Agent skill · writing content · aiskillstore
awesome-repo-builder
Create a topic-specific GitHub awesome-list repository scaffold with a polished README, concise contribution rules, AI-agent instructions, URL verification, license, and reusable templates. Use when the user wants to generate a new awesome repo, awesome list, curated repository, or topic resource catalog from a topic plus optional taxonomy, inclusion criteria, and researched entries.
Why this skill is useful
Adds a script for generating a structured awesome-list repository with specific guidelines and templates that the AI wouldn't reliably create on its own.
What it needs
Requires python installed locally. About 2k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Awesome Repo Builder Build a new awesome-list repository with a polished README.md, concise contribution rules, AI-agent instructions, URL verification script, license, .gitignore, and optional reusable templates. Required Inputs Ask only for missing essentials. Topic: required. Example: "future event prediction", "AI coding harnesses", "LLM time-series forecasting". Taxonomy: recommended. If absent, propose one before generating. Inclusion criteria: recommended. If absent, draft concise criteria and exclusions. Initial entries: optional. The user may provide an already researched awesome list; otherwise research enough seed entries before generating. Output directory: optional. If absent, create a local folder named from the repo slug. Workflow 1. Inspect the user's requested topic and decide whether enough taxonomy and criteria exist. 2. If initial entries are absent, research the topic first. Prefer official project pages, papers, docs, GitHub repositories, and existing awesome lists. Keep the first generated repo useful but not exhaustive. 3. Create a JSON spec with title, slug, topic, tagline, description, taxonomy, criteria, initialentries, and relatedlists. 4. Run scripts/createawesomerepo.py with the spec and output directory. 5. Read the generated files and make small manual edits if the topic requires wording changes. 6. Report the output path and any assumptions, especially taxonomy or criteria that were inferred. Scaffold Command Use the bundled script from the skill directory: If the current project uses a Python virtual environment, activate it first. The script only uses the Python standard library. Spec Format Minimum: Notes: taxonomy[].entries may be empty; the script will create a placeholder. taxonomy[].subsections is supported for nested sections. initialentries is also supported as a mapping from section name to entries when entries are collected separately. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/awesome-repo-builder