Agent skill · software engineering · shubhamsaboo
advisor-orchestrator-worker
Use when a task is too large for one model pass, needs parallel research or generation across many subtasks (like researching a dozen competitors at once), or the user asks to orchestrate multiple models, split work across a model team, run an advisor-worker loop, have a stronger model review the plan while cheap workers execute, or says "too big for one model" or "fan this out". Not for single-file edits or tasks one model handles in one pass.
Why this skill is useful
Adds executable bash scripts for orchestrating a multi-model workflow that aren't available in public documentation.
What it needs
Requires antigravity, jq installed locally. Requires anthropic, google account access. About 5k tokens when loaded. Last updated 2026-08-03. 131,141 stars on the source repository.
What this skill does
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.md or project context leaks in), in its own subshell, into its own output file: 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 --model pin keeps primary and fallback on one model, and --effort high satisfies 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 with pids=(), reap each worker with its own wait "$pid" (a collective wait reports only the last status), and read each $out in dispatch order, since a shared stdout hands verify interleaved output. Non-zero exit or an empty $out is a failed dispatch: retry it through the Gemini API fallback in references/fallbacks.md when 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 -p has no prompt-file input). …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills shubhamsaboo/advisor-orchestrator-worker