Query and curate knowledge-base using ByteRover CLI. Use `brv query` for knowledge retrieval, `brv curate` for adding context, and `brv push/pull` for syncing.
SkillScore73Maintained
Automatic
◆Knowledge Management pack
Last verified 8/19/2026
What it changes
Passed 12 of 17 test cases with the skill, and 5 of 17 without it.
Without skill27%
With skill73%
Checks it passed that the model alone missed
Includes the --force flag with brv init
Runs brv status with --headless --format json
Uses --branch staging-patch in the brv push command
and 8 more in the full report
10 fixed6 passed either way6 still failing0 broke
Pass-rate lift
+45 pp
Tokens
+61%
Agent turns
0%
Response time
-52%
Model gemini-3.6-flashMeasured 8/19/2026v116 of 22 cases passed with skill · 17 comparable
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 10
Prompt
What output format does `brv login --api-key <key>` produce upon successfully authenticating?
What was checked, without and with the skill
Checked
Without skill
With skill
Identifies plain text output stating Logged in as user email rather than JSON
Missed
Passed
case-15 · gemini-3.6-flash · 8/19/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md222 linessingle file
ByteRover Knowledge Management
Use the brv CLI to manage your own knowledgebase. ByteRover maintains a context tree that stores patterns, decisions, and implementation details about a project.
IMPORTANT: For headless/automated use, always add --headless --format json flags to get machine-parseable JSON output.
Setup (Headless)
ByteRover can be fully set up in headless mode. If user has not logged in or initialized .brv/ in the current working directory (check via projectInitialized and and authStatus in brv status --headless --format json response), ask them to provide: