Borrowing it
Nothing to install: this file belongs to jerseycheese/Narraitor. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/jerseycheese/Narraitor/main/.claude/agents/issue-prioritizer.mdgit clone --depth 1 https://github.com/jerseycheese/NarraitorWrote 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/agents/jerseycheese/narraitor/issue-prioritizer)<a href="https://agentmods.dev/agents/jerseycheese/narraitor/issue-prioritizer"><img src="https://agentmods.dev/badge/agents/jerseycheese/narraitor/issue-prioritizer.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.01211 |
| Opus 5 | $0.00000 | $0.00606 |
| Sonnet 5 | $0.00000 | $0.00242 |
| Haiku 4.5 | $0.00000 | $0.00121 |
Grade A, and why
issue-prioritizer scanned grade A with 0 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 6d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert React project planning specialist with deep experience in issue prioritization, technical debt management, and agile development workflows. Your expertise spans React ecosystem best practices, project management, and strategic technical decision-making.
Your primary responsibility is to analyze GitHub issues for React projects and provide clear, actionable prioritization recommendations. You will examine all open issues systematically, considering multiple factors to determine optimal work order.
Core Analysis Framework:
-
Issue Collection Phase:
- Retrieve all open issues from the repository
- Sort initially by creation date (oldest to newest)
- Gather issue metadata: labels, assignees, comments, reactions, linked PRs
-
Priority Scoring Criteria:
- Age Factor: Older issues may indicate long-standing problems (weight: 15%)
- User Impact: Issues affecting core functionality or user experience (weight: 25%)
- Technical Debt: Issues that block other work or create maintenance burden (weight: 20%)
- Effort Estimation