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-codescan/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-codescan)<a href="https://agentmods.dev/skills/fitlab-ai/agent-infra/import-codescan"><img src="https://agentmods.dev/badge/skills/fitlab-ai/agent-infra/import-codescan/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-codescan"><img src="https://agentmods.dev/badge/skills/fitlab-ai/agent-infra/import-codescan.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 | $0.00041 | $0.01490 |
| Opus 5.5 | $0.00016 | $0.00596 |
| Sonnet 5.5 | $0.00008 | $0.00298 |
| Haiku 4.5 | $0.00004 | $0.00149 |
Grade A, and why
import-codescan scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
导入 Code Scanning 告警
导入指定的 Code Scanning(CodeQL)告警并创建修复任务。
行为边界 / 关键规则
- 本技能仅负责导入告警并创建任务骨架 —— 不直接修改业务代码或关闭告警
- 不要自动提交。绝不自动执行
git commit或git add - 执行本技能后,你必须立即更新 task.md 中的任务状态
任务入参短号别名
如果
{task-id}入参匹配^[#]?[0-9]+$(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):
- {started_at} — **Import Codescan [started]** by {agent} — started
- {done_at} — **Import Codescan** 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. 获取告警信息
执行前先读取 .agents/rules/security-alerts.md,然后运行 agent-infra-internal platform-security read --kind code-scanning --number {alert-number},解析其 JSON 结果获取告警详情。
提取关键信息:
number:告警编号state:状态(open/dismissed/fixed)rule:规则信息(id、severity、description、security_severity_level)tool:扫描工具信息(name、version)most_recent_instance:位置(path、start_line、end_line)、消息html_url:平台告警链接
2. 创建任务目录和文件
检查是否已存在该告警的任务。如果不存在,创建:
目录:<task-workspace>/active/TASK-{yyyyMMdd-HHmmss}/
任务元数据:
id: TASK-{yyyyMMdd-HHmmss}
codescan_alert_number: <alert-number>
3. 更新任务状态
获取当前时间:
date "+%Y-%m-%d %H:%M:%S%z" | sed 's/\([+-][0-9][0-9]\)\([0-9][0-9]\)$/\1:\2/'
更新 task.md:current_step -> requirement-analysis。
- 追加到
## Activity Log(不要覆盖之前的记录):- {YYYY-MM-DD HH:mm:ss±HH:MM} — **Import Codescan** by {agent} — Code Scanning alert #{alert-number} imported
4. 完成校验
先调用短号分配(保证注册表 entry 已分配;完成校验阶段会读取):
node .agents/scripts/task-short-id.js alloc "$task_id"
如失败(退出码非 0),按提示「归档若干任务」或「调高 task.shortIdLength」处理;不要继续执行后续步骤。
运行完成校验,确认任务产物和同步状态符合规范:
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 bac57fc78174
- 24d ago Changed 1ea686633205
- 1mo ago Changed 26d88e1fb29a
- 1mo ago Changed 936a52250e17
- 1mo ago Changed d67fd1e494c7
- 1mo ago First seen · 135 lines · 41 tokens per session scan A 2a84e11fd99c
import-codescan is a skill published in the GitHub repository fitlab-ai/agent-infra (86 stars, last pushed yesterday), licensed MIT. It adds 41 tokens to every session and 1,490 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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