Ce Riffrec Feedback Analysis logo

Ce Riffrec Feedback Analysis

OrganizationPopular
EveryInc
ce-riffrec-feedback-analysis

Analyze recorded product feedback into evidence for bugs and requirements. Use when a Riffrec capture or other screen, voice, or notes artifact needs interpretation. Use for Riffrec setup, capture, or sharing help when no recording exists yet.

Overview

PublisherEveryInc
Repositorycompound-engineering-plugin
Skill namece-riffrec-feedback-analysis
Stars
25.1K
Forks
2.1K
Bundled files
6
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by EveryInc on GitHub. Read the source before you install it.

Installation

Install the Ce Riffrec Feedback Analysis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/EveryInc/compound-engineering-plugin.git /tmp/compound-engineering-plugin
mkdir -p .claude/skills
cp -r /tmp/compound-engineering-plugin/skills/ce-riffrec-feedback-analysis .claude/skills/ce-riffrec-feedback-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ce Riffrec Feedback Analysis in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ce Riffrec Feedback Analysis on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ce Riffrec Feedback Analysis is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Riffrec Feedback Analysis

Turn raw product feedback into structured evidence for downstream agents. This skill is the consumption side of Riffrec, a capture tool that records synchronized screen + voice + event sessions and emits a riffrec-*.zip bundle.

Done:

  • Setup ends with a current capture/share path.
  • Quick analysis ends with one evidence-backed bug report and no durable artifact unless requested.
  • Extensive analysis ends with the complete evidence set and a ce-brainstorm handoff unless the user asked for extraction only.
  • A missing input, analyzer failure, or unresolved route ends with an actionable blocker rather than a partial success claim.

Choose the path

Route from the input. Read only the references named for that route; do not load the other path references.

  • Setup — user has no recording yet and asks how to install Riffrec, capture a session, or share feedback. Read references/install-riffrec.md.
  • Quick bug report — input is a short recording (under ~60 seconds), the user describes a single specific issue, or asks for "quick", "small", or "just transcribe". Read references/analyzer.md, then references/quick-bug-report.md.
  • Extensive analysis — input is longer, contains multiple issues, requirements, or a workflow walkthrough, or the user wants requirements material. Read references/analyzer.md, then references/extensive-analysis.md. Continue into ce-brainstorm unless the user explicitly asked only to extract or analyze artifacts.

When the input is ambiguous (e.g., a zip arrived without context), inspect the recording length and event count before choosing. If still unclear, ask the user which path applies before running anything heavy.

Common rules

  • Keep raw recordings, audio chunks, zip contents, session dumps, and extracted screenshots local-only by default. Do not commit raw/ or frames/ directories unless the user explicitly asks and privacy is acceptable.
  • Text/metadata artifacts (requirements kickoff material, analysis summaries, problem analyses, source manifests) may be committed when they are needed for traceability and contain no sensitive data.
  • Use repo-relative screenshot paths in any committed doc so later agents can open the evidence without absolute local paths.

The Compound Engineering output format used by the extensive path is documented in references/compound-engineering-feedback-format.md.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ce Riffrec Feedback Analysis AI skill do?

Analyze recorded product feedback into evidence for bugs and requirements. Use when a Riffrec capture or other screen, voice, or notes artifact needs interpretation. Use for Riffrec setup, capture, or sharing help when no recording exists yet.

Why use Ce Riffrec Feedback Analysis on TypingMind?

Because you install it once and use it with any model. Ce Riffrec Feedback Analysis is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ce Riffrec Feedback Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/EveryInc/compound-engineering-plugin/tree/main/skills/ce-riffrec-feedback-analysis. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ce Riffrec Feedback Analysis?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ce Riffrec Feedback Analysis?

As many as you like. As long as a model supports skills, you can use Ce Riffrec Feedback Analysis with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ce Riffrec Feedback Analysis AI skill free?

Yes. It is published on GitHub by EveryInc under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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