What is awesome-awesome?

A library-finding assistant for a particular programming language or technology stack. It searches curated GitHub lists known as “awesome lists” and compares candidate libraries using available project activity data.

Use it to find, compare, and choose libraries such as a Vue icon library, a Python task queue, or a Rust web framework.

It reduces the time spent searching through scattered library options and helps narrow a choice to candidates that fit the requested technology and job.

awesome-awesome — Skill for Claude Code and Codex by DimDoremy

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add DimDoremy/awesome-awesome --skill skill

Clone the repo

git clone --depth 1 https://github.com/DimDoremy/awesome-awesome

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/dimdoremy/awesome-awesome/skill/github.svg)](https://agentmods.dev/skills/dimdoremy/awesome-awesome/skill)

Your own site

<a href="https://agentmods.dev/skills/dimdoremy/awesome-awesome/skill"><img src="https://agentmods.dev/badge/skills/dimdoremy/awesome-awesome/skill/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/dimdoremy/awesome-awesome/skill"><img src="https://agentmods.dev/badge/skills/dimdoremy/awesome-awesome/skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 69 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 722 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00069	$0.00722
Opus 5.5	$0.00028	$0.00289
Sonnet 5.5	$0.00014	$0.00144
Haiku 4.5	$0.00007	$0.00072

Measured 2mo ago against content hash 446abd99dd16, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

awesome-awesome scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

packages/mcp/skill/SKILL.md · 83 lines

How it starts

The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.

awesome-awesome Skill

Use the awesome-awesome MCP tools to find candidate libraries and help the user choose. This skill never invents library data — it always comes from the discover tool.

When to use

Trigger when the user expresses intent like:

"I need a Vue icon library"
"Find me a Python task queue"
"What's a good Rust HTTP framework?"
"Compare options for X"

Workflow

Step 1 — Collect intent

Parse the user's request into { tech, category }.

"Vue icon library" → tech=vue, category=icon
"Python async task queue" → tech=python, category=task-queue or queue
If you cannot extract both clearly, ask the user one short question.

Step 2 — Call discover (initial)

Call discover({ tech, category }). Branch on status:

need-awesome-list-choice — Multiple awesome-lists exist. Present them as a table and ask the user which to use. Re-invoke discover with the chosen awesomeList ("owner/repo").

#	repo	stars	updated	description

need-section-choice — The awesome-list is loaded. Inspect sections yourself; you decide which matches category:

If one section obviously matches (e.g., category=icon, sections contains Icons), select it directly. Do not ask the user.
If several could match or none is obvious, list the top candidates and ask the user. Then re-invoke discover with awesomeList and section (exact title, case-sensitive).

done — Go to Step 3.

no-result — Tell the user nothing was found. Suggest rephrasing the category or trying a broader tech term.

Step 3 — Present results

Show the candidates as a markdown table, in the order they were returned (they are already ranked). Columns:

Mention that archived projects are pre-filtered out.

Step 4 (optional) — Deep compare

If the user is torn between 2-3 candidates, call get_repo_details for each and synthesize a short comparison covering:

Maintenance activity (last commit, recent releases)
Stars growth / open issues trend (if visible)
License compatibility
Whether it fits the user's stated constraints

Read the full file on GitHub · 83 lines

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

2mo ago First seen · 83 lines · 69 tokens per session scan A 446abd99dd16

Subscribe to this mod's changes

awesome-awesome is a skill published in the GitHub repository DimDoremy/awesome-awesome (0 stars, last pushed 3mo ago), licensed MIT. It adds 69 tokens to every session and 722 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Research

awesome-awesome next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
awesome-awesomeDimDoremy/awesome-awesome	Skill	0	3mo ago	MIT	A	69	Claude Code, Codex
imagegenopenai/codex	Skill	134k	today	Apache-2.0	A	113	Codex
local-ai-agentsmicrosoft/ai-agents-for-beginners	Skill	79k	2d ago	MIT	A	200	Claude Code, Codex
playwright-climicrosoft/playwright	Skill	100k	yesterday	Apache-2.0	A	19	Claude Code
browser-automationopenclaw/openclaw	Skill	394k	today	MIT	A	36	Claude Code
systematic-debuggingobra/superpowers	Skill	315k	5d ago	MIT	A	21	Claude Code
next-cache-components-adoptionvercel/next.js	Skill	143k	today	MIT	A	95	Claude Code