Agent skill

Issue Prioritizer

by LeoYeAI in LeoYeAI/openclaw-master-skills

Prioritize GitHub issues by ROI, solution sanity, and architectural impact.

MITAuto-check passedSales & Support

Install Issue Prioritizer

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill issue-prioritizer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills issue-prioritizer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/issue-prioritizer .claude/skills/issue-prioritizer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
issue-prioritizer
GitHub stars
2.2k
Token cost
~3.9k tokens
SKILL.md length
1,142 words
Files
6
Skills in repo
1,235
Repo updated
First seen
Licence
MIT

At a glance

Prioritize GitHub issues by ROI, solution sanity, and architectural impact.

  • Works in 6 steps: Get Repository → Fetch Issues → Filter Issues with Existing PRs → …
  • Ranking issues to identify quick wins
  • SKILL.md covers When to use, When NOT to use, Requirements and Instructions, plus 2 more sections
  • Calls gh; reaches github.com

What it does

Issue Prioritizer is an agent skill from LeoYeAI/openclaw-master-skills. Prioritize GitHub issues by ROI, solution sanity, and architectural impact. Use when triaging or ranking issues to identify quick wins, over-engineered proposals, and actionable bugs. Don't use when managing forks (use fork-manager) or general GitHub queries (use github). Read-only — never modifies repositories.

Its SKILL.md is about 3.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files (for example `README.md`, `_meta.json` and `commands/CLAUDE.md`).

It sits in Sales & Support, covering Proposals and quotes. It works with GitHub and Telegram. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.

When your agent uses it

  • Ranking issues to identify quick wins
  • Over-engineered proposals
  • Actionable bugs
  • Managing forks (use fork-manager)

Example prompts

  • “/issue-prioritizer”

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Get Repository
  2. Fetch Issues
  3. Filter Issues with Existing PRs
  4. Analyze Each Issue
  5. Categorize
  6. Present Results

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • gh

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Issue Prioritizer loads about 3.9k tokens when it runs. Until then it costs about 83 tokens; SKILL.md has 1,142 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~83
When it runs · the whole SKILL.md, loaded when a task matches
~3.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 1,142 words, ~3,944 tokens.

Download SKILL.mdSave it as .claude/skills/issue-prioritizer/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
issue-prioritizer
description
Prioritize GitHub issues by ROI, solution sanity, and architectural impact. Use when triaging or ranking issues to identify quick wins, over-engineered proposals, and actionable bugs. Don't use when managing forks (use fork-manager) or general GitHub queries (use github). Read-only — never modifies repositories.

Issue Prioritizer

Analyze issues from a GitHub repository and rank them by Adjusted Score — ROI penalized by Tripping Scale (solution sanity), Architectural Impact, and Actionability.

This is a read-only skill. It analyzes and presents information. The user makes all decisions.

When to use

  • Triaging or ranking issues in a repository
  • Identifying quick wins for contributors
  • Filtering out non-actionable items (questions, duplicates)
  • Detecting over-engineered proposals
  • Matching issues to contributor skill levels

When NOT to use

  • Managing forks or syncing with upstream → use fork-manager instead
  • General GitHub CLI queries (PR status, CI runs) → use github instead
  • Reviewing code changes before publishing → use pr-review instead

Requirements

  • gh CLI authenticated (gh auth login)

Instructions

Step 1: Get Repository

If the user didn't specify a repository, ask which one to analyze (format: owner/repo).

Step 2: Fetch Issues

Basic fetch (most recent):

bash
gh issue list --repo {owner/repo} --state open --limit {limit} --json number,title,body,labels,createdAt,comments,url

Default limit is 30. Store the full JSON response.

Targeted fetch with --topic:

When the user specifies --topic <keyword> (e.g. --topic telegram, --topic agents), use GitHub search to find issues matching that topic instead of just fetching the most recent:

bash
# Search by topic keywords in title and body
gh issue list --repo {owner/repo} --state open --limit {limit} --search "{topic} in:title,body" --json number,title,body,labels,createdAt,comments,url

Multiple topics can be combined: --topic "telegram agents" searches for issues containing either term.

