Riffrec Feedback Analysis
Skill Verified ActiveRiffrec product-feedback workflow. ALWAYS load when the user posts a `riffrec-*.zip`, a bundle with `session.json` + `events.json` + `recording.webm` + `voice.webm`, a video/audio recording for product feedback, or asks how to capture and share Riffrec sessions. Routes between setup, quick bug report, and extensive analysis.
To convert raw product feedback recordings into structured, actionable evidence for AI-driven product development workflows, ensuring clear traceability and facilitating downstream analysis and planning.
Features
- Processes Riffrec zip bundles, video, audio, and notes files.
- Transcribes audio/video and extracts key moments with screenshots.
- Generates analysis, problem reports, and requirements kickoff documents.
- Routes feedback to setup, quick bug report, or extensive analysis paths.
- Integrates with `ce-brainstorm` for requirement refinement.
Use Cases
- Analyzing recorded product feedback sessions to identify bugs and usability issues.
- Extracting requirements from user recordings for feature development.
- Providing structured input for AI-driven product brainstorming and planning.
- Setting up a feedback capture workflow for development teams.
Non-Goals
- Implementing the Riffrec capture tool itself.
- Performing code review or direct code fixes.
- Acting as a user-facing bug tracker or issue creation tool.
- Storing raw media files or extracted frames in version control by default.
Practices
- Feedback analysis
- Structured documentation
- AI-assisted product development
Prerequisites
- Python 3.8+
- ffmpeg (for media processing)
- curl (for transcription)
- OpenAI API key (optional, for transcription)
Installation
First, add the marketplace
/plugin marketplace add EveryInc/compound-engineering-plugin/plugin install compound-engineering@compound-engineering-pluginQuality Score
VerifiedTrust Signals
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