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/inclusionai/avernet/task-loopnpx skills add inclusionAI/Avernet --skill task-loopgit clone --depth 1 https://github.com/inclusionAI/AvernetWhat 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 | $0.00210 | $0.16362 |
| Opus 5 | $0.00105 | $0.08181 |
| Sonnet 5 | $0.00042 | $0.03272 |
| Haiku 4.5 | $0.00021 | $0.01636 |
Grade A, and why
task-loop 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 3d 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 — 984 lines — stays where its author put it; the contents beside it link to each section on GitHub.
task-loop
预装到所有 bot 的任务目标驱动执行闭环 skill。本 skill 内含七段,只执行被触发词命中的那一段,其余段不参与;给任一 bot 预装本 skill 等同把对应段单独安装到该 bot。
- 任务识别(recognition):对话 bot 用户面——
/task或平台[RESUME_TASK]回传或仅副屏标签 - 任务规划(planning):owner bot——框架 prompt 头部
[planning](非 arch 场景) - 任务规划·arch 场景(planning-arch):owner bot——框架
[planning]且 prompt 含「某某某公司」,按根验收交付物集合 + done_children 确定式查表 - 任务派发搜推(search):owner bot——框架 prompt 头部
[search] - 任务验收(acceptance):协作群 driver/owner bot——群产出后自验收并 push 上报(single_bot 叶子由框架内联 JSON 走 poll,不走本段)
- BBS 接力(bbs-relay-single-task):中继 bot——引擎主动通知
- 架构师名册 mock(arch-analysis):中继/worker bot——叶子 instruction 含「某某某公司」,返伪造架构师名册
段体取自各段真源 SKILL.md,仅标题层级统一降一级并入;各段逻辑 / 规则 / 卡片格式 / 触发与输出契约保持原样。先读下面"路由规则"确定本该执行哪段,再只跑那一段。
路由规则(最先读,只跑命中段)
按本次收到的触发词 / 上下文分流(不按 bot 身份;同一 bot 按其收到的触发执行对应段):
| 触发 / 上下文 | 命中段 | 执行要点 |
|---|---|---|
用户消息以 /task 开头;或上下文含 [RESUME_TASK];或消息仅 <AixUI type="panel" component="taskPanel.TaskLoopView" ...> 副屏标签 |
段1 任务识别 | 出 AixUI 卡片(cardId 固定 card_3e31e1f1),到 task_ready 为止;执行由平台层调 POST /api/v1/collaboration/tasks/execute |
prompt 头部标记 [planning],含目标节点 node_id 与任务态快照,且 prompt 不含「某某某公司」(非 arch 场景) |
段2 任务规划 | 返回 JSON 对象 {tasks: List[TaskSpec], has_gap, gap_detail};tasks 为空即 gap 闭=验收通过 |
prompt 头部标记 [planning],且 prompt 含「某某某公司」(arch 场景;交付物含架构师名册/技术栈概览/双视角分析等) |
段7 任务规划·arch 场景 | 同段2 输出契约;按根验收交付物集合 + done_children 确定式查表产 N_tech_stack / N_dual_view / N_architects |
prompt 头部标记 [search],含子任务需求与候选集 catalog |
段3 任务派发搜推 | 返回 4 态 JSON(HIT_SINGLE / HIT_GROUP / HIT_MULTI_BOTS / MISS) |
| 你是协作群 driver/owner bot,群已跑完叶子并产出交付物,需按其 goal.acceptances 自验收并上报(从群上下文取 {backend}/{loop_task_id}) | 段4 任务验收 | push:POST {backend}/api/v1/collaboration/tasks/callback/report {loop_task_id,result{success,data,gaps}}→on_report;single_bot 叶子不走本段(框架内联 JSON→poll) |
| 收到引擎主动发的 BBS 任务消息(含 task_id + 后端 base url + 自身 bot_id,且引擎已替你占根) | 段5 BBS 接力 | 跳过 scan / claim / 自判,直接 attach -> 执行 -> result |
叶子执行输入(instruction)含关键词「某某某公司」,且非框架 [planning]/[search] 头 |
段6 架构师名册 mock | 不联网,直接返 mock 伪造架构师名册 JSON(domain/architects[]/note) |
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.
- 3d ago First seen · 984 lines · 210 tokens per session scan A 4da3e13ec7c3
task-loop is a skill published in the GitHub repository inclusionAI/Avernet (522 stars, last pushed 3d ago), licensed Apache-2.0. It adds 210 tokens to every session and 16,362 once invoked, about $0.0011 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.
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