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 agentmods add skills/nexus-research-lab/nexus/execution-orchestratornpx skills add nexus-research-lab/nexus --skill execution-orchestratorgit clone --depth 1 https://github.com/nexus-research-lab/nexusWrote 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/nexus-research-lab/nexus/execution-orchestrator)<a href="https://agentmods.dev/skills/nexus-research-lab/nexus/execution-orchestrator"><img src="https://agentmods.dev/badge/skills/nexus-research-lab/nexus/execution-orchestrator.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00072 | $0.01177 |
| Opus 5 | $0.00036 | $0.00589 |
| Sonnet 5 | $0.00014 | $0.00235 |
| Haiku 4.5 | $0.00007 | $0.00118 |
Grade A, and why
execution-orchestrator scanned grade A with 0 findings 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 6d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Execution Orchestrator
Execution 管理“当前责任如何交付”；Goal 管理“什么目标需要跨轮持续追求”。两者可以独立存在，也可以显式绑定，不以任务长度、Room 人数或是否调用 Subagent 相互推断。
入口与命令协议
-
substantial execution 前先判断直接执行、局部 Task、Subagent、WorkGraph、Room Assignment、Gate/Loop 或 Goal 中哪些结构真的降低风险；简单原子任务直接完成。
-
只有需要读取或改变受管责任图时才调用宿主提供的
nexus.command。先调用：{"domain":"execution","action":"inspect"}明确读取同一可信 scope 的历史图时才传
input.execution_id。 每轮动态上下文只自动保留运行中、异常、Artifact 和控制回连事实；显式 inspect 会附带有界successful_nodes，需要恢复已完成工具结果时先读它，不要盲目重做。 -
只从最新
data.execution_context.allowed_actions选择动作。mutation 前读取 fresh exact contract：{"domain":"execution","action":"contract","operation":"<operation>"}完整遵守返回的 contract；不要凭 Skill、记忆或旧 round 重建字段、identity、authority 或 revision。
-
业务输入是
additionalProperties=false的 closed object，直接放在工具的input字段中。只提交 freshinput_schema.properties中属于当前意图的字段；opaque locator 来自 inspect/receipt，不从标题或正文猜。相同语义重试复用request_id，operation、目标或输入变化时换新 ID。 -
只有顶层
is_error=false且data.outcome=applied表示 mutation 已应用；prepare_plan_execution成功是data.outcome=prepared。next_actions是建议，不授权，始终服从同一结果里的最新 lane、binding 和allowed_actions。
context_status=refresh_required 时在本轮重新 inspect；round_refresh_required 表示旧 round authority 已失效，立即结束本轮，不再 inspect、改 Plan 或重试 mutation，等待宿主 successor round。
按当前动作读取参考
- 选择直接执行、Task、Subagent、Room 或 WorkGraph：references/structure-selection.md
- 创建、replan、replace、abandon 或提交 sealed Plan：references/graph-control.md
- assign、submit、review 或 takeover：references/responsibility-and-delivery.md
- block、resume、Execution audit、Goal promotion 与跨域收口：references/recovery-and-alignment.md
- 查询历史图、提取/恢复 Draft、版本化编辑、保存或复用命名 WorkGraph Slash：references/workgraph-distillation.md
- Room/父子 Agent 的内容传递、并行与连续执行：references/communication-and-continuity.md
What ships with it
6 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.
- 6d ago First seen · 53 lines · 72 tokens per session scan A b526c137e523
execution-orchestrator is a skill published in the GitHub repository nexus-research-lab/nexus (143 stars, last pushed yesterday), licensed Apache-2.0. It adds 72 tokens to every session and 1,177 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
smoke-test
End-to-end smoke test skill for DeerFlow. Guides through: 1) Pulling latest code, 2) Docker OR Local installation and deployment (user preference, default to Local if Docker network issues), 3) Service availability verification, 4) Health check, 5) Final test report. Use when the user says "run smoke test", "smoke…
podcast-generation
Use this skill when the user requests to generate, create, or produce podcasts from text content. Converts written content into a two-host conversational podcast audio format with natural dialogue.
vercel-deploy
Deploy applications and websites to Vercel. Use this skill when the user requests deployment actions such as "Deploy my app", "Deploy this to production", "Create a preview deployment", "Deploy and give me the link", or "Push this live". No authentication required - returns preview URL and claimable deployment link.
skill-reviewer
Reviews DeerFlow skill packages for readiness, triggers, safety boundaries, resources, and evidence. Invoke when users ask to audit, grade, or production-check an existing skill.
surprise-me
Create a delightful, unexpected "wow" experience for the user by dynamically discovering and creatively combining other enabled skills. Triggers when the user says "surprise me" or any request expressing a desire for an unexpected creative showcase. Also triggers when the user is bored, wants inspiration, or asks for…
zh-output-example
Reviews Chinese skill documentation for publication readiness. Invoke when the user asks in Chinese to review a skill.