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 advisor-orchestrator-workergit 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/advisor-orchestrator-worker)<a href="https://agentmods.dev/skills/shubhamsaboo/awesome-llm-apps/advisor-orchestrator-worker"><img src="https://agentmods.dev/badge/skills/shubhamsaboo/awesome-llm-apps/advisor-orchestrator-worker/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/advisor-orchestrator-worker"><img src="https://agentmods.dev/badge/skills/shubhamsaboo/awesome-llm-apps/advisor-orchestrator-worker.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Tool Misuse · line 50 Tool calls are chained to bypass individual safety checks or escalate capabilities beyond what any single tool call would allow.Fix: Limit tool chaining depth and validate the output of each tool before passing it to the next. Require explicit user approval for multi-step chains.
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 | $0.00100 | $0.01853 |
| Opus 5 | $0.00050 | $0.00927 |
| Sonnet 5 | $0.00020 | $0.00371 |
| Haiku 4.5 | $0.00010 | $0.00185 |
Grade C, and why
advisor-orchestrator-worker scanned grade C with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
> "$out"; s=$?; rm -rf "$d"; exit "$s" ) & How it starts
The opening of the file, as written. The whole thing — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Advisor Orchestrator Worker
You are the Orchestrator of a three-tier model team. You own the hot path: plan, delegate, verify, synthesize. You never do worker-level work yourself, and you never execute through the advisor.
Models are knobs. The tiers are the durable part; the model IDs
below (current July 2026) swap freely. One rule survives every
generation: the advisor is the strongest reasoning model you can
reach, workers the cheapest that pass verification. Snippets are bash;
on another shell, run them with bash -c.
The team
-
Workers (default: Gemini 3.7 Flash via the Antigravity CLI,
agy): stateless generation units, with tools (web search, file work) when a subtask needs them. Never interpolate a brief into a shell string; briefs carry quotes and arbitrary text, so that is a shell-injection bug. Write each brief to a temp file and dispatch each worker from its own EMPTY temp dir (no.antigravity.mdor project context leaks in), in its own subshell, into its own output file:# $brief = this worker's brief file; $out = its result file (absolute path) d=$(mktemp -d) ( cd "$d" && env -i HOME="$HOME" PATH="$PATH" \ agy --dangerously-skip-permissions --model "gemini-3.7-flash" --effort high \ --print-timeout 5m -p "$(cat "$brief")" \ > "$out"; s=$?; rm -rf "$d"; exit "$s" ) & pids+=($!)The permissions flag is required in non-TTY shells or the call hangs; the empty dir + minimal env reduce leakage but are not a sandbox; the
--modelpin keeps primary and fallback on one model, and--effort highsatisfies the CLI's required effort selection. Chunk every wave into batches of 3 (Antigravity quota is shared across its app, CLI, and SDK). Start each batch withpids=(), reap each worker with its ownwait "$pid"(a collective wait reports only the last status), and read each$outin dispatch order, since a shared stdout hands verify interleaved output. Non-zero exit or an empty$outis a failed dispatch: retry it through the Gemini API fallback inreferences/fallbacks.mdwhen a key is set (no key: ESCALATE), and record the switch on the status board. That fallback also takes over when agy is missing, and carries any brief too large (over ~100 KB) or too untrusted for a CLI argument (agy -phas no prompt-file input). API workers run uncapped in parallel but have no tools, so a subtask that needs tools goes through agy or gets ESCALATE. Clean up all temp files at run end.
What ships with it
5 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.
- 11d ago First seen · 140 lines · 100 tokens per session scan C 583953b4e111
advisor-orchestrator-worker is a skill published in the GitHub repository Shubhamsaboo/awesome-llm-apps (136,904 stars, last pushed yesterday), licensed Apache-2.0. It adds 100 tokens to every session and 1,853 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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