advisor-orchestrator-worker

Shubhamsaboo/awesome-llm-apps · Agent Skill

Orchestrates complex tasks by breaking them into subtasks for parallel processing by multiple models, followed by verification and synthesis of results.

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Overview

PublisherShubhamsabooCommunity
RepositoryShubhamsaboo/awesome-llm-apps ↗
Skill folderadvisor-orchestrator-worker
Stars138,130
Forks20,293
Bundled files6
LicenseApache-2.0
Last updatedSeptember 14, 2026
LinksSkill source ↗ · Repository ↗

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What we found in the skill files. Details come with a subscription.

Security
medium risk · 2 points need attention
Compatibility
instructions only — works with most agents
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51 similar skills found
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Installation

A skill is a folder with a SKILL.md file. Pick your agent and run the commands in a terminal (macOS, Linux or Git Bash on Windows) from your project root.

Copy the prompt into Claude, Codex or another assistant: it will review the skill and install it for you.

Install the “advisor-orchestrator-worker” skill from mozgi.io (https://mozgi.io/skills/advisor-orchestrator-worker/) for me.

1. Download the archive: https://mozgi.io/skills/advisor-orchestrator-worker/download
   If it fails, take the skill folder from the source: https://github.com/Shubhamsaboo/awesome-llm-apps/tree/main/agent_skills/advisor-orchestrator-worker
2. Read SKILL.md and the other files. Check the skill is safe: no hidden commands, no sending data elsewhere, no scripts doing something other than described. If anything looks suspicious, stop and tell me.
3. Put the “advisor-orchestrator-worker” folder into your agent's skills folder: .claude/skills/ for Claude Code, .agents/skills/ for Codex, .cursor/skills/ for Cursor, .gemini/skills/ for Gemini CLI. Inside the project if we are in one, otherwise in the home folder (~/.claude/skills/ etc.).
4. Make sure <skills folder>/advisor-orchestrator-worker/SKILL.md exists.
5. Briefly tell me what this skill does and offer to try it on my task.

If you cannot do a step yourself, tell me what to click or type.

Review the source files

6 files Download Zip

Read SKILL.md and any companion files before deciding whether to install. Click a file to preview it.

SKILL.md contents

Original author text · read-only

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 3.8 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:

```bash

# $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-3.8-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 --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). 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.

  • Advisor (default: Claude Fable 5.1 via the claude CLI): consult

written to a temp file, passed on stdin (never inline in the

command), behind a timeout so a hung consult can't stall the loop

(perl's alarm; timeout(1) is missing on stock macOS):

perl -e 'alarm shift; exec @ARGV' 300 claude --model claude-fable-5-1 -p < "$consult".

Expensive judgment kept out of the hot path: strategy, decomposition

critique, risk, taste. Never execution. If the CLI is missing or a

consult fails, use the Anthropic API fallback in

references/fallbacks.md.

The loop

1. Frame. State the deliverable and 3 to 5 checkable success

criteria; if the task is too vague for that, ask one question and

stop. Check tools now, not mid-run: agy, jq, the claude CLI,

ANTHROPIC_API_KEY, and api_key="${GEMINI_API_KEY:-$GOOGLE_API_KEY}".

Each role resolves CLI first, then API key; announce every fallback

up front. If a role has no working path, say exactly how to set it

up, then offer degraded mode: you temporarily play that role

yourself, same budgets, every affected section and the final result

labeled [DEGRADED: <role>], context-isolation caveat noted.

Degraded mode is the one exception to the never-do-worker-work rule

and covers at most one role; with two or more missing there is no

team left, so say so and proceed as ordinary single-model work.

2. Plan. Decompose into self-contained subtasks with inline inputs,

acceptance criteria, and wave assignments that maximize parallelism.

3. Plan review (mandatory advisor consult #1). Send the plan using

the format in references/advisor-consult.md. Revise. State what

you changed and what you rejected.

4. Delegate. Dispatch each wave using the format in

references/worker-brief.md. Parallel background calls, then wait.

5. Verify. Check every result against its own acceptance criteria,

and make the check exercise the deliverable itself: run the actual

command, read the actual output. Grepping a README, testing

something adjacent, printing True while exiting zero, or re-checking

that a file exists proves nothing. Verdict per result: PASS, FIX

(redispatch naming the specific failure), or ESCALATE. Never

silently accept a partial pass; never hand-patch a substantive

failure; redispatch instead.

6. Synthesize. When all subtasks pass, assemble the deliverable.

Resolve conflicts between worker outputs explicitly, never by

averaging.

7. Taste pass (mandatory advisor consult #2). Send the draft to

the advisor for taste and risk review. Apply or rebut each note.

Commitment boundaries (when to escalate to the advisor mid-loop)

  • Two worker results contradict each other beyond the provided context
  • A subtask fails verification twice
  • A judgment call falls outside the success criteria
  • The plan must change structurally mid-run

Budget: set one at the frame step, sized to the plan, and state it

alongside the success criteria. A reasonable shape is twice the subtask

count in worker dispatches (retries and fallback redispatches count)

plus 5 advisor consults, 2 of which are the mandatory reviews. The cap

is not the point; the rule is that spending past it is never silent. If

the budget runs out, stop and report, or tell the user what more would

cost and let them decide.

Finish

Stop at a verified deliverable, an exhausted budget, or a blocker that

needs the user. Return: the deliverable, the plan, a verification

ledger per subtask, advisor notes applied and rejected, and remaining

risks. Print a one-line status board after each loop step: per subtask,

its state (PENDING / DISPATCHED / PASS / FIX / ESCALATED), dispatch

path, and retries, e.g. W2: FIX → PASS | agy→api | 1 retry.

Frequently asked questions

What does the advisor-orchestrator-worker skill do?

Orchestrates complex tasks by breaking them into subtasks for parallel processing by multiple models, followed by verification and synthesis of results.

How do I install advisor-orchestrator-worker?

Open the “Installation” block on this page and pick your agent — Claude Code, Codex, Cursor or Gemini CLI — for ready-made commands that copy the skill folder to the right place. Or copy the prompt from the “Via assistant” tab and let the assistant review and install the skill.

Which AI models work with advisor-orchestrator-worker?

Skills are plain Markdown instructions (the Agent Skills format), so advisor-orchestrator-worker works with any agent that reads SKILL.md: Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and more. The model loads the instructions only when a task matches the skill's description.

Is advisor-orchestrator-worker free?

The skill is published openly by Shubhamsaboo under the Apache-2.0 license. The skill itself costs nothing — you only pay your AI provider for the tokens you use.

Is it safe to install skills?

A skill is a set of instructions your agent will follow, so read SKILL.md and its companion files before installing. Scripts in the skill folder only run if your agent runs them — keep an eye on what it does and do not grant unnecessary permissions.

How is a skill different from an MCP server or a plugin?

An MCP server or plugin gives the model new tools — code that runs and returns a result. A skill gives it knowledge and process: how to approach a task, which steps to follow and what good output looks like. It needs no server or API key.

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