agentleFS
d-oitd-oit/rust-2026-template/.agents/skills/issue-triage/SKILL.md

issue-triage

Skill15 starsChanged 5 months agoMITView on GitHub

Read all open issues (GitHub/GitLab), categorize by type and effort, plan batch implementation order, and coordinate implementation across multiple issues in a single PR/MR. Auto-detects platform from git remote. Use when asked to "implement all issues", "fix all open issues", "batch implement", or "address all issues". Triggers: "all issues", "implement all", "batch issues", "open issues", "fix all", "address all issues".

  • Commits and pushes

63 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use meeting-notes-and-actions, pr-review-ci-fix and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • ComposioHQSkill

    meeting-notes-and-actions

    Turn meeting transcripts or rough notes into crisp summaries with decisions, risks, and owner-tagged action items; use for Zoom/Meet/Teams transcripts, call notes, or long meeting chats to generate share-ready outputs.

    Also in 4 projects that use this

  • ComposioHQSkill

    pr-review-ci-fix

    Automated PR review and CI auto-fix for GitHub and GitLab using the Composio CLI. Pulls diffs, fetches failing job logs, posts review comments, and loops fix commits until checks go green.

    Also in 4 projects that use this

  • FlorianBruniauxSkill

    tdd-rust

    Also in 3 projects that use this

  • GitHubSkill

    phoenix-cli

    Debug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, structure trace review with open coding and axial coding, inspect datasets, review experiments, query annotation configs, and use the GraphQL API. Use whenever the user is analyzing traces or spans, investigating LLM/agent failures, deciding what to do after instrumenting an app, building failure taxonomies, choosing what evals to write, or asking "what's going wrong", "what kinds of mistakes", or "where do I focus" — even without naming a technique.

    Also in 3 projects that use this

  • petestewartSkill

    datadog-logs

    Query Datadog logs via the Logs Search API v2. Use when the user asks to "check the logs", "search logs", "find errors in Datadog", "look up logs for", "show me production logs", "check for errors", "search Datadog", or any request involving log search, error investigation, or production debugging that requires Datadog log data.

    Also in 3 projects that use this

  • +7 more from ComposioHQ

The file

We split this skill into the rules it gives an agent

2 rules. 0 are common, 1 is distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 51 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Single PR/MR policy: All issues implemented in one PR/MR unless user specifies otherwise.

    line 25
  2. Very specific:

    Exception: When issues have clear dependency layers (e.g., models → API → frontend), stacked PRs via gh stack may be used instead.

    line 27

“Common” is measured: kept by at least 6 in 10 of the 51 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Issue Triage & Batch Implementation Skill

Overview

Workflow that orchestrates issue discovery → categorization → implementation planning → batch execution → CI verification → PR/MR merge.

Single PR/MR policy: All issues implemented in one PR/MR unless user specifies otherwise.

Exception: When issues have clear dependency layers (e.g., models → API → frontend), stacked PRs via gh stack may be used instead. See stacked-prs skill. Only when explicitly requested and repository is on GitHub.

Platform Detection

Auto-detect from git remote:

REMOTE=$(git remote get-url origin)
if echo "$REMOTE" | grep -qi "github"; then
  PLATFORM="github"; CLI="gh"
elif echo "$REMOTE" | grep -qi "gitlab"; then
  PLATFORM="gitlab"; CLI="glab"
else
  echo "Unknown platform - set --platform github|gitlab"
fi

CLI Command Mapping

ActionGitHub (gh)GitLab (glab)
List issuesgh issue list --state openglab issue list --state opened
Read issuegh issue view 123glab issue view 123
Create PR/MRgh pr create --title "..."glab mr create --title "..."
Check CIgh pr checksglab ci status
Mergegh pr merge 123 --squashglab mr merge 123 --squash
Close issuegh issue close 123glab issue close 123

Arguments

ArgumentDescriptionDefault
--repo OWNER/REPORepositoryauto-detect from git remote
--platform github|gitlabForce platformauto-detect
--state open|closed|allIssue filteropen (GitHub) / opened (GitLab)
--limit NMax issues to process50
--batch-size NIssues per PR/MRall
--dry-runPlan only, no implementationfalse
--skip-mergeCreate PR/MR but don't mergefalse

Prerequisites

  • gh CLI authenticated (gh auth status) — for GitHub
  • glab CLI authenticated (glab auth status) — for GitLab
  • Repository hosted on GitHub or GitLab
  • CI enabled (GitHub Actions / GitLab CI) for verification

