basedhardware/omi
Overview
This skill is a meta-skill that analyzes pull requests, issues, and user interactions to automatically improve Cursor rules and skills. It extracts concrete lessons from code reviews, bug reports, and user feedback, then creates or updates rules and tracks their effectiveness. The goal is continuous prevention of common mistakes and faster convergence on useful guidance.
How this skill works
The skill fetches PR and issue data, parses review comments and labels, and scans conversation transcripts for corrections and preference signals. It identifies recurring failure and success patterns, maps findings to existing rules, and either updates those rules or generates new ones with concrete examples and references. Finally, it records metrics to measure rule effectiveness over time.
When to use it
- After a closed PR (merged or rejected) to capture lessons learned
- When multiple issues reveal the same bug or misunderstanding
- Following repeated user corrections or clarification requests
- To create rules when a new recurring pattern emerges
- When validating that rule updates reduced future errors
Best practices
- Be specific: capture concrete examples and exact rejection reasons
- Always reference sources: include PR/issue numbers and snippets
- Prioritize high-impact patterns that cause most rejections
- Test rule changes in a safe environment before wide rollout
- Iterate: refine rules as more examples accumulate
Example use cases
- Analyze PR #3567 to extract deprecated-function errors and update common-mistakes
- Scan issues tagged with ‘bug’ to find missing conversation persistence patterns
- Monitor conversations to learn individual user preferences and update profiles
- Create a new rule when several PRs misuse audio storage flow
- Measure reduction in PR rejections after adding pre-implementation checks
FAQ
It parses review comments and code diffs for mention of deprecated names, cross-references them with a known-deprecations list, and records examples to add to rules.
What metrics track rule effectiveness?
Typical metrics are reduction in related PR rejections, frequency of user corrections, rule coverage across scenarios, and post-update regression rates.
10 skills
This skill analyzes PRs, issues, and user interactions to generate and update Cursor rules for continuous self-improvement.
This skill guides Omi plugin development, including webhook patterns, chat tools, and OAuth flows to streamline building robust integrations.
This skill generates changelog entries from git commits and PRs, formats, and updates CHANGELOG.md for releases.
This skill guides backend development for Omi conversations, memory extraction, and LangGraph integration across Firestore, Pinecone, and Redis.
This skill generates Mermaid diagrams to visualize architecture, data flows, and component relationships, helping documentation and architectural analysis.
This skill automates browser testing, design-to-code generation, and accessibility checks to speed web development and quality assurance.
This skill guides you to choose and use Agent, Ask, Plan, and Debug modes to match tasks and workflows.
This skill helps you manage context efficiently using @ mentions, context window optimization, and semantic search for targeted references.
This skill analyzes GitHub issues, scores priority, maps to Omi layers, and suggests lanes to streamline triage and resolution.
This skill automates pull request workflows by generating descriptions, validating requirements, linking issues, and suggesting reviewers before PR creation.
