check-my-prs
Skill2 starsChanged 19 days agoApache-2.0View on GitHub
Check your open MRs for feedback from reviewers. Shows MRs needing response, awaiting review, pipeline failed, needs rebase, and approved. Use when you want to see PR status, any MR feedback, or before creating more MRs.
In this repository124 other files it gives its agents.
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- check-mr-feedbackSkill
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- ci-retrySkill
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- cleanup-stale-executionsSkill
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- close-mrSkill
- cloud-inventorySkill
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- compare-optionsSkill
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- create-mrSkill
- create-slide-deckSkill
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- debug-prodSkill
- deploy-to-ephemeralSkill
- dev-workflow-orchestratorSkill
- discovered-work-summarySkill
- edit-slide-deckSkill
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- environment-overviewSkill
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- export-presentationSkill
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- find-similar-codeSkill
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- gdrive-researchSkill
- gke-cluster-opsSkill
- hello-worldSkill
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- The first 60 shown.
The file
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 2 are distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 8
Check your open MRs for feedback and status.
- Very specific: line 38
Use analyze_mr_status (shared parser): status = needs_response | approved | pipeline_failed | needs_rebase | awaiting
- Distinctive: line 50
Awaiting Review: no feedback yet
- Very specific: line 54
Update state/current_work → open_mrs with pipeline_status, needs_review
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Check My PRs
Check your open MRs for feedback and status.
Inputs
| Input | Type | Default | Purpose |
project | string | - | GitLab project (resolved from repo_name/cwd) |
repo_name | string | - | Config repo name |
show_approved | bool | true | Include approved MRs |
auto_merge | bool | false | Auto-merge approved MRs |
auto_rebase | bool | false | Auto-rebase MRs with conflicts |
slack_format | bool | false | Slack link format |
Workflow
1. Load Persona
persona_load("developer")
2. Resolve Project
project→repo_namefrom config → cwd → session project → defaultautomation-analytics/automation-analytics-backend
3. Get Username
config.user.usernameorUSERenv
4. List My MRs
gitlab_mr_list(project, state="opened", author="{my_username}")- Parse with
parse_mr_list(shared parser)
5. Check First MR
gitlab_mr_view(project, mr_id="{first_mr.iid}")- Use
analyze_mr_status(shared parser): status = needs_response | approved | pipeline_failed | needs_rebase | awaiting
6. Get Feedback Details (if needs_response)
gitlab_mr_viewagain for full comments
7. Auto-rebase (if auto_rebase and needs_rebase)
skill_run("rebase_pr", '{"mr_id": {iid}, "force_push": false}')
8. Build Summary
- Needs Your Response: reviewers, unresolved discussions
- Needs Rebase: merge conflicts
- Pipeline Failed: fix CI
- Awaiting Review: no feedback yet
- Approved: ready to merge
9. Memory
- Update
state/current_work→open_mrswith pipeline_status, needs_review memory_session_log("Checked {count} PRs", "First MR status: {status}")
10. Learn from Failures
- VPN:
learn_tool_fix("gitlab_mr_list", "no such host", "VPN", "vpn_connect()") - Merge conflict:
learn_tool_fix("gitlab_mr_merge", "merge conflict", "Conflicts", "skill_run rebase_pr")
MCP Tools
persona_loadgitlab_mr_listgitlab_mr_viewskill_run(rebase_pr)memory_session_logmemory_update(state/current_work)learn_tool_fix
Output
- Categorized MRs with links
- Quick actions: view comments, merge, rebase
- PR best practices, known issues
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

