advisor-orchestrator-worker

Shubhamsaboo/awesome-llm-apps · Agent Skill

Karmaşık görevleri birden çok modelin paralel işlemesi için alt görevlere ayırarak, ardından sonuçları doğrulayıp sentezleyerek düzenler.

Kuruluma geç ↓

Genel bakış

YayıncıShubhamsabooTopluluk
DepoShubhamsaboo/awesome-llm-apps ↗
Skill klasörüadvisor-orchestrator-worker
Yıldız138.130
Fork20.293
Dosyalar6
LisansApache-2.0
Son güncelleme14 Eylül 2026
BağlantılarSkill kaynağı ↗ · Depo ↗

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Kurulum

Skill, SKILL.md dosyası içeren bir klasördür. Ajanınızı seçin ve komutları proje kökünde bir terminalde çalıştırın (macOS, Linux veya Windows'ta Git Bash).

Promptu Claude, Codex veya başka bir asistana yapıştırın: skill'i inceleyip sizin için kuracaktır.

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.

Kaynak dosyaları

6 dosya ZIP indir

Kurulumdan önce SKILL.md ve eşlik eden dosyaları inceleyin. Önizleme için dosyaya tıklayın.

SKILL.md içeriği

Yazarın orijinal metni · salt okunur

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.

Sık sorulan sorular

advisor-orchestrator-worker skill'i ne yapar?

Karmaşık görevleri birden çok modelin paralel işlemesi için alt görevlere ayırarak, ardından sonuçları doğrulayıp sentezleyerek düzenler.

advisor-orchestrator-worker nasıl kurulur?

Bu sayfadaki “Kurulum” bölümünü açın ve ajanınızı seçin — Claude Code, Codex, Cursor veya Gemini CLI: skill klasörünü doğru yere kopyalayan hazır komutlar orada. Ya da “Asistanla” sekmesindeki promptu kopyalayın; asistan skill'i inceleyip kuracaktır.

advisor-orchestrator-worker hangi yapay zekâlarla çalışır?

Skill'ler düz Markdown talimatlarıdır (Agent Skills formatı), bu yüzden advisor-orchestrator-worker SKILL.md okuyan her ajanla çalışır: Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode ve diğerleri. Model talimatları yalnızca görev skill açıklamasına uyduğunda yükler.

advisor-orchestrator-worker ücretsiz mi?

Skill, Shubhamsaboo tarafından açık olarak yayımlandı (Apache-2.0 lisansı). Skill'in kendisi ücretsizdir — yalnızca kullandığınız token'lar için yapay zekâ sağlayıcınıza ödeme yaparsınız.

Skill kurmak güvenli mi?

Skill, ajanınızın izleyeceği talimatlardır; kurmadan önce SKILL.md ve eşlik eden dosyaları okuyun. Skill klasöründeki betikler yalnızca ajanınız çalıştırırsa çalışır — ne yaptığını izleyin ve gereksiz izin vermeyin.

Skill, MCP sunucusu veya eklentiden nasıl farklıdır?

MCP sunucusu veya eklenti modele yeni araçlar verir — çalışıp sonuç döndüren kod. Skill ise bilgi ve süreç verir: göreve nasıl yaklaşılacağı, hangi adımların izleneceği ve iyi sonucun neye benzediği. Sunucu veya API anahtarı gerekmez.

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