A better way to understand what someone might bring.
Libelle is an experiment we're building at The Chamber of Us.
We're testing a practical workflow for meeting volunteers: understand what someone brings, consider it in the context of what we're doing, start conversations when something seems promising, and remember what mattered when the answer is “not yet.”
It started with a very ordinary problem.
People would come to TCUS wanting to volunteer. They had different backgrounds, skills, interests, and reasons for showing up.
Sometimes there was an obvious place to start. Often there wasn't.
Understanding someone meant reading what they sent, looking through a résumé, figuring out unfamiliar experience, thinking about current projects, having conversations, and trying to remember all of it later.
Too much of that understanding lived in someone's inbox, browser tabs, notes, or memory.
The workflow matters more than the algorithm.
Libelle isn't trying to produce a perfect match. The current build is about giving a human reviewer better context and a better memory of what happened.
Someone introduces themselves.
They tell us about their background, experience, interests, curiosity, availability, and whatever evidence helps us understand them.
Libelle brings the evidence together.
What the person actually supplied stays distinct from anything inferred or generated later.
A person at TCUS reviews it in context.
Libelle can assist the reviewer in understanding what stands out, what might be worth exploring, and what remains unclear.
People talk.
A possibility is not an assignment. If something seems worth exploring, the next step is a conversation.
Libelle remembers what happened.
What stood out, what was decided, what should happen next, and when it might be worth looking again can stay attached to the relationship.
A possibility begins a conversation.
It doesn't make the decision. A volunteer and the people at TCUS still have to decide whether something makes sense, whether it's responsible, and whether they actually want to do it.
“Not yet” is information too.
Someone can be interesting before there's a useful next step. Instead of forcing a match — or forgetting the person entirely — Libelle can preserve enough context to know why they mattered and when to look again.
Some things we're deliberately not building.
The easiest version of this product would be a matching engine. That isn't the experiment we're interested in right now.
Libelle doesn't reduce a person to a numerical fit.
Human judgment remains visible and responsible.
A possible contribution is something to discuss, not something the system declares.
Libelle is open source, in development, and being tested through real use at TCUS.
In development, on purpose.
We're building Libelle in small steps and using each version to learn what is actually useful before adding more machinery.
The current work focuses on contributor coordination: better intake, reviewer understanding, organizational context, human takeaways, next actions, and simple reconsideration when the answer is “not yet.”
Want to bring something to it?
You don't need to know exactly where you fit. Tell us about yourself, and we'll start there.