kulaxyz/self-learning-skills

I'd genuinely value feedback on the approach, especially from people running agents more autonomously: once you've stopped watching every step, how are you capturing what your agent learns mid-session?

u/Super_Birthday_4097 — 5 days ago