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create-entry

An AI agent skill from wilsonfreitas/awesome-quant, scored 63 out of 100 by code.

63/ 100C

Code score by group

  • Spec20/20
  • Description7/20
  • Procedure12/15
  • Done criteria4/15
  • Resources8/10
  • Scripts5/8
  • Safety7/7
  • Upkeep0/5

It loses the most points in Description (13), the group that checks whether the description says what the skill does and when to use it.

What the author says it does

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 placement.

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/create-entry
License
Not identified by GitHub
GitHub stars
30,587
SKILL.md lines
115
Other files
0

Add create-entry to your agent

npx skills add wilsonfreitas/awesome-quant --skill create-entry

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. 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.