Your team's unwritten review conventions, mined from PR history, served to your AI coding assistant.
Every team enforces conventions that never make it into a handbook. They show up as review comments, get applied, and stay in reviewers' heads. Consilient reads a repository's pull-request review history, extracts the conventions the team actually enforces, drops everything a linter or an existing doc already covers, and writes the rest into a compact ruleset your coding agent loads. Re-run it on a schedule and the ruleset follows the team.
This project is a build spec, not a tool. You hand SPEC.md
to a capable coding agent, answer a short set of questions, and it scaffolds the pipeline
against your review history on GitHub, GitLab, or any other host.
Clone the repo and open the spec in your coding agent:
git clone https://github.com/bartlomiejzuber/consilient.git
cd consilient
claude # or any agent that can run an agentic workspace, then: "Build SPEC.md"
The agent interviews you about the decisions that matter (LLM provider, Git host, models, output format, deployment), then scaffolds the pipeline, by default as a weekly CI job. The pitfall list in the spec comes from building and operating the reference implementation, so your build does not have to rediscover them.
The repository ships a complete run against React, Meta's UI library: real review comments, real pull requests, and a ruleset where every kept rule traces back to the comment it was learned from.
review comments mined, one extraction agent per comment.
pull requests carrying review discussion.
rules kept, each traced to its source comment.
candidates dropped: already linted, already documented, or decayed.