Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/serejaris/kimi-skillsnpx agentmods add skills/serejaris/kimi-skills/glab-mrWrote 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/serejaris/kimi-skills/glab-mr)<a href="https://agentmods.dev/skills/serejaris/kimi-skills/glab-mr"><img src="https://agentmods.dev/badge/skills/serejaris/kimi-skills/glab-mr/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/serejaris/kimi-skills/glab-mr"><img src="https://agentmods.dev/badge/skills/serejaris/kimi-skills/glab-mr.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00092 | $0.02809 |
| Opus 5 | $0.00046 | $0.01404 |
| Sonnet 5 | $0.00018 | $0.00562 |
| Haiku 4.5 | $0.00009 | $0.00281 |
Grade A, and why
glab-mr scanned grade A with 2 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 7d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
import json, urllib.request, urllib.parse, subprocess Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
token = subprocess.run( How it starts
The opening of the file, as written. The whole thing — 352 lines — stays where its author put it; the contents beside it link to each section on GitHub.
glab mr
创建、查看和管理 GitLab merge request(合并请求)。
快速入门
# 从当前分支创建 MR
glab mr create --fill
# 列出我的 MR
glab mr list --assignee=@me
# 审查一个 MR
glab mr checkout 123
glab mr diff
glab mr approve
# 合并一个 MR
glab mr merge 123 --when-pipeline-succeeds --remove-source-branch
常用工作流
创建 MR
从当前分支创建:
glab mr create --fill --label bugfix --assignee @reviewer
从议题创建:
glab mr for 456 # 创建与议题 #456 关联的 MR
创建草稿 MR:
glab mr create --draft --title "WIP: Feature X"
审查工作流
-
列出待审查的 MR:
glab mr list --reviewer=@me --state=opened -
检出并测试:
glab mr checkout 123 npm test -
提交反馈:
glab mr note 123 -m "Looks good, one question about the cache logic" # 在添加备注的同时解决讨论线程(v1.88.0+) glab mr note 123 --resolve <discussion-id> -m "Fixed, addressed in latest commit." # 重新打开已解决的线程 glab mr note 123 --unresolve <discussion-id> -
批准:
glab mr approve 123
自动化审查工作流:
对于重复性的审查任务,可以使用自动化脚本:
scripts/mr-review-workflow.sh 123
scripts/mr-review-workflow.sh 123 "pnpm test"
该脚本自动完成:检出 → 运行测试 → 发布结果 → 测试通过则批准。
合并策略
流水线通过后自动合并:
glab mr merge 123 --when-pipeline-succeeds --remove-source-branch
压缩提交:
glab mr merge 123 --squash
合并前变基:
glab mr rebase 123
glab mr merge 123
故障排除
合并冲突:
- 检出 MR:
glab mr checkout 123 - 在编辑器中手动解决冲突
- 提交解决结果:
git add . && git commit - 推送:
git push
无法批准 MR:
- 检查你是否是作者(大多数配置下不能自我批准)
- 验证权限:
glab mr approvers 123 - 确保 MR 不处于草稿状态
流水线未运行但又是必需的:
- 检查分支中是否存在
.gitlab-ci.yml - 验证项目是否启用了 CI/CD
- 手动触发:
glab ci run
"MR already exists" 错误:
- 列出该分支的现有 MR:
glab mr list --source-branch <branch> - 如果已过时则关闭旧 MR:
glab mr close <id> - 或更新现有的:
glab mr update <id> --title "New title"
相关技能
处理议题:
- 参见
glab-issue了解议题的创建和管理 - 使用
glab mr for <issue-id>创建与议题关联的 MR - 脚本:
scripts/create-mr-from-issue.sh自动化分支创建 + MR 创建
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.
- 7d ago First seen · 352 lines · 92 tokens per session scan A 1108c4aa1c51
glab-mr is a skill published in the GitHub repository serejaris/kimi-skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 92 tokens to every session and 2,809 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 2 findings (makes network calls, runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
resolve-pr-comments
Evaluate, fix, answer, and reply to GitHub pull request review comments and conversation comments. Handles both change requests (fix or skip) and reviewer questions (explain using reasoning recalled from past Claude Code transcripts). Use when the user asks to "resolve PR comments", "fix review comments", "address PR…
fetch-pr-comments
Fetch and summarize review feedback and conversation from a GitHub PR (unresolved review threads, review bodies, and PR conversation comments) without making changes. Use when the user asks to "fetch PR comments", "show PR comments", "check PR for unresolved comments", "list review comments", "what comments are on the…
reply-to-pr-threads
Draft, confirm, and post replies to GitHub PR review threads. Handles per-category reply formatting, re-fetches thread resolution state so auto-resolved threads are skipped, and posts via GraphQL. Use when the user asks to "reply to PR threads", "post PR thread replies", or "draft PR reply messages".
reply-to-pr-conversation
Draft, confirm, and post a single conversational reply to GitHub PR conversation comments (issue comments). The reply addresses all tracked items in one natural-prose message. Use when the user asks to "reply to PR conversation", "post PR conversation replies", or "draft PR conversation messages".
review-pr
Review a pull request by fetching PR comments, running a comprehensive code review, evaluating findings, and dispatching to implementation. Use when the user asks to "review PR", "review pull request", "review this PR", "check PR before merging", or "full PR review".
audit-pr
Audit a whole PR against the delivery contract and return MERGE-READY or evidenced blockers with the full URL. Consumes the current review-change REVIEW-PASS receipt instead of re-running review axes; posts a SHA-bound ready comment; never edits or merges. Triggers: "audit-pr", "is this PR ready", "merge gate".