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update-pypi-dates

An AI agent skill from wilsonfreitas/awesome-quant, scored 60 out of 100 by code.

60/ 100C

Code score by group

  • Spec20/20
  • Description13/20
  • Procedure7/15
  • 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

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.

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/update-pypi-dates
License
Not identified by GitHub
GitHub stars
30,587
SKILL.md lines
38
Other files
2

Add update-pypi-dates to your agent

npx skills add wilsonfreitas/awesome-quant --skill update-pypi-dates

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. bprr

    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 la…

    68C
  3. 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

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.