Fix Institutional Memory Framework PR comments - #20
Conversation
Co-authored-by: dandye <121151+dandye@users.noreply.github.com>
|
👋 Jules, reporting for duty! I'm here to lend a hand with this pull request. When you start a review, I'll add a 👀 emoji to each comment to let you know I've read it. I'll focus on feedback directed at me and will do my best to stay out of conversations between you and other bots or reviewers to keep the noise down. I'll push a commit with your requested changes shortly after. Please note there might be a delay between these steps, but rest assured I'm on the job! For more direct control, you can switch me to Reactive Mode. When this mode is on, I will only act on comments where you specifically mention me with New to Jules? Learn more at jules.google/docs. For security, I will only act on instructions from the user who triggered this task. |
Summary of ChangesHello @dandye, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request significantly enhances the AI Runbooks project by integrating a new 'Institutional Memory Framework' and the 'SuperClaude_Framework' as a submodule. The core purpose is to introduce adaptive learning capabilities, allowing AI agents to dynamically improve their procedural execution based on operational feedback and organizational context. This involves a comprehensive set of new documentation, runbooks, and common steps for managing, optimizing, and applying institutional knowledge, alongside a refactoring of the existing skills system. The changes aim to streamline security operations, improve decision-making accuracy, and foster continuous organizational learning within the AI-assisted cybersecurity workflows. Highlights
🧠 New Feature in Public Preview: You can now enable Memory to help Gemini Code Assist learn from your team's feedback. This makes future code reviews more consistent and personalized to your project's style. Click here to enable Memory in your admin console. Ignored Files
Using Gemini Code AssistThe full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips. Invoking Gemini You can request assistance from Gemini at any point by creating a comment using either
Customization To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a Limitations & Feedback Gemini Code Assist may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments. If you're interested in giving your feedback about your experience with Gemini Code Assist for Github and other Google products, sign up here. You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension. Footnotes
|
![gemini-code-assist[bot]](https://avatars.githubusercontent.com/in/956858?s=60&v=4){kind=small}
There was a problem hiding this comment.
Code Review
This pull request introduces a significant and well-structured "Institutional Memory" framework, which is a great step towards an adaptive learning system for security runbooks. The addition of the SuperClaude_Framework submodule and the detailed documentation for new commands and concepts are excellent. The cleanup of old runbooks by removing rubrics and boilerplate completion steps simplifies the codebase.
My review focuses on ensuring consistency within the new framework. I've identified a minor but important inconsistency in how "confidence levels" are defined across different new files. Aligning these definitions will improve clarity and prevent ambiguity during implementation. Overall, this is a high-quality contribution that greatly enhances the repository's capabilities.
| ### HIGH_CONFIDENCE | ||
| USE FOR: High Trust, Proven Memory, Validated Learning | ||
| BT: Confidence Level | ||
| RT: Automatic Application, Validated Memory, High Success Rate | ||
| SN: Confidence level 0.9-1.0, suitable for automatic application | ||
|
|
||
| ### EXPERIMENTAL_CONFIDENCE | ||
| USE FOR: Experimental Memory, Unproven Learning | ||
| BT: Confidence Level | ||
| RT: Low Confidence, Validation Required, Analyst Approval | ||
| SN: Confidence level 0.0-0.3, requires analyst approval before application | ||
|
|
||
| ### LOW_CONFIDENCE | ||
| USE FOR: Low Trust, Cautious Application | ||
| BT: Confidence Level | ||
| RT: Experimental Confidence, Medium Confidence, Careful Monitoring | ||
| SN: Confidence level 0.4-0.6, suggest with caution and track closely | ||
|
|
||
| ### MEDIUM_CONFIDENCE | ||
| USE FOR: Moderate Trust, Recommended Application | ||
| BT: Confidence Level | ||
| RT: High Confidence, Recommended Procedure | ||
| SN: Confidence level 0.7-0.8, suitable for recommendation with explanation No newline at end of file |
There was a problem hiding this comment.
The definitions for confidence levels have gaps between the ranges (e.g., values between 0.3 and 0.4, or 0.6 and 0.7 are undefined), which could lead to ambiguity. To ensure consistency across the new framework, consider adopting continuous ranges as defined in rules_bank/institutional_memory/SYSTEM_HEALTH_DASHBOARD.md. This would make the definitions clearer and cover all possible values.
### HIGH_CONFIDENCE
USE FOR: High Trust, Proven Memory, Validated Learning
BT: Confidence Level
RT: Automatic Application, Validated Memory, High Success Rate
SN: Confidence level ≥0.9, suitable for automatic application.
### EXPERIMENTAL_CONFIDENCE
USE FOR: Experimental Memory, Unproven Learning, Very Low Confidence
BT: Confidence Level
RT: Low Confidence, Validation Required, Analyst Approval
SN: Confidence level <0.4, requires analyst approval before application.
### LOW_CONFIDENCE
USE FOR: Low Trust, Cautious Application
BT: Confidence Level
RT: Experimental Confidence, Medium Confidence, Careful Monitoring
SN: Confidence level 0.4-0.69, suggest with caution and track closely.
### MEDIUM_CONFIDENCE
USE FOR: Moderate Trust, Recommended Application
BT: Confidence Level
RT: High Confidence, Recommended Procedure
SN: Confidence level 0.7-0.89, suitable for recommendation with explanation.| - **0.0-0.3**: Experimental/unvalidated memories (requires analyst approval) | ||
| - **0.4-0.6**: Low confidence (suggest with caution, track closely) | ||
| - **0.7-0.8**: Medium confidence (recommend with explanation) | ||
| - **0.9-1.0**: High confidence (apply automatically with notification) |
There was a problem hiding this comment.
The confidence scoring guidelines have gaps between the ranges, which can cause ambiguity. To ensure consistency across the framework, I recommend using continuous ranges, similar to the definitions in SYSTEM_HEALTH_DASHBOARD.md.
| - **0.0-0.3**: Experimental/unvalidated memories (requires analyst approval) | |
| - **0.4-0.6**: Low confidence (suggest with caution, track closely) | |
| - **0.7-0.8**: Medium confidence (recommend with explanation) | |
| - **0.9-1.0**: High confidence (apply automatically with notification) | |
| - **<0.4**: Experimental/unvalidated memories (requires analyst approval) | |
| - **0.4-0.69**: Low confidence (suggest with caution, track closely) | |
| - **0.7-0.89**: Medium confidence (recommend with explanation) | |
| - **≥0.9**: High confidence (apply automatically with notification) |
1 participant
Addressed feedback from PR #13 review:
apply_memory_procedure.md.MEMORY-THESAURUS.md.automated_memory_lifecycle.md.PHASE_3_INTEGRATION_SUMMARY.md.README.md.PR created automatically by Jules for task 8647627324536481608 started by @dandye