fix(apply-patch): skip initial new-file hunk header emitted by some AI models

[msmhrt]

What & Why

Some AI-generated patches (e.g. from Gemini 2.0 Flash) prepend a full diff hunk header like:

@@ -0,0 +1,2 @@ SectionName

before any + lines. Our parser treats that as invalid, causing parse failures or stray blank lines when adding new files.
This PR makes both the TypeScript and Rust parsers ignore only the first such "new-file" hunk header, then proceed normally.

---

How

TypeScript

• Introduce NEW_FILE_HUNK regex + isFirst flag in parse_add_file()
• Skip the very first header matching that pattern

Rust

• Import regex::Regex
• Apply the same new_file_re + "first" logic in parse_one_hunk()

---

Tests

• TS

  • process_patch – add file skips full hunk headers
  • Regex self-test

• Rust

  • test_add_file_skip_full_hunk_header_with_section
  • test_new_file_hunk_regex

[github-actions]

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[msmhrt]

I have read the CLA Document and I hereby sign the CLA

[bolinfest]

I know we are on the hook for putting up an actual proposal, but we are planning to introduce a concept of "plugins." Accepting a patch format that OpenAI models consider to be invalid output (from its own internal specification) seems like something that should live in a plugin rather in the core of Codex itself.

[msmhrt]

Hi @bolinfest,

Thank you for your comment and for sharing the insight about the planned "plugins" concept. That sounds like an interesting direction for enhancing extensibility in the long run.

Regarding the immediate issue with the apply_patch command, I wanted to provide a bit more context on the problems it's causing. Specifically, there are two main concerns:

1. Strict Input Requirements: The command currently lacks tolerance for patch formats that slightly deviate from the expected structure, even if they are conceptually valid.
2. Cryptic Error Messages: The error messages provided upon failure are often unclear and don't offer enough information to pinpoint the exact issue, making debugging very difficult, especially for an AI agent.

These issues combined create a significant usability problem. AI agents interacting with apply_patch often get stuck in inefficient loops: they generate a patch, it fails (sometimes for unclear reasons), the error message isn't helpful for correction, leading to repeated attempts, wasted token consumption, and ultimately, task failure. This severely impacts the ability to reliably use Codex CLI for automating patch applications right now.

I understand from your comment that you envision functionality like accepting varied patch formats potentially living in a plugin, rather than the core. That makes sense from an architectural perspective to keep the core clean.

However, given the significant usability hurdle the current behavior presents, and acknowledging that developing the plugin system might take time, I'd like to ask if exploring any interim improvements to the core apply_patch command might be possible. Would you be open to considering merging the proposed tolerance improvements PR #695, perhaps as a temporary measure or even optionally, just to alleviate the immediate pain point and make the tool more usable in the meantime? The current strictness and poor error feedback are major blockers.

Looking ahead to the plugin system, I think it's a great opportunity. To ensure plugins (and potentially core components too) work effectively in collaboration with AI agents, I'd strongly suggest considering these two principles as part of the design guidelines or best practices:

1. Robustness/Forgiveness towards AI-generated Input: Components handling AI-generated content should anticipate and tolerate slight imperfections or variations in format, which is inherent in AI generation.
2. Clear and Actionable Error Messages: Errors should be reported in a way that helps the user (AI or human) understand what went wrong and how to potentially fix it. This is crucial for enabling an effective learning and iteration process.

Incorporating these principles would greatly enhance the value and usability of Codex CLI as an AI-collaborative tool.

Thank you again for engaging with this feedback and considering the PR. I'm happy to discuss further.

Best regards,