Borrowing it
Nothing to install: this file belongs to fitlab-ai/agent-infra. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/fitlab-ai/agent-infra/main/.agents/skills/complete-task/SKILL.mdgit clone --depth 1 https://github.com/fitlab-ai/agent-infraWrote 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/fitlab-ai/agent-infra/complete-task)<a href="https://agentmods.dev/skills/fitlab-ai/agent-infra/complete-task"><img src="https://agentmods.dev/badge/skills/fitlab-ai/agent-infra/complete-task/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/fitlab-ai/agent-infra/complete-task"><img src="https://agentmods.dev/badge/skills/fitlab-ai/agent-infra/complete-task.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00043 | $0.04625 |
| Opus 5.5 | $0.00017 | $0.01850 |
| Sonnet 5.5 | $0.00009 | $0.00925 |
| Haiku 4.5 | $0.00004 | $0.00462 |
Grade A, and why
complete-task 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 — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.
完成任务
--agent取值见.agents/rules/task-management.md「合作者 token 规范」。
宿主 finalization 使用 receipt v4(不可变 receiptId、单调 revision、摘要暂存和 canonical warnings)。生命周期/身份/required PR 等硬失败返回 result: failed|blocked;生命周期完成后,只重试 receipt 指出的 pending 外围步骤或 open backfill warning。逐检查 verification warning 以 observed target 为单位转换:新观察替代旧 code 时保留旧行 resolved 历史,未观察的 target 不变。
行为边界 / 关键规则
持久化报告证据
生成收尾报告或同步内容时,先读取 .agents/rules/evidence-reporting.md。成功检查记录命令、范围、状态/结构化结果、实际结果和未覆盖部分;失败、阻塞或争议保留复现入口、准确位置和决定性摘录。
- 本命令由宿主更新任务元数据并原子移动任务目录
- 入口接受可选
--external-pr <N>;仅用于外部交付候选歧义时的显式选择,不绕过身份或平台门禁
版本戳规则:创建或更新 task.md frontmatter 时,先读取 .agents/rules/version-stamp.md,并写入或刷新 agent_infra_version。
第 0 步:状态核对(执行前硬约束)
在加载 workflow / skill / rules 指令之后、做任何任务状态判断或用户可见结论之前,必须先执行状态核对。指令类文件读取不算对外动作或结论。
运行以下命令,并在本轮产物的 ## 状态核对 段记录任务/产物范围、关键结果和未覆盖部分;正常成功不粘贴完整目录清单或 task.md 尾部。失败、阻塞、身份不一致或争议时,附决定性原文行:
agent-infra-internal task-snapshot {task-id} --format text
状态核对完成前,禁止任何关于外部状态的断言(例如“代码没变”“测试已通过”“没有其他引用”),包括思考阶段。本门禁只提供结构下限;逐条证据配对和真实性仍需按报告模板与审查要求核对。
任务上下文解析
入口可省略 task ref;显式 task scope 仅接受
--task <ref>或-t <ref>,不再解释位置 task ref。保留其余业务操作数后调用agent-infra-internal task-context resolve {task-scope};{task-scope}为空或 task flag 之一。只读取结构化结果的taskId,后续把{task-id}绑定为完整TASK-YYYYMMDD-HHMMSS。解析失败时透传非零退出码,不自行扫描任务。
解析任务引用,并确认任务位于本技能支持的状态或目录且存在
task.md;无法定位时按未找到任务处理并停止。
步骤开始:本地生命周期边界
正常完成路径在 active 阶段完成业务更新、平台同步和预完成门禁后,才由步骤 6 的单次 finalization intent 按固定顺序原子推进生命周期、终态 task 评论和完成校验;不得提前手工写入这些机械状态。已归档任务只允许进入 finalization-retry 场景,不回迁目录或重新执行 lifecycle。
执行步骤
1. 验证任务存在
检查任务是否存在于 <task-workspace>/active/{task-id}/。
注意:{task-id} 格式为 TASK-{yyyyMMdd-HHmmss},例如 TASK-20260306-143022
如果在 active/ 中未找到,检查 blocked/ 和 completed/:
- 如果在
completed/且 task.md 存在匹配的 Complete Task Activity Log:进入场景 Bfinalization-retry,跳过步骤 2-6,直接执行步骤 7 - 如果在
completed/但缺少匹配日志:告知用户任务已完成但终态身份不完整并停止,不手工修补 - 如果在
blocked/:告知用户任务被阻塞;建议先解除阻塞
场景 A 为 active 任务的正常完成路径;场景 B finalization-retry 重试允许的 artifact 回填、终态 task 评论、完成校验和摘要封口,不回迁生命周期。
What ships with it
2 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 Changed 5ff4dd573940
- 5d ago Changed · -15 lines 6b7610874f70
- 6d ago Changed 3f4e0918cbc2
- 9d ago Changed 9f59bdd746e5
- 14d ago Changed · +8 lines · -3 tokens per session 36ab3d3fa622
- 17d ago Changed · -18 lines 8a3020bbbf59
- 23d ago Changed · -3 lines b8a4f288013e
- 25d ago Changed · +1 lines f8e66cda7143
- 28d ago Changed · +2 lines 5d7d118c93d7
- 1mo ago First seen · 266 lines · 46 tokens per session scan A d2da7b159aed
complete-task is a skill published in the GitHub repository fitlab-ai/agent-infra (86 stars, last pushed yesterday), licensed MIT. It adds 43 tokens to every session and 4,625 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 2026-08-30.
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