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/check-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/check-task)<a href="https://agentmods.dev/skills/fitlab-ai/agent-infra/check-task"><img src="https://agentmods.dev/badge/skills/fitlab-ai/agent-infra/check-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/check-task"><img src="https://agentmods.dev/badge/skills/fitlab-ai/agent-infra/check-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 | $0.00046 | $0.01274 |
| Opus 5.5 | $0.00018 | $0.00510 |
| Sonnet 5.5 | $0.00009 | $0.00255 |
| Haiku 4.5 | $0.00005 | $0.00127 |
Grade A, and why
check-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 23d 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
查看任务状态
行为边界 / 关键规则
- 本技能是只读操作 —— 不修改任何文件
- 机械数据(frontmatter 元数据、产物分组、Git/Platform 状态,以及跨 active、blocked、completed 三目录定位任务)一律委托给确定性的
ai task status命令。本技能只负责 CLI 无法产出的语义层:工作流阶段解读和审查结论解析;下一步 action 直接使用 CLI 的 canonical recommendation。
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。
执行步骤
1. 通过 ai task status 收集事实
运行确定性 CLI 收集全部机械数据,并以其 stdout 作为状态报告的事实基底:
ai task status --task {task-id}
该命令会跨 active、blocked、completed 三目录解析任务,并输出五段:任务头(id、短号、标题)、Metadata(frontmatter 字段)、Artifacts(按工作流阶段分组的产物)、Git(分支匹配、未提交数、ahead/behind)、Platform(Issue/PR 状态)。把该输出视为权威事实 —— 不要再手工复述其中任何内容。
降级处理:
- 若命令不可用(如
ai不在 PATH 或dist/未构建)或非零退出,回退到降级读取:展示task.mdfrontmatter 并ls任务目录,同时告知用户本次为降级输出(建议先构建或安装 CLI,如ai init)。 - 若在任何目录都未找到任务,提示 "Task {task-id} not found"。
2. 解读工作流阶段与审查结论
这是 CLI 不产出的语义层。基于步骤 1 的 Artifacts 分组与 task.md 的活动日志:
- 把每个工作流阶段映射为状态指示器,并标注其最新产物与轮次:
[done]- 步骤已完成[current]- 当前进行中[pending]- 尚未开始[blocked]- 被阻塞[skipped]- 已跳过
- 对各阶段最新的审查产物(
review-analysis、review-plan、review-code),读取报告正文并解析结论:总体结论(通过 / 需要修改 / 拒绝)与阻塞项 / 主要问题 / 次要问题计数。这些结论只用于阶段状态展示和解释,不得用于重算下一步 action。
把工作流进度作为 CLI 输出之上的叠加层呈现,标注最新轮次与解析出的审查结论,例如:
工作流进度:
[done] 需求分析 analysis.md (Round 1, latest)
[done] 需求分析审查 review-analysis.md (Round 1, latest, 通过)
[current] 技术设计 plan.md (Round 1)
[pending] 技术方案审查
3. 展示 canonical recommendation
只读取 ai task status --task {task-id} 输出中的 Recommendation.action,将其作为唯一下一步来源。不要根据 current_step、目录中的最新产物、审查正文或 Activity Log 再计算 action。
当 action 非空时,仅做以下 action 到 skill 的字面转换,然后调用一次统一 helper:
| Recommendation.action | next skill |
|---|---|
analysis |
analyze-task |
plan |
plan-task |
code |
code-task |
| 其他 action | 同名 skill |
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.
- 23d ago Changed db752d934769
- 1mo ago Changed · -21 lines 42ee0dff8d79
- 1mo ago Changed ffbb644c8c50
- 1mo ago First seen · 102 lines · 46 tokens per session scan A f81426a3b605
check-task is a skill published in the GitHub repository fitlab-ai/agent-infra (86 stars, last pushed yesterday), licensed MIT. It adds 46 tokens to every session and 1,274 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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