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/import-issue/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/import-issue)<a href="https://agentmods.dev/skills/fitlab-ai/agent-infra/import-issue"><img src="https://agentmods.dev/badge/skills/fitlab-ai/agent-infra/import-issue/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/import-issue"><img src="https://agentmods.dev/badge/skills/fitlab-ai/agent-infra/import-issue.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 | $0.00029 | $0.02957 |
| Opus 5.5 | $0.00012 | $0.01183 |
| Sonnet 5.5 | $0.00006 | $0.00591 |
| Haiku 4.5 | $0.00003 | $0.00296 |
Grade A, and why
import-issue scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 230 lines — stays where its author put it; the contents beside it link to each section on GitHub.
导入 Issue
--agent取值见.agents/rules/task-management.md「合作者 token 规范」。
导入指定的 Issue 并创建任务。参数:Issue 原始 token,由选定 provider 解析。
行为边界 / 关键规则
- 本技能的唯一产出是
task.md - 不要编写或修改业务代码。仅做导入
- 执行本技能后,你必须立即更新任务状态
任务入参短号别名
如果
{task-id}入参匹配^[#]?[0-9]+$(裸数字或带#前缀),先读取.agents/rules/task-short-id.md的「SKILL 入参解析」段执行解析;后续命令视{task-id}为解析后的全长TASK-YYYYMMDD-HHMMSS形式。
步骤开始:记录开始时间
本技能会创建 task.md,开始时尚无文件可写。先在内存记录开始时间 started_at(date "+%Y-%m-%d %H:%M:%S%z" | sed 's/\([+-][0-9][0-9]\)\([0-9][0-9]\)$/\1:\2/');在最后写活动日志时一次性补两条——started 行用 started_at、done 行用完成时间,二者同基名(started 行 action 加 [started] 后缀、note 用 started)。基名必须跟实际导入场景一致:
# 场景 B:新 Issue 导入
- {started_at} — **Import Issue [started]** by {agent} — started
- {done_at} — **Import Issue** by {agent} — {完成说明}
# 场景 C:从历史 Issue 评论恢复
- {started_at} — **Import Issue (Recovered) [started]** by {agent} — started
- {done_at} — **Import Issue (Recovered)** by {agent} — {完成说明}
ai task log 会按基名把两条配对成一行(进行中 → 已完成)。约定见 .agents/rules/task-management.md 的「Activity Log started / done 双标记约定」。
执行流程
1. 获取 Issue 信息
执行前先读取 .agents/rules/issue-pr-commands.md,并按其中的前置步骤完成认证和代码托管平台检测;随后按其中的 “读取 Issue” 命令获取 Issue 信息。
提取:Issue identity、标题、描述、标签。
从 Issue 标题派生任务标题:按下方契约剥掉可选的单层前导 Conventional Commits 前缀,其余描述原文与原始语言保持不变。下方 fenced 契约是权威的、与语言无关的规则——在所有 import-issue 变体中保持逐字节一致:
# title-derivation-contract
strip-prefix: type(scope):
prefix-types: feat fix docs style refactor perf test build ci chore revert
single-layer-only: true
preserve-body-colon: true
keep-when-no-prefix: true
example-strip: "feat(meta): create-pr summary" => "create-pr summary"
example-keep: "修复某问题" => "修复某问题"
example-single-layer: "feat: add A: B" => "add A: B"
只剥第一层前缀,且仅当前导 token 是 prefix-types 之一、其后可带 (scope) 与 !,再接 : 和至少一个空格;描述正文中的冒号永远不算前缀。
2. 检查已有任务
2.1 搜索 <task-workspace>/active/ 中是否已有链接到此 Issue 的任务。
- 如果找到,默认复用现有任务(场景 A),不询问用户;在最终告知中明确「已复用现有任务
{task-id},未重新导入」。若用户希望重新导入,需要先手动归档/删除已有任务再次执行本技能 - 如果未找到,继续执行 2.2
What ships with it
1 file 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.
- 6d ago Changed 5dcb3c5fc5c6
- 24d ago Changed 79d325ab7817
- 1mo ago Changed 14f46d77f538
- 1mo ago Changed 374ef516e53b
- 1mo ago First seen · 230 lines · 29 tokens per session scan A 155481077614
import-issue is a skill published in the GitHub repository fitlab-ai/agent-infra (86 stars, last pushed yesterday), licensed MIT. It adds 29 tokens to every session and 2,957 once invoked, about $0.0001 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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