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Issue Triage

CommunityPopular
FlorianBruniaux
issue-triage

3-phase issue backlog management with audit, deep analysis, and validated triage actions. Use when triaging GitHub issues, sorting bug reports, cleaning up stale tickets, or detecting duplicate issues. Args: 'all' to analyze all, issue numbers to focus (e.g. '42 57'), 'en'/'fr' for language, no arg = audit only.

Overview

PublisherFlorianBruniaux
Repositoryclaude-code-ultimate-guide
Skill nameissue-triage
Stars
6K
Forks
782
Bundled files
1
LicenseCC-BY-SA-4.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by FlorianBruniaux on GitHub. Read the source before you install it.

Installation

Install the Issue Triage AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/FlorianBruniaux/claude-code-ultimate-guide.git /tmp/claude-code-ultimate-guide
mkdir -p .claude/skills
cp -r /tmp/claude-code-ultimate-guide/examples/skills/issue-triage .claude/skills/issue-triage
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Issue Triage in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Issue Triage on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Issue Triage is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Issue Triage

3-phase workflow for maintainers: automated audit of all open issues, opt-in deep analysis via parallel agents, and validated triage actions (comments, labels, closures).

When to Use This Skill

SkillUsageOutput
/issue-triageSort, analyze, and act on an issue backlogTriage tables + analysis + executed actions
/pr-triageSort, review, and comment on a PR backlogTriage table + reviews + posted comments

Triggers:

  • Manually: /issue-triage or /issue-triage all or /issue-triage 42 57
  • Proactively: when >10 open issues without label, or stale issues >30 days detected

Language

  • Check the argument passed to the skill
  • If en or english → tables and summary in English
  • If fr, french, or no argument → French (default)
  • Note: GitHub comments and labels (Phase 3) are ALWAYS in English (international audience)

Configuration

Thresholds used throughout the workflow. Edit to match your project:

ParameterDefaultDescription
staleness_days30Days without activity before flagging as stale
very_stale_days90Days without activity before flagging as very stale
jaccard_threshold60%Minimum Jaccard similarity to flag two issues as duplicates
closed_compare_count20Number of recent closed issues to compare for duplicate detection
open_limit100Maximum open issues to fetch and analyze

Preconditions

bash
git rev-parse --is-inside-work-tree
gh auth status

If either fails, stop and explain what is missing.


Phase 1: Audit (always executed)

Data Gathering (parallel commands)

bash
# Repo identity
gh repo view --json nameWithOwner -q .nameWithOwner

# Open issues (exclude PRs, limit 100)
gh issue list --state open --limit 100 \
  --json number,title,author,createdAt,updatedAt,labels,body,comments,assignees,milestone

# Recent closed issues (for duplicate detection)
gh issue list --state closed --limit 20 \
  --json number,title,body,labels,stateReason

# Open PRs (bodies for cross-reference detection)
gh pr list --state open --limit 50 --json number,title,body

# Collaborators (to distinguish reporter types)
gh api "repos/{owner}/{repo}/collaborators" --jq '.[].login'

Collaborators fallback: if gh api .../collaborators returns 403/404:

bash
# Extract authors from last 10 merged PRs
gh pr list --state merged --limit 10 --json author --jq '.[].author.login' | sort -u

If still ambiguous, ask via AskUserQuestion.

Note: comments field in gh issue list --json comments returns the count, not content. For Phase 2, fetch full content separately: gh issue view {num} --json comments.

Analysis Dimensions

Run all 6 dimensions for each open issue:

1. Categorization

Classify each issue by reading title + first 200 chars of body:

CategoryLabelCriteria
BugbugDescribes broken behavior, unexpected output, crash
Feature RequestenhancementAsks for new functionality
Question / SupportquestionUser asking how something works
DocumentationdocumentationMissing or incorrect docs
Out of ScopewontfixClearly outside project boundaries
Unclearneeds-infoBody empty, too vague to categorize

If body is empty → category is always Unclear (never assume).

2. Cross-reference to PRs

Scan each open PR body for references to the issue number:

  • Patterns: fixes #N, closes #N, resolves #N, fix #N, close #N (case-insensitive, N = issue number)
  • Use regex locally on the body fields already fetched; do NOT make N additional API calls
  • If found: flag issue as "PR-linked" with PR number
3. Duplicate Detection via Jaccard Similarity

Algorithm (self-contained, no external library):

For each open issue, compute Jaccard similarity against all other open issues AND the 20 most recent closed issues.

Step 1: Normalize title + first 300 chars of body:
  - Lowercase the full text
  - Strip category prefixes: "feat:", "fix:", "bug:", "chore:", "docs:", "test:", "refactor:"
  - Remove punctuation: .,!?;:'"()[]{}-_/\@#

Step 2: Tokenize:
  - Split on whitespace
  - Remove stop words: the a an is in on to for of and or with this that it can not no be
  - Remove tokens shorter than 3 characters

Step 3: Compute Jaccard:
  tokens_A = set of tokens from issue A
  tokens_B = set of tokens from issue B
  jaccard = |tokens_A ∩ tokens_B| / |tokens_A ∪ tokens_B|

Step 4: Flag:
  - If jaccard >= 0.60: mark as potential duplicate
  - Report: "Similar to #N (Jaccard: 0.72)"
  - Keep the OLDER issue as canonical; newer = duplicate candidate

Jaccard is computed at runtime using the fetched data; no API calls beyond Phase 1 gather.

