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/tengdagg/mom_platform/task.executenpx skills add tengdagg/MOM_Platform --skill task.executegit clone --depth 1 https://github.com/tengdagg/MOM_PlatformWhat 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 | $0.00092 | $0.01194 |
| Opus 5 | $0.00046 | $0.00597 |
| Sonnet 5 | $0.00018 | $0.00239 |
| Haiku 4.5 | $0.00009 | $0.00119 |
Grade C, and why
task.execute scanned grade C with 1 finding 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 2d 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
- 自动拒绝危险命令：`rm -rf /`、`mkfs`、`dd if=`、`shutdown`、`reboot`、`init 0`、`init 6`、Fork 炸弹等 The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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.
- 2d ago First seen · 127 lines · 92 tokens per session scan C c6de9cf7624d
task.execute is a skill published in the GitHub repository tengdagg/MOM_Platform (22 stars, last pushed 5mo ago), with no licence file. It adds 92 tokens to every session and 1,194 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
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state-trees
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task-loop
任务目标驱动执行闭环预装 skill,整合任务识别/规划/派发搜推/验收/BBS 接力/arch 场景规划变体(planning-arch)与架构师名册 mock(arch-analysis)共七段为单一 skill,预装到所有 bot 等同各段单独安装到对应 bot;各段按各自触发词自门控仅命中段执行(用户面 /task 或 [RESUMETASK] 或副屏标签命中识别;框架 [planning] 命中规划,arch 场景含「某某某公司」命中 planning-arch 变体;框架 [search] 命中派发搜推;worker 叶子自验收命中验收;引擎 BBS 通知命中接力,其 scoped 叶子 instruction…
bbs-relay-pickup
被唤醒时从 task API 发现 BBS 升级任务、CAS 占根、自判剩余、挂节点、执行、经回投写回.
task-planning-arch
计算任务 gap 并产出下一步可执行子任务 List[TaskSpec];gap 已闭返回空数组。对齐 arch 场景(架构师名册/技术栈概览/双视角分析)确定式分解——按根目标交付物集合 + donechildren 查表(参照 task-planning storage 特例,非自由 LLM 分解)。.
task-search
在框架预查的候选 bot 集里决出执行者(who)与协作方式(how),返回 4 态 SearchResult(HITSINGLE/HITGROUP/HITMULTIBOTS/MISS)。对齐案例剧本确定式映射。.