Traction scoreGitHub stars can be faked, so popularity alone can be misleading. Traction Score looks for broader signs of recent attention, adoption, and active maintenance.
Caveman provides a suite of compression tools designed to extend the lifespan of an agent's main context window. By converting memory files and task lists into a condensed format, it minimizes the input tokens required for ongoing coding sessions.
The system includes specialized roles for investigation, building, and reviewing. These agents operate with compressed output, allowing developers to delegate specific file edits or code location tasks without saturating the primary context.
Compatible AgentsThe repository documents support for these agents. The skills may also work with other agents that can load SKILL.md files, but they may need some setup or small changes.
The diff or an accurate description of the changes.
The project's commit conventions and any relevant breaking-change, security, migration, or issue context.
Make sure caveman-commit is installed in your agent before trying this task.
Use caveman-commit to write a Conventional Commits message for the diff I provide. Match the project's conventions, keep the subject concise, and include a body when the change needs context. Output only the message in a code block; do not stage files or create a commit.
Enough surrounding code and intended behavior to assess the changes.
Make sure caveman-review is installed in your agent before trying this task.
Use caveman-review to review the small diff I provide. Give actionable findings with exact file and line locations, the problem, and a concrete fix. Keep ordinary findings to one line each, but explain security findings fully. Return comments for me to review without posting them or changing code.
The observed failure, reproduction steps if available, and relevant error output or logs.
Make sure investigate-first is installed in your agent before trying this task.
Use investigate-first to diagnose the single failure I describe in this repository. Separate observations from inferred causes, trace the relevant inputs and state transitions, and rank hypotheses by evidence and cheap ways to falsify them. Stop when you can report a credible cause with proof or the exact blocker. Do not edit product code or implement a fix.