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 write-postgit 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/write-post)<a href="https://agentmods.dev/skills/arindam200/awesome-ai-apps/write-post"><img src="https://agentmods.dev/badge/skills/arindam200/awesome-ai-apps/write-post/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/write-post"><img src="https://agentmods.dev/badge/skills/arindam200/awesome-ai-apps/write-post.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 | $0.00106 | $0.00433 |
| Opus 5.5 | $0.00042 | $0.00173 |
| Sonnet 5.5 | $0.00021 | $0.00087 |
| Haiku 4.5 | $0.00011 | $0.00043 |
Grade A, and why
write-post 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:
- write-post — 100% identical, 0 lines differ
What it actually says
Write LinkedIn Post
Generate a LinkedIn post using the linkedin-writer MCP server.
Working Directory
All output goes into outputs/{slug}/ relative to the project root. Derive the slug from:
- The dataset seed/guideline 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.
Input Preparation
The working directory needs guideline.md and research.md.
If the user provides raw text for the guideline, create guideline.md in the working directory:
# LinkedIn Post Guideline
## Topic
[What the post is about]
## Angle
[What perspective or approach to take]
## Target Audience
[Who this post is for]
## Key Points to Cover
[3-5 bullet points]
## Tone
[How it should sound]
If research.md is in a different location, copy it into the working directory.
Execution
Read the WORKFLOW_INSTRUCTIONS from src/writing/routers/prompts.py and follow those steps exactly, using the linkedin-writer MCP tools. Pass outputs/{slug}/ as the working directory path to each tool.
After Completion
Present the final outputs/{slug}/post.md content to the user. If an image was generated, mention outputs/{slug}/post_image.png.
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 · 52 lines · 106 tokens per session scan A a171ed0c5005
write-post is a skill published in the GitHub repository Arindam200/awesome-ai-apps (16,627 stars, last pushed 14d ago), licensed MIT. It adds 106 tokens to every session and 433 once invoked, about $0.0004 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.
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ai-agent-evaluation-benchmarking
Autonomous AI agent benchmark evaluation register: task completion rates, planning accuracy, tool invocation precision, and cost benchmarks.
create-tech-design
Only use when the user explicitly invokes this skill by name.
iterate-tech-design
Only use when the user explicitly invokes this skill by name.
otto-pr-review-loop
Run Polly and OCR on a PR until all findings settle.
implement-outline
Only use when the user explicitly invokes this skill by name.
iterate-implementation
Only use when the user explicitly invokes this skill by name.