4. Risk Classification

Assign Red / Yellow / Green based on signals in title + body:

LevelColorCriteria
CriticalRedSecurity vulnerability, data loss, regression blocking users, crash in production
Needs AttentionYellowMissing validation, performance degradation, breaking change undocumented, Unclear with no response for >7 days
NormalGreenEverything else
5. Staleness
StatusCriterion
ActiveUpdated within 30 days
StaleNo activity 30–90 days
Very StaleNo activity >90 days

Use updatedAt field. Staleness does NOT depend on comments count; a commented-on issue with old updatedAt is still stale.

6. Recommendations

One recommended action per issue:

SituationAction
Category = Unclear, body emptyComment requesting details
Jaccard >= 0.60 with known issueClose as duplicate, link original
Very stale + no assigneeComment requesting status, suggest close
Risk = RedPin to top of triage, escalate immediately
Category = OOSClose with explanation
PR-linkedNo action needed (tracked via PR)
Normal + labeledNo action needed

Output: Triage Tables

## Open Issues ({count})

### Critical: Immediate Attention (Risk: Red)
| # | Title | Category | Reporter | Days Open | Action |
|---|-------|----------|----------|-----------|--------|

### PR-Linked (tracked in open PRs)
| # | Title | Category | PR | Days Open |
|---|-------|----------|----|-----------|

### Active Issues
| # | Title | Category | Labels | Reporter | Days | Action |
|---|-------|----------|--------|----------|------|--------|

### Duplicate Candidates
| # | Title | Similar To | Jaccard | Action |
|---|-------|------------|---------|--------|

### Stale Issues
| # | Title | Category | Last Activity | Reporter | Action |
|---|-------|----------|---------------|----------|--------|

### Summary
- Total open: {N}
- Critical (Red): {count}
- PR-linked: {count}
- Duplicate candidates: {count}
- Stale (30–90d): {count}
- Very stale (>90d): {count}
- Unlabeled: {count}
- Recommended actions: {comment: N, label: N, close: N}

0 issues → display No open issues. and stop.

Protection rules (apply to all phases):

  • Never close an issue authored by a collaborator without explicit user confirmation
  • Never re-label an issue that already has labels (only add missing labels)
  • If body is empty → always request details before any other action
  • Never auto-close a Red issue without user confirmation

Automatic Copy

After displaying the triage tables, copy to clipboard using platform-appropriate command:

bash
UNAME=$(uname -s)
if [ "$UNAME" = "Darwin" ]; then
  pbcopy <<'EOF'
{full triage tables}
EOF
elif command -v xclip &>/dev/null; then
  echo "{full triage tables}" | xclip -selection clipboard
elif command -v wl-copy &>/dev/null; then
  echo "{full triage tables}" | wl-copy
elif command -v clip.exe &>/dev/null; then
  echo "{full triage tables}" | clip.exe
fi

Confirm: Triage tables copied to clipboard. (EN) / Tableaux copiés dans le presse-papier. (FR)


Phase 2: Deep Analysis (opt-in)

Issue Selection

If argument passed:

  • "all" → all issues with recommended actions
  • Numbers ("42 57") → only those issues
  • No argument → propose via AskUserQuestion

If no argument, display:

question: "Which issues do you want to analyze in depth?"
header: "Deep Analysis"
multiSelect: true
options:
  - label: "All ({N} issues with recommended actions)"
    description: "Launch parallel analysis agents for each actionable issue"
  - label: "Critical only ({M} Red issues)"
    description: "Focus on high-risk issues requiring immediate action"
  - label: "Duplicate candidates ({K} issues)"
    description: "Verify Jaccard similarity with full body + comments"
  - label: "Stale only ({J} stale issues)"
    description: "Decide which stale issues to close vs. revive"
  - label: "Skip"
    description: "Stop here, audit only"

If "Skip" → end workflow.

Executing Analysis

For each selected issue, launch an analysis agent via Task tool in parallel:

subagent_type: general
model: sonnet
prompt: |
  Analyze GitHub issue #{num}: "{title}"

  **Metadata**: Category={category}, Risk={risk}, Days open={days}, Labels={labels}
  **Reporter**: @{author} ({collaborator? "collaborator" : "external"})
  **Assignees**: {assignees or "none"}

  **Body**:
  {body}

  **Comments** (fetch via: gh issue view {num} --json comments):
  {comments[].body, truncate at 5000 chars total}

  **Duplicate candidates**: {jaccard_results or "none found"}
  **Linked PRs**: {pr_refs or "none"}

  Tasks:
  1. Verify the category assigned in Phase 1 (correct? suggest alternative if not)
  2. If duplicate candidate: confirm or deny similarity with rationale
  3. If Unclear/needs-info: identify exactly what information is missing
  4. Suggest the most appropriate action with exact text if a comment is needed
  5. Estimate effort to fix if it's a Bug or Feature Request (XS/S/M/L/XL)

  Return structured output:
  ### Verification
  ### Duplicate Analysis
  ### Missing Information
  ### Recommended Action
  ### Effort Estimate

Fallback if parallel agents unavailable: run analysis sequentially, one issue at a time. Notify user: Running sequential analysis (parallel agents not available).

