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 skills add rnixai/rnix --skill decomposegit clone --depth 1 https://github.com/rnixai/rnixWrote 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/rnixai/rnix/decompose)<a href="https://agentmods.dev/skills/rnixai/rnix/decompose"><img src="https://agentmods.dev/badge/skills/rnixai/rnix/decompose/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/rnixai/rnix/decompose"><img src="https://agentmods.dev/badge/skills/rnixai/rnix/decompose.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00049 | $0.00724 |
| Opus 5.5 | $0.00020 | $0.00290 |
| Sonnet 5.5 | $0.00010 | $0.00145 |
| Haiku 4.5 | $0.00005 | $0.00072 |
Grade A, and why
decompose 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 1mo 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.
What it actually says
Intent Decompose
使用场景
当用户给出一个高层目标或复杂意图时，使用此技能将其分解为结构化的子任务 DAG（有向无环图）。
分解规则
将高层意图分解为具体子任务时，遵循以下原则：
- 每个子任务用 JSON 对象表示，包含 id（短标识符）、intent（具体任务描述）、depends_on（依赖的子任务 id 列表，无依赖则为空数组）
- 子任务粒度适中——每个子任务应能由单个智能体独立完成
- 正确声明依赖关系——有数据流依赖的任务必须声明 depends_on
- 返回纯 JSON 数组，不要包含其他文本
示例分解结果:
[
{"id": "design", "intent": "设计数据模型和 API 接口", "depends_on": []},
{"id": "backend", "intent": "实现后端 API 服务", "depends_on": ["design"]},
{"id": "frontend", "intent": "实现前端界面", "depends_on": ["design"]},
{"id": "test", "intent": "编写集成测试", "depends_on": ["backend", "frontend"]}
]
增量更新规则
当需要在已有意图树上增量更新时：
- 分析新需求与现有子任务的关系
- 返回合并后的完整子任务列表（包括已有的和新增的）
- 已有子任务保持原 id 不变；新增子任务用新 id
- 如果新需求修改了已有子任务的目标，更新其 intent 字段
- 正确声明 depends_on 依赖关系
工作流程
意图从分解到完成遵循五阶段生命周期，每阶段关注"做什么 / 判断什么"：
- 分解 — 将高层意图拆解为子任务 DAG，明确每个子任务的 id、intent 与 depends_on 依赖（遵循上方《分解规则》）。
- 审查 — 检查分解结果：子任务粒度是否适中、依赖关系是否完整且无环、是否覆盖原始意图的全部目标。
- 确认 — 在执行前批准该计划；确认即冻结当前 DAG 结构，进入执行阶段。
- 执行 — 按依赖拓扑顺序推进子任务：依赖已满足的节点可并行执行，未满足的等待其上游完成。
- 监控 — 跟踪各子任务的执行进度与状态，识别失败或漂移；失败节点会级联标记其下游依赖，必要时回到分解阶段做增量更新（见上方《增量更新规则》）。
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.
- 1mo ago First seen · 57 lines · 49 tokens per session scan A 455536ba7ef9
decompose is a skill published in the GitHub repository rnixai/rnix (5 stars, last pushed 2mo ago), licensed MIT. It adds 49 tokens to every session and 724 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-31.
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