Awesome AI Apps is a collection of 132 projects, tutorials, and recipes for building applications powered by large language models. Developers use it to explore text and voice agents, retrieval-augmented generation, workflows, MCP tools, memory, and fine-tuning.
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 Arindam200/awesome-ai-apps --skill researchgit clone --depth 1 https://github.com/Arindam200/awesome-ai-appsWrote 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/arindam200/awesome-ai-apps/research)<a href="https://agentmods.dev/skills/arindam200/awesome-ai-apps/research"><img src="https://agentmods.dev/badge/skills/arindam200/awesome-ai-apps/research/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.
<a href="https://agentmods.dev/skills/arindam200/awesome-ai-apps/research"><img src="https://agentmods.dev/badge/skills/arindam200/awesome-ai-apps/research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00078 | $0.00317 |
| Opus 5.5 | $0.00031 | $0.00127 |
| Sonnet 5.5 | $0.00016 | $0.00063 |
| Haiku 4.5 | $0.00008 | $0.00032 |
Grade A, and why
research 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- research — 100% identical, 0 lines differ
What it actually says
Research
Research a topic using the deep-research MCP server.
Working Directory
All output goes into outputs/{slug}/ relative to the project root. Derive the slug from:
- The dataset seed filename if the user references one (e.g.,
my-topic_seed.md→my-topic) - Otherwise, slugify the topic (lowercase, hyphens, no special chars, max 60 chars)
Create the directory if it doesn't exist.
Execution
- Load the
research_workflowMCP prompt from thedeep-researchserver. - Follow the workflow instructions to research the user's topic using the available tools:
deep_research— for web research queriesanalyze_youtube_video— for any YouTube URLs the user providescompile_research— to produce the final research.md
- Use
outputs/{slug}/as theworking_dirfor all tool calls.
After Completion
Show the user the path to outputs/{slug}/research.md and a brief summary of what was found.
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.
- 1mo ago First seen · 30 lines · 78 tokens per session scan A e8e2e7764bac
research is a skill published in the GitHub repository Arindam200/awesome-ai-apps (16,207 stars, last pushed 6d ago), licensed MIT. It adds 78 tokens to every session and 317 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 2026-08-30.
Other skills, from other repositories
ai-agent-evaluation-benchmarking
Autonomous AI agent benchmark evaluation register: task completion rates, planning accuracy, tool invocation precision, and cost benchmarks.
otto-pr-review-loop
Run Polly and OCR on a PR until all findings settle.
verify-omnigent
Drive Omnigent the way a user does and prove a behavior with recorded evidence, using an isolated server, runner, and mock model plus a feature map of every user entry point. Load before reproducing a user-facing bug, before claiming a fix works, or when reviewing whether a change covered every surface (session vs.…
deploy-docker-compose
Run the Omnigent server as a Docker compose stack (server + Postgres) on any Docker host — your laptop, a VPS, EC2 by hand, or as the base layer of any container-platform deploy. Invoke when the user wants to build the image, bring up the compose stack, debug the stack on a host they already have, or extend the stack…
security-audit
Audit a codebase or directory for security issues (hardcoded secrets, injection, unsafe deserialization, weak crypto, authz gaps) and produce a structured findings report. Use when the user asks for a security review, an audit, or to check code for vulnerabilities. Report only — never fix.
Workspace Data Analyst
Analyze CSV files in the workspace and summarize insights.