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some justifications

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Fahad
2025-06-14 19:01:53 +04:00
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@@ -332,6 +332,11 @@ with the best architecture for my project
**Thinking Mode:** Default is `medium` (8,192 tokens). Use `high` for security-critical code (worth the extra tokens) or `low` for quick style checks (saves ~6k tokens).
**Model Recommendation:** This tool particularly benefits from Gemini Pro or Flash models due to their 1M context window,
which allows comprehensive analysis of large codebases. Claude's context limitations make it challenging to see the
"big picture" in complex projects - this is a concrete example where utilizing a secondary model with larger context
provides significant value beyond just experimenting with different AI capabilities.
#### Example Prompts:
```
@@ -350,6 +355,12 @@ I need an actionable plan but break it down into smaller quick-wins that we can
**Thinking Mode:** Default is `medium` (8,192 tokens). Use `high` or `max` for critical releases when thorough validation justifies the token cost.
**Model Recommendation:** Pre-commit validation benefits significantly from models with
extended context windows like Gemini Pro, which can analyze extensive changesets across
multiple files and repositories simultaneously. This comprehensive view enables detection of
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missed when reviewing changes in isolation due to context constraints.
<div align="center">
<img src="https://github.com/user-attachments/assets/584adfa6-d252-49b4-b5b0-0cd6e97fb2c6" width="950">
</div>
@@ -429,6 +440,11 @@ Use zen and perform a thorough precommit ensuring there aren't any new regressio
**Thinking Mode (Extended thinking models):** Default is `medium` (8,192 tokens). Use `high` for complex systems with many interactions or `max` for critical systems requiring exhaustive test coverage.
**Model Recommendation:** Test generation excels with extended reasoning models like Gemini Pro or O3,
which can analyze complex code paths, understand intricate dependencies, and identify comprehensive edge
cases. The combination of large context windows and advanced reasoning enables generation of thorough test
suites that cover realistic failure scenarios and integration points that shorter-context models might overlook.
#### Example Prompts:
**Basic Usage:**