Targeted fetch with --search:

When the user specifies --search <query>, pass it directly as a GitHub search query for full control:

bash
gh issue list --repo {owner/repo} --state open --limit {limit} --search "{query}" --json number,title,body,labels,createdAt,comments,url

Examples:

  • --search "telegram in:title" — only title matches
  • --search "label:bug telegram" — bugs mentioning telegram
  • --search "label:bug,enhancement telegram agents" — bugs or enhancements about telegram/agents
  • --search "comments:>5 telegram" — active discussions about telegram

Label-based fetch with --label:

bash
gh issue list --repo {owner/repo} --state open --limit {limit} --label "{label}" --json number,title,body,labels,createdAt,comments,url

All fetch modes can be combined: --topic telegram --label bug --limit 50 fetches up to 50 open bugs about telegram.

Error handling:

  • Auth error → tell user to run gh auth login
  • Rate limited → inform user, suggest reducing --limit
  • Repo not found → check format owner/repo
  • No issues → report and exit (if using --topic/--search, suggest broadening the query)
  • Missing fields → treat null/missing body and labels as empty
Step 3: Filter Issues with Existing PRs

Note: If user specified --include-with-prs, skip this entire step and proceed to Step 4 with all fetched issues.

Before analyzing, check for open PRs that already address issues to avoid duplicate work.

bash
gh pr list --repo {owner/repo} --state open --json number,title,body,url

Detect linked issues using ALL of these methods:

Method 1 — Explicit Keywords (high confidence): Scan PR title and body (case-insensitive):

  • fixes #N, fix #N, fixed #N
  • closes #N, close #N, closed #N
  • resolves #N, resolve #N, resolved #N

Method 2 — Issue References (medium confidence):

  • #N anywhere in text
  • issue N, issue #N, related to #N, addresses #N

Method 3 — Title Similarity (fuzzy): Normalize titles (lowercase, remove punctuation/common words). If 70%+ word overlap → likely linked.

Method 4 — Semantic Matching (ambiguous cases): Extract key terms from issue (error names, function names, components). Check if PR body discusses same things.

Confidence icons:

  • 🔗 Explicit link (fixes/closes/resolves)
  • 📎 Referenced (#N mentioned)
  • 🔍 Similar title (fuzzy match)
  • 💡 Semantic match (same components)

Remove linked issues from analysis. Report them separately before the main report.

If all issues have PRs, report that and exit.

Step 4: Analyze Each Issue

For each remaining issue, score the following:

Difficulty (1-10)

Base score: 5. Adjustments:

SignalAdjustment
Documentation only-3
Has proposed solution-2
Has reproduction steps-1
Clear error message-1
Unknown root cause+3
Architectural change+3
Race condition/concurrency+2
Security implications+2
Multiple systems involved+2
Importance (1-10)
RangeLevelExamples
8-10CriticalCrash, data loss, security vulnerability, service down
6-7HighBroken functionality, errors, performance issues
4-5MediumEnhancements, feature requests, improvements
1-3LowCosmetic, documentation, typos
Tripping Scale (1-5) — Solution Sanity (How "Out There" Is It?)
ScoreLabelDescription
1Total SanityProven approach, standard patterns
2Grounded w/FlairPractical with creative touches
3Dipping ToesExploring cautiously
4Wild AdventureBold, risky, unconventional
5TrippingQuestionable viability

Red Flags (+score): rewrite from scratch, buzzwords (blockchain, AI-powered, ML-based), experimental/unstable, breaking change, custom protocol Green Flags (-score): standard approach, minimal change, backward compatible, existing library, well-documented

Show full SKILL.md (507 more words)Show less
Architectural Impact (1-5)

Always ask: "Is there a simpler way?" before scoring.

ScoreLabelDescription
1SurgicalIsolated fix, 1-2 files, no new abstractions
2LocalizedSmall addition, follows existing patterns exactly
3ModerateNew component within existing architecture
4SignificantNew subsystem, new patterns, affects multiple modules
5TransformationalRestructures core, changes paradigms, migration needed

Red Flags (+score): "rewrite", "refactor entire", new framework for existing capability, changes across >5 files, breaking API changes, scope creep Green Flags (-score): single file fix, uses existing utilities, follows established patterns, backward compatible, easily revertible

Critical: If a simple solution exists, architectural changes are wrong. Don't create a "validation framework" when a single if-check suffices.