Fetch full comments via:

bash
gh issue view {num} --json comments --jq '.comments[].body'

Aggregate all reports. Display a summary after all analyses complete.


Phase 3: Actions (mandatory validation)

Draft Generation

For each analyzed issue, generate the appropriate action using the template templates/issue-comment.md.

3 action types:

TypeCommandWhen
Commentgh issue comment {num} --body-file -Needs info, stale ping, OOS explanation
Labelgh issue edit {num} --add-label "{label}"Unlabeled issue with clear category
Closegh issue close {num} --reason "not planned"Duplicate, OOS, very stale

Rules:

  • Language for comments: English (international audience)
  • Labels added: use existing repo labels only (fetch with gh label list)
  • Close reason: "not planned" for OOS/duplicate, "completed" only if a fix was merged
  • Never post a comment AND close in the same action without user seeing both drafts
  • Always attach a comment when closing (explain why)

Display and Validation

Display ALL drafted actions in format:

---
### Draft: Issue #{num}: {title}

**Action**: {Comment / Label / Close + Comment}
**Reason**: {1 sentence}

{full comment text if applicable}

---

Then request validation via AskUserQuestion:

question: "These actions are ready. Which ones do you want to execute?"
header: "Execute Triage Actions"
multiSelect: true
options:
  - label: "All ({N} actions)"
    description: "Execute all drafted triage actions"
  - label: "Issue #{x}: {title_truncated} ({action_type})"
    description: "Execute only this action"
  - label: "None"
    description: "Cancel, execute nothing"

(Generate one option per issue + "All" + "None")

Execution

For each validated action:

bash
# Comment
gh issue comment {num} --body-file - <<'TRIAGE_EOF'
{comment}
TRIAGE_EOF

# Label
gh issue edit {num} --add-label "{label}"

# Close with comment
gh issue comment {num} --body-file - <<'TRIAGE_EOF'
{close comment}
TRIAGE_EOF
gh issue close {num} --reason "not planned"

Confirm each action: Action executed on issue #{num}: {title}

If "None" → No actions executed. Workflow complete.


Edge Cases

SituationBehavior
0 open issuesDisplay No open issues. + stop
Body emptyCategory = Unclear, action = request details, never assume
Collaborator as reporterProtect from auto-close, flag explicitly in table
Jaccard inconclusive (0.55–0.65)Flag as "possible duplicate, verify manually"
Label not in repoSkip label action, notify user to create the label first
Issue already closed during workflowSkip silently, note in summary
gh api .../collaborators 403/404Fallback to last 10 merged PR authors
Parallel agents unavailableRun sequential analysis, notify user
Very large body (>5000 chars)Truncate to 5000 chars with [truncated] note
Milestone assignedInclude in table, never close milestoned issues without confirmation

Notes

  • Always derive owner/repo via gh repo view, never hardcode
  • Use gh CLI (not curl GitHub API) except for collaborators list
  • comments in gh issue list --json comments = count only; full content requires gh issue view {num} --json comments
  • Never execute any action without explicit user validation in chat
  • Drafted actions must be visible BEFORE any gh issue comment or gh issue close
  • Jaccard is computed locally: no external API, no library, pure set operations on fetched data
  • Signature on all comments: *Triaged via Claude Code /issue-triage*

Related: /pr-triage

/issue-triage/pr-triage
ScopeIssue backlogPR backlog
Use whenCatching up on reporter feedback, periodic issue cleanupCatching up after PR accumulation
Phases3 (audit + deep analysis + actions)3 (audit + deep review + comments)
AgentsParallel sub-agents per issueParallel sub-agents per PR
Duplicate detectionJaccard similarity on title+bodyFile overlap % between PRs
ActionsComment / label / closeGitHub review comment
ValidationAskUserQuestion before executingAskUserQuestion before posting

Decision rule: use /issue-triage for issue backlog management, /pr-triage for code review backlog.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Issue Triage AI skill do?

3-phase issue backlog management with audit, deep analysis, and validated triage actions. Use when triaging GitHub issues, sorting bug reports, cleaning up stale tickets, or detecting duplicate issues. Args: 'all' to analyze all, issue numbers to focus (e.g. '42 57'), 'en'/'fr' for language, no arg = audit only.

Why use Issue Triage on TypingMind?

Because you install it once and use it with any model. Issue Triage is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Issue Triage in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/FlorianBruniaux/claude-code-ultimate-guide/tree/main/examples/skills/issue-triage. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Issue Triage?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Issue Triage?

As many as you like. As long as a model supports skills, you can use Issue Triage with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Issue Triage AI skill free?

Yes. It is published on GitHub by FlorianBruniaux under the CC-BY-SA-4.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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