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/fitlab-ai/agent-infra/complete-tasknpx skills add fitlab-ai/agent-infra --skill complete-taskgit 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.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 | $0.00046 | $0.04235 |
| Opus 5 | $0.00023 | $0.02117 |
| Sonnet 5 | $0.00009 | $0.00847 |
| Haiku 4.5 | $0.00005 | $0.00424 |
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 yesterday.
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 — 261 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 v2(不可变 receiptId、单调 revision 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,评论、外围验证和其他同步失败返回 result: completed_with_warnings 及六字段 warning,并仅重试 receipt 中的 pending step。
行为边界 / 关键规则
- 本命令更新任务元数据并物理移动任务目录
- 除非强制执行,不要转移有未完成工作流步骤的任务
- 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,并把原文粘贴到本轮产物的 ## 状态核对 段:
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 .agents/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 只重试归档后的 task 评论与终态门禁。
2. 验证完成前置条件(未满足则必须停止)
先读取 reference/external-delivery.md,然后在 active 任务上调用:
agent-infra-internal platform-pr resolve-external {task-id} --agent {standard-agent-token} [--pr {external-pr}]
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.
- yesterday Changed 019badea8678
- 5d ago First seen · 261 lines · 46 tokens per session scan A ee8b7298d84d
complete-task is a skill published in the GitHub repository fitlab-ai/agent-infra (83 stars, last pushed today), licensed MIT. It adds 46 tokens to every session and 4,235 once invoked, about $0.0002 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
contextualize
Detect application-context mismatch after execution. Fires when correct output may not fit the actual context. Type: (ApplicationDecontextualized, AI, CONTEXTUALIZE, Result) → ContextualizedExecution.
review-loop
Convergence-paced review-resolve loop over a change and its governing surfaces. Verifies each finding against the codebase and the base it is measured from, then re-reviews until each is disposed of.
conduct
Conduct method before object-level work. Fires when the work needs several moves in non-trivial order. Type: (MethodUnderdetermined, Hybrid, CONDUCT, WorkProspect × MoveGround) → ConductedMethod.
apportion
Apportion an autonomous goal into execution units carrying their own completion conditions. Type: (GoalPlanUncompiled, User, APPORTION, AutonomousGoal × ExecutionHorizon) → ConditionBearingUnitPlan.
ground
Validate structural mapping between abstract and concrete domains. Presents concrete instantiations when mapping uncertainty is detected. Type: (MappingUncertain, AI, GROUND, R) → ValidatedMapping.
steer
Project-profile recalibration. Audits session calibration drift, presents per-cluster evidence for a user verdict, writes the updated project-profile rule, and inscribes a settled-direction clause.