Actionability (1-5) — Can it be resolved with a PR?
ScoreLabelDescription
1Not ActionableQuestion, discussion, duplicate, support request
2Needs TriageMissing info, unclear scope, needs clarification
3Needs InvestigationRoot cause unknown, requires debugging first
4Ready to WorkClear scope, may need some design decisions
5PR ReadySolution is clear, just needs implementation

Blockers (-score): questions ("how do I?"), discussions ("thoughts?"), labels (duplicate, wontfix, question), missing repro Ready signals (+score): action titles ("fix:", "add:"), proposed solution, repro steps, good-first-issue label, specific files mentioned

Derived Values
issueType: "bug" | "feature" | "docs" | "other"
suggestedLevel:
  - "beginner": difficulty 1-3, no security/architecture changes
  - "intermediate": difficulty 4-6
  - "advanced": difficulty 7+ OR security implications OR architectural changes
Calculation Formulas
ROI = Importance / Difficulty
AdjustedScore = ROI × TripMultiplier × ArchMultiplier × ActionMultiplier

Tripping Scale Multiplier:

ScoreLabelMultiplier
1Total Sanity1.00 (no penalty)
2Grounded w/Flair0.85
3Dipping Toes0.70
4Wild Adventure0.55
5Tripping0.40

Architectural Impact Multiplier:

ScoreLabelMultiplier
1Surgical1.00 (no penalty)
2Localized0.90
3Moderate0.75
4Significant0.50
5Transformational0.25

Actionability Multiplier:

ScoreLabelMultiplier
5PR Ready1.00 (no penalty)
4Ready to Work0.90
3Needs Investigation0.70
2Needs Triage0.40
1Not Actionable0.10
Step 5: Categorize
  • Quick Wins: ROI ≥ 1.5 AND Difficulty ≤ 5 AND Trip ≤ 3 AND Arch ≤ 2 AND Actionability ≥ 4
  • Critical Bugs: issueType = "bug" AND Importance ≥ 8
  • Tripping Issues: Trip ≥ 4
  • Over-Engineered: Arch ≥ 4 (simpler solution likely exists)
  • Not Actionable: Actionability ≤ 2

Sort all issues by AdjustedScore descending.

Step 6: Present Results
═══════════════════════════════════════════════════════════════
  ISSUE PRIORITIZATION REPORT
  Repository: {owner/repo}
  Filter: {topic/search/label or "latest"}
  Analyzed: {count} issues
  Excluded: {excluded} issues with existing PRs
═══════════════════════════════════════════════════════════════

  Quick Wins: {n} | Critical Bugs: {n} | Tripping: {n} | Over-Engineered: {n} | Not Actionable: {n}

═══════════════════════════════════════════════════════════════
  TOP 10 BY ADJUSTED SCORE
═══════════════════════════════════════════════════════════════

  #123 [Adj: 3.50] ⭐ Quick Win
  Fix typo in README
  ├─ Difficulty: 1/10 | Importance: 4/10 | ROI: 4.00
  ├─ Trip: ✅ Total Sanity (1/5) | Arch: ✅ Surgical (1/5)
  ├─ Act: ✅ PR Ready (5/5) | Level: beginner
  └─ https://github.com/owner/repo/issues/123

═══════════════════════════════════════════════════════════════
  QUICK WINS (High Impact, Low Effort, Sane & Actionable)
═══════════════════════════════════════════════════════════════

  #123: Fix typo in README [Adj: 3.50]
        Difficulty: 1 | Importance: 4 | beginner

═══════════════════════════════════════════════════════════════
  RECOMMENDATIONS BY LEVEL
═══════════════════════════════════════════════════════════════

  BEGINNER (Difficulty 1-3, no security/architecture):
  - #123: Fix typo - Low risk, good first contribution

  INTERMEDIATE (Difficulty 4-6):
  - #456: Add validation - Medium complexity, clear scope

