advisor-orchestrator-worker

Shubhamsaboo/awesome-llm-apps · Agent Skill

透過將複雜任務分解為子任務以供多個模型並行處理,然後進行驗證和結果綜合來進行協調。

前往安裝 ↓

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發布者Shubhamsaboo社群
儲存庫Shubhamsaboo/awesome-llm-apps ↗
技能資料夾advisor-orchestrator-worker
星138,130
分支20,293
附帶檔案6
授權Apache-2.0
最近更新2026年9月14日
連結技能原始碼 ↗ · 儲存庫 ↗

Mozgi 檢查

我們在技能檔案中發現的內容。詳細資訊需訂閱。

安全性
中等風險 · 發現 2 個需要注意的地方
相容性
僅含說明 — 適用於多數代理
相似技能
找到 51 個相似技能
訂閱另含

安裝

技能就是一個含 SKILL.md 的資料夾。選擇你的代理,在專案根目錄的終端機(macOS、Linux 或 Windows 的 Git Bash)執行指令。

把提示貼到 Claude、Codex 或其他助理:它會自行檢查並安裝技能。

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.

技能檔案

6 個檔案 下載 ZIP

安裝前請先檢視 SKILL.md 與相關檔案。點擊檔案即可預覽。

SKILL.md 內容

作者原文 · 唯讀

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.

常見問題

advisor-orchestrator-worker 技能有什麼用途?

透過將複雜任務分解為子任務以供多個模型並行處理,然後進行驗證和結果綜合來進行協調。

如何安裝 advisor-orchestrator-worker?

打開本頁的「安裝」區塊並選擇你的代理 — Claude Code、Codex、Cursor 或 Gemini CLI:那裡有把技能資料夾複製到正確位置的現成指令。也可以複製「透過助理」分頁中的提示,讓助理自行檢查並安裝技能。

advisor-orchestrator-worker 適用哪些 AI 模型?

技能是純 Markdown 指示(Agent Skills 格式),因此 advisor-orchestrator-worker 適用任何讀取 SKILL.md 的代理:Claude Code、Codex、Cursor、Gemini CLI、GitHub Copilot、OpenCode 等。只有在任務符合技能描述時,模型才會載入指示。

advisor-orchestrator-worker 是免費的嗎?

此技能由 Shubhamsaboo 公開發布,採用 Apache-2.0 授權。技能本身免費 — 你只需向 AI 供應商支付實際使用的 token 費用。

安裝技能安全嗎?

技能是代理會遵循的指示,安裝前請閱讀 SKILL.md 及相關檔案。技能資料夾中的腳本只有在代理執行時才會運行 — 留意它的動作,不要授予多餘權限。

技能和 MCP 伺服器或外掛有何不同?

MCP 伺服器或外掛為模型提供新工具 — 會執行並回傳結果的程式碼。技能提供的是知識與流程:如何處理任務、要遵循哪些步驟、好的成果長什麼樣子。它不需要伺服器或 API 金鑰。

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