Open dataset of what AI coding agents cost: token pricing, per-million rates, and every published figure (prices, percentages, multiples, token counts and durations) as JSON and CSV. Figures are one row each, carrying the sentence they came from. Plus the field notes behind the numbers.
View the Project on GitHub xyzs996/ai-coding-field-notes
AI Features
3 of the 32 write-ups here are tagged AI Features. Every figure quoted below is in the figures table with the sentence it came from.
- 90% — 90% of developers still rely on manual prompt writing, while top performers use Skill Package to automate 80% of repetitive tasks, saving hours weekly. →
- 90% — 90% of beginners fixate on tools (e.g., Pi’s 4 default tools: read/write/edit/bash) instead of defining clear task boundaries. →
- 80% — When the Claude Code team decided to slash 80% of their system prompts, most developers expected the model to lose its edge in complex engineering tasks. →
- 80% — Stripping away that redundant 80% removes the cognitive drag holding the model back, freeing native reasoning capacity and cutting the token burn. →
- 40-second — When an independent developer uses Agency Agents to set up a 40-second response cycle for e-commerce listings, they are building a feedback loop that reads market conditions and adjusts, which is what separates a timed automation from a script on a timer. →
- 40-second — The 40-second number I cannot check. →
All figures, 335 rows
The write-ups
Managing AI Agent skills is not merely about tools; it’s about designing workflows to boost your productivity.
If you run a solo dev shop, the day goes to fragmented feeds, forty open tabs, and backend maintenance that eats the hours meant for product logic.
When the Claude Code team decided to slash 80% of their system prompts, most developers expected the model to lose its edge in complex engineering tasks.
All 32 write-ups