  ADVANCED (Difficulty 7-10 or security/architecture):
  - #789: Refactor auth - Architectural knowledge needed

═══════════════════════════════════════════════════════════════
  CRITICAL BUGS (Importance ≥ 8)
═══════════════════════════════════════════════════════════════

  #111 [Adj: 1.67] 🔴 Critical
  App crashes on startup with large datasets
  ├─ Difficulty: 6/10 | Importance: 9/10 | ROI: 1.50
  ├─ Trip: ✅ (2/5) | Arch: ✅ (2/5) | Act: ⚠️ (3/5)
  └─ https://github.com/owner/repo/issues/111

═══════════════════════════════════════════════════════════════
  TRIPPING ISSUES (Trip ≥ 4 — Review Carefully)
═══════════════════════════════════════════════════════════════

  #999 [Trip: 🚨 5/5 — Tripping]
  Rewrite entire backend in Rust with blockchain storage
  ├─ Red Flags: "rewrite from scratch", "blockchain"
  ├─ Adjusted Score: 0.12 (heavily penalized)
  └─ Consider: Is this complexity really needed?

═══════════════════════════════════════════════════════════════
  OVER-ENGINEERED (Arch ≥ 4 — Simpler Solution Likely Exists)
═══════════════════════════════════════════════════════════════

  #777 [Arch: 🏗️ 5/5 — Transformational]
  Add form validation
  ├─ Proposed: New validation framework with schema definitions
  ├─ Simpler Alternative: Single validation function, 20 lines
  └─ Ask: Why create a framework for one form?

  💡 TIP: Maintainers often reject PRs that change architecture
     unnecessarily. Always start with the simplest fix.

═══════════════════════════════════════════════════════════════
  NOT ACTIONABLE (Actionability ≤ 2)
═══════════════════════════════════════════════════════════════

  - #222: "How do I deploy to Kubernetes?" (Act: 1/5 — question)
  - #333: Duplicate of #111 (Act: 1/5 — duplicate)

═══════════════════════════════════════════════════════════════
  EXCLUDED — EXISTING PRs
═══════════════════════════════════════════════════════════════

  #123: Login crashes on empty password
        └─ 🔗 PR #456: "Fix login validation" (explicit: fixes #123)

  Detection: 🔗 Explicit link | 📎 Referenced | 🔍 Similar title | 💡 Semantic match

═══════════════════════════════════════════════════════════════
  SCALE LEGEND
═══════════════════════════════════════════════════════════════

  Trip (Solution Sanity):        Arch (Structural Impact):
  ✅ 1-2 = Sane                  ✅ 1-2 = Minimal change
  ⚠️  3  = Cautious              ⚠️  3  = Moderate
  🚨 4-5 = Risky                 🏗️ 4-5 = Over-engineered

  Actionability:
  ✅ 4-5 = Ready for PR
  ⚠️  3  = Needs Investigation
  ❌ 1-2 = Not Actionable

  AdjustedScore = ROI × TripMult × ArchMult × ActionMult
  Higher = Better (prioritize first)

  🎯 SIMPLICITY PRINCIPLE: If a 10-line fix exists,
     a 200-line refactor is wrong.

  Mode: SKILL (read-only) — analyzes only, never modifies.
═══════════════════════════════════════════════════════════════

Options

  • --json: Raw JSON output
  • --markdown / --md: Markdown table output
  • --quick-wins: Show only quick wins
  • --level beginner|intermediate|advanced: Filter by contributor level
  • --limit N: Number of issues to analyze (default: 30)
  • --topic <keywords>: Search issues by topic (e.g. --topic telegram, --topic "agents telegram")
  • --search <query>: Raw GitHub search query for full control (e.g. --search "label:bug telegram in:title")
  • --label <name>: Filter by GitHub label (e.g. --label bug)
  • --include-with-prs: Skip PR filtering, include all issues

LLM Deep Analysis (Optional)

