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docs/docs/core-abilities/index.md
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# Core Abilities
PR-Agent utilizes a variety of core abilities to provide a comprehensive and efficient code review experience. These abilities include:
- [Local and global metadata](core-abilities/metadata.md)
- [Line localization](core-abilities/line_localization.md)
- [Dynamic context](core-abilities/dynamic_context.md)
- [Self-reflection](core-abilities/self_reflection.md)
- [Interactivity](core-abilities/interactivity.md)
- [Compression strategy](core-abilities/compression_strategy.md)
- [Code-oriented YAML](core-abilities/code_oriented_yaml.md)
- [Static code analysis](core-abilities/static_code_analysis.md)
- [Local and global metadata](/metadata)
- [Line localization](/line_localization)
- [Dynamic context](/dynamic_context)
- [Self-reflection](/self_reflection)
- [Interactivity](/interactivity)
- [Compression strategy](/compression_strategy)
- [Code-oriented YAML](/code_oriented_yaml)
- [Static code analysis](/static_code_analysis)

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docs/docs/core-abilities/metadata.md
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## Overview - Local and global metadata injection with multi-stage analysis
## Local and global metadata injection with multi-stage analysis
(1)
PR-Agent initially retrieves for each PR the following data:
- PR title and branch name
- PR original description
- Commit messages history
- PR diff patches, in [hunk diff](https://loicpefferkorn.net/2014/02/diff-files-what-are-hunks-and-how-to-extract-them/) format
- The entire content of the files that were modified in the PR
In addition, PR-Agent is able to receive from the user additional data, like [`extra_instructions` and `best practices`](https://pr-agent-docs.codium.ai/tools/improve/#extra-instructions-and-best-practices) that can be used to enhance the PR analysis.
In addition, PR-Agent can incorporate supplementary information provided by the user and more tailored to his specific preferences, like [`extra_instructions` and `organization best practices`](https://pr-agent-docs.codium.ai/tools/improve/#extra-instructions-and-best-practices) that can be used to enhance the PR analysis.
(2)
By default, the first command that PR-Agent executes is [`describe`](https://pr-agent-docs.codium.ai/tools/describe/), which generates three types of outputs:
- PR Type (e.g. bug fix, feature, refactor, etc)
- PR Description - a bullet points summary of the PR
- Changes walkthrough - going file-by-file, PR-Agent generate a one-line summary and longer bullet points summary of the changes in the file
These AI-generated outputs are now considered part of the PR metadata, and can be used in subsequent commands like `review` and `improve`.
This effectively enables chain-of-thought analysis, without doing any additional API calls which will cost time and money.
These AI-generated outputs are now considered as part of the PR metadata, and can be used in subsequent commands like `review` and `improve`.
This effectively enables multi-stage chain-of-thought analysis, without doing any additional API calls which will cost time and money.
For example, when generating code suggestions for different files, PR-Agent can inject the AI-generated file summary in the prompt:
For example, when generating code suggestions for different files, PR-Agent can inject the AI-generated ["Changes walkthrough"](https://github.com/Codium-ai/pr-agent/pull/1202#issue-2511546839) file summary in the prompt:
```
## File: 'src/file1.py'
@ -46,6 +48,8 @@ __old hunk__
...
```
(3) The entire PR files that were retrieved are used to expand and enhance the PR context (see [Dynamic Context](https://pr-agent-docs.codium.ai/core-abilities/dynamic-context/)).
(3) The entire PR files that where retrieved are also used to expand and enhance the PR context (see [Dynamic Context](https://pr-agent-docs.codium.ai/core-abilities/dynamic-context/)).
(4) All the metadata described above represent several level of analysis - from hunk level, to file level, to PR level, and enables PR-Agent AI models to generate more accurate and relevant suggestions and feedbacks.
(4) All the metadata described above represents several level of cumulative analysis - ranging from hunk level, to file level, to PR level, to organization level.
This comprehensive approach enables PR-Agent AI models to generate more precise and contextually relevant suggestions and feedback.