Agent skill · Documentation

crafting-effective-readmes

Use when writing or improving README files. Not all READMEs are the same — provides templates and guidance matched to your audience and project type.

Softaworks2,247★ · +27/wk · 1 repos on radarProfile →
claude-codeMIT
Install
npx skills add softaworks/agent-toolkit --skill crafting-effective-readmes --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 14
SKILL.md size: 3 KB
Bundled scripts: none
Path: skills/crafting-effective-readmes/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 2,276 · +29 this week
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Crafting Effective READMEs ## Overview READMEs answer questions your audience will have. Different audiences need different information - a contributor to an OSS project needs different context than future-you opening a config folder. **Always ask:** Who will read this, and what do they need to know? ## Process ### Step 1: Identify the Task **Ask:** "What README task are you working on?" | Task

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About this skill
What does the crafting-effective-readmes skill do?

Use when writing or improving README files. Not all READMEs are the same — provides templates and guidance matched to your audience and project type.

How do I install it?

Run `npx skills add softaworks/agent-toolkit --skill crafting-effective-readmes --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From softaworks/agent-toolkit, a repository with 2,276 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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