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.
npx skills add DimDoremy/awesome-awesome --skill skillgit clone --depth 1 https://github.com/DimDoremy/awesome-awesomeWrote 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.
[](https://agentmods.dev/skills/dimdoremy/awesome-awesome/skill)<a href="https://agentmods.dev/skills/dimdoremy/awesome-awesome/skill"><img src="https://agentmods.dev/badge/skills/dimdoremy/awesome-awesome/skill.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00034 | $0.00361 |
| Sonnet 5 | $0.00014 | $0.00144 |
| Haiku 4.5 | $0.00007 | $0.00072 |
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 7d 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.
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-queueorqueue - 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 containsIcons), 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
awesomeListandsection(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:
| # | repo | ⭐ stars | updated | ⑂ forks | 👁 watchers | description |
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
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.
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.
- 7d ago First seen · 83 lines · 69 tokens per session scan A 446abd99dd16
awesome-awesome is a skill published in the GitHub repository DimDoremy/awesome-awesome (0 stars, last pushed 1mo ago), licensed MIT. It adds 69 tokens to every session and 722 once invoked, about $0.0003 per session on Opus 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.
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