For higher-quality scoring, use an LLM to analyze each issue individually. For each issue, prompt the model with the issue details and scoring criteria, requesting structured JSON output:

json
{
  "number": 123,
  "difficulty": 5,
  "difficultyReasoning": "base 5; has repro (-1); unknown cause (+3) = 7",
  "importance": 7,
  "importanceReasoning": "broken functionality affecting users",
  "tripScore": 2,
  "tripLabel": "Grounded with Flair",
  "tripRedFlags": [],
  "tripGreenFlags": ["minimal change", "standard approach"],
  "archScore": 2,
  "archLabel": "Localized",
  "archRedFlags": [],
  "archGreenFlags": ["uses existing patterns"],
  "archSimplerAlternative": null,
  "actionScore": 4,
  "actionLabel": "Ready to Work",
  "actionBlockers": [],
  "actionReadySignals": ["has proposed solution"],
  "issueType": "bug",
  "suggestedLevel": "intermediate",
  "roi": 1.40,
  "adjustedScore": 0.96
}

Truncate issue bodies longer than 2000 characters before sending to the model.

When to use LLM Deep Analysis:

  • Complex repositories with nuanced issues
  • When accuracy matters more than speed
  • For repositories you're unfamiliar with

Tradeoffs: Slower (~2-5s per issue) but more accurate. 1 API call per issue.

Integration: For each issue, call the LLM with the analysis prompt, parse the JSON response, and merge into results before Step 5 (Categorize).

© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files in skills/issue-prioritizer of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • README.md
  • _meta.json
  • commands/CLAUDE.md
  • commands/issue-prioritizer.md
  • tests/trigger-prompts.csv

Open the folder on GitHubat commit e5199b5

Compare with similar skills

Issue Prioritizer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Issue Prioritizer compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Issue Prioritizer this skillLeoYeAI/openclaw-master-skills2.2k—~3.9kAutomated safety check: PassMIT
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Review Proposalsjpicklyk/task-orchestrator207—~4.5kAutomated safety check: PassMIT
Supporthaacked/dotfiles134—~2.9kAutomated safety check: PassNone
B2b Sdr AgentiPythoning/b2b-sdr-agent-template190—~744Automated safety check: PassMIT-0
Exploration Modefjrevoredo/mini-diarium309—~3.4kAutomated safety check: PassMIT

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Works with

Categories

Questions about Issue Prioritizer

What does Issue Prioritizer do?

Prioritize GitHub issues by ROI, solution sanity, and architectural impact. Issue Prioritizer is an agent skill from LeoYeAI/openclaw-master-skills. Prioritize GitHub issues by ROI, solution sanity, and architectural impact.

When should I use Issue Prioritizer?

Issue Prioritizer fits situations like: ranking issues to identify quick wins; over-engineered proposals; actionable bugs; managing forks (use fork-manager).

How do I install Issue Prioritizer in Claude Code?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill issue-prioritizer -a claude-code`. Or copy the skill folder (skills/issue-prioritizer in LeoYeAI/openclaw-master-skills) into .claude/skills/issue-prioritizer in your project. Claude Code loads it when a task matches its description.

How do I install Issue Prioritizer in Codex?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill issue-prioritizer -a codex`. Or copy the skill folder (skills/issue-prioritizer in LeoYeAI/openclaw-master-skills) into .agents/skills/issue-prioritizer in your project. Codex loads it when a task matches its description.

Can I use Issue Prioritizer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill issue-prioritizer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/issue-prioritizer, .gemini/skills/issue-prioritizer, .github/skills/issue-prioritizer and .opencode/skills/issue-prioritizer in your project.

What does Issue Prioritizer need to run?

Going by SKILL.md and its folder, Issue Prioritizer needs the command-line tools its instructions call (gh).

Does Issue Prioritizer access the network?

SKILL.md names 1 domain. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Issue Prioritizer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Issue Prioritizer use?

Issue Prioritizer is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Issue Prioritizer use?

About 3.9k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Issue Prioritizer?

Skills that share tags, products or a category with Issue Prioritizer: Lead-to-Payment Sales Flow (hewi333/Mom-n-Pop-Skills, 122 stars), Review Proposals (jpicklyk/task-orchestrator, 207 stars), Support (haacked/dotfiles, 134 stars) and B2b Sdr Agent (iPythoning/b2b-sdr-agent-template, 190 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Issue Prioritizer?

LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,161 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.