Workflow Phases

[Start]
   ↓
Phase 1: DETECT PLATFORM
   - Check git remote URL
   - Select gh or glab CLI
   ↓
Phase 2: DISCOVER
   - gh issue list / glab issue list
   - Read issue bodies, labels, acceptance criteria
   ↓
Phase 3: CATEGORIZE
   - Group by type: feature, bugfix, docs, ci, chore
   - Group by effort: trivial, small, medium, complex
   - Identify dependencies between issues
   - Flag already-implemented issues
   ↓
Phase 4: PLAN
   - Sort by dependency order (dependencies first)
   - Group compatible issues for single PR/MR
   - Estimate risk level per group
   - Create implementation checklist
   ↓
Phase 5: IMPLEMENT
   - Execute implementation groups in order
   - For each group: code → tests → docs
   - Track progress per issue
   ↓
Phase 6: VERIFY
   - Run quality gates
   - Push and create PR/MR
   - Monitor CI until green
   - Fix any failures
   ↓
Phase 7: REPORT
   - Summarize implemented issues
   - List any skipped/blocked issues
   - Report CI status

Categorization Rules

By Type

Label PatternCategoryImplementation Approach
feature, enhancementFeatureNew code + tests + docs
bug, bugfixBugfixFix + regression test
documentation, docsDocsREADME/md updates only
ci, chore, dependenciesInfrastructureConfig/script changes
securitySecurityAudit + fix + verify

By Effort

SignalEffortBatch Strategy
< 50 LOC changeTrivialBatch with any other issue
50-200 LOC changeSmallBatch with 2-3 similar issues
200-500 LOC changeMediumIndividual or pair
> 500 LOC changeComplexIndividual PR/MR consideration

Already-Implemented Detection

Skip issues where:

  • Source code already exists with the described functionality
  • Tests already cover the acceptance criteria
  • README already documents the feature

Dependency Detection

Look for:

  • Issue mentions another issue number (e.g., "depends on #123")
  • Acceptance criteria references files created by another issue
  • Feature requires infrastructure from another issue

Quality Gates

PhaseCheckFailure Action
PLANAll issues categorizedRe-scan with labels
IMPLEMENTEach change compilesFix before proceeding
VERIFY./scripts/quality-gates.shFix and retry
VERIFYAll CI checks passself-fix-loop skill
REPORTPR/MR created and greenDone

Integration with Other Skills

  • atomic-commit: Used in Phase 6 for PR/MR creation
  • self-fix-loop: Used in Phase 6 if CI fails
  • task-decomposition: Used in Phase 4 for complex issues
  • build-rust: Used in Phase 5 for compilation checks
  • lint-rust: Used in Phase 5 for quality checks
  • test-rust: Used in Phase 5 for test verification

Output Format

## Issue Triage Report

**Platform:** GitHub (gh) / GitLab (glab)

### Summary
- Total open: N
- To implement: M
- Already done: K
- Skipped (too complex): L

### Implementation Plan
| # | Issue | Type | Effort | Status |
|---|-------|------|--------|--------|
| 1 | #123 | feature | small | done |
| 2 | #124 | docs | trivial | done |

### Skipped Issues
- #125 (complex feature, needs design discussion)

### CI Status
- All checks: pass
- PR/MR: https://github.com/.../pull/XXX  or  https://gitlab.com/.../-/merge_requests/XXX

Example Usage

User: "read all open github issues and implement all missing"
Agent: Detects GitHub → uses gh → discover → categorize → plan → implement → verify → report

User: "implement all issues on gitlab"
Agent: Detects GitLab → uses glab → discover → categorize → plan → implement → verify → report

User: "implement issues #100-#110 in one MR"
Agent: Uses issue-triage with --limit 11 → filters to specific issues → batch implement

User: "triage open issues and tell me what's already done"
Agent: Uses issue-triage with --dry-run → discover → categorize → report (no implementation)

Rationalizations

MisconceptionReality
"Just implement issues one by one"Batching in a single PR/MR reduces CI overhead and ensures consistent implementation
"Skip already-implemented issues silently"Always report which issues were already done so the user knows
"Complex issues should wait"Flag them but include in the plan with appropriate warnings
"Platform doesn't matter"Commands differ between GitHub/GitLab; auto-detection prevents errors

Red Flags

  • Implementing issues without reading their full acceptance criteria
  • Creating separate PRs/MRs for each issue when user asked for batch
  • Skipping dependency analysis between issues
  • Not reporting which issues were already implemented
  • Ignoring CI failures after implementation
  • Using wrong CLI for the platform (gh on GitLab or vice versa)

Download now (ZIP)

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
What did you use it for? e.g. Reviewing pull requests in a Rails app

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.