Skip to content
SkillCheatSheet Sign in

bprr

An AI agent skill from wilsonfreitas/awesome-quant, scored 68 out of 100 by code.

68/ 100C

Code score by group

  • Spec20/20
  • Description18/20
  • Procedure10/15
  • Done criteria0/15
  • Resources8/10
  • Scripts5/8
  • Safety7/7
  • Upkeep0/5

It loses the most points in Done criteria (15), the group that checks whether there is a checklist and a step that verifies the work.

What the author says it does

Bulk PR reviewer for awesome-quant. Use when the user asks to review all open PRs, review unreviewed PRs, bulk review, or mentions "bprr". Reviews open PRs lacking the reviewed label and presents a summary before any merge/comment/label action.

This is the description in the skill's own SKILL.md, written by its author, not by SkillCheatSheet.

Repository
wilsonfreitas/awesome-quant
Folder
.agents/skills/bprr
License
Not identified by GitHub
GitHub stars
30,587
SKILL.md lines
62
Other files
0

Add bprr to your agent

npx skills add wilsonfreitas/awesome-quant --skill bprr

The command uses the open skills CLI. A skill is instructions and scripts your agent will follow, so read it first: open the folder on GitHub at the commit that was scored. GitHub identifies no license for this repository, so check its terms before you use it.

More skills from wilsonfreitas/awesome-quant

  1. sprr

    Single PR reviewer for awesome-quant. Use when the user asks to review, validate, comment on, label, close, or merge one specific pull request that adds README.md entries. Trigger…

    Verified80B
  2. create-entry

    Use when the user wants to suggest, draft, create, or add an awesome-quant README entry from a project URL, GitHub issue, or Linear issue, including choosing tags and category pla…

    63C
  3. update-pypi-dates

    Refresh tracked PyPI last-updated dates in awesome-quant README.md. Use when the user asks to update PyPI dates, refresh PyPI metadata, or run update-pypi-dates.

    60C

All skills in wilsonfreitas/awesome-quant

Scores come from a snapshot of 300 public GitHub repositories taken on 12 November 2026. Every score is the result of fixed checks run by code; no AI is involved. How scoring works.