Awesome LLM Apps is a collection of open-source applications built around large language models, including AI agents and retrieval-augmented generation apps. It is intended for developers who want to study, run, or adapt these applications and related agent skills.
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 Shubhamsaboo/awesome-llm-apps --skill thinking-out-loudgit clone --depth 1 https://github.com/Shubhamsaboo/awesome-llm-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/shubhamsaboo/awesome-llm-apps/thinking-out-loud)<a href="https://agentmods.dev/skills/shubhamsaboo/awesome-llm-apps/thinking-out-loud"><img src="https://agentmods.dev/badge/skills/shubhamsaboo/awesome-llm-apps/thinking-out-loud/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/shubhamsaboo/awesome-llm-apps/thinking-out-loud"><img src="https://agentmods.dev/badge/skills/shubhamsaboo/awesome-llm-apps/thinking-out-loud.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- 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 | $0.00206 | $0.01749 |
| Opus 5.5 | $0.00082 | $0.00700 |
| Sonnet 5.5 | $0.00041 | $0.00350 |
| Haiku 4.5 | $0.00021 | $0.00175 |
Grade A, and why
thinking-out-loud 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.
How it starts
The opening of the file, as written. The whole thing — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Thinking Out Loud
A ten minute voice ramble transfers more context than any prompt a person would type, and models reconstruct rambles well. The failure is downstream and invisible: the model fills every gap in the ramble confidently. "The usual model" silently becomes a specific model. "The standard size" becomes a specific viewport. A position the user reversed mid-ramble survives as fact. None of this registers as uncertainty from the inside, so none of it ever becomes a clarifying question. The model then acts on a misreading it fully believes, and the user discovers it an hour of generated work later.
This skill is the fix: before acting on any ramble, produce an echo, a short structured audit of everything absorbed, with the model's own additions quarantined from the user's words. The user corrects three lines instead of debugging a built artifact.
Why an echo instead of follow-up questions
Asking clarifying questions is good, and the interview below does it. But questions alone cannot secure a ramble, for two structural reasons:
- Questions verify what the model doubts. The echo verifies what the model believes. A clarifying question requires felt uncertainty, and confident misreadings feel like knowledge. The echo forces every inference and gap-fill into the open whether or not it felt uncertain.
- Questions sample; the echo audits. A long ramble carries dozens of facts and half-decisions. Even good questions probe three or four; the rest of the model's understanding goes unverified into action. The echo inventories the entire transfer, and it works by recognition, not recall: the user reads and spots what is wrong, which is far cheaper than producing answers, and ramblers often do not know their answer until they see the wrong guess written down.
The contract
- Act on nothing. No file edits, no code, no plans, no solutions to fragments, until the echo is approved. Reconstruct first.
- Label every addition. Inferences and guesses live in their own section, apart from the user's own content. Never present a guess in the user's voice.
- Surface every reversal. Adopt the later position, but flag the flip. Never silently average or pick.
- Lose nothing. Tangents get parked, not dropped.
- Never remark on dictation artifacts. Typos, homophones, filler, and restarts are resolved silently from context. Keep the user's own vocabulary and project names.
- Ask before persisting. The approved brief is offered a home, never saved unprompted.
What ships with it
2 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.
- 1mo ago First seen · 159 lines · 206 tokens per session scan A 50eebfbf1e8f
thinking-out-loud is a skill published in the GitHub repository Shubhamsaboo/awesome-llm-apps (143,051 stars, last pushed 3d ago), licensed Apache-2.0. It adds 206 tokens to every session and 1,749 once invoked, about $0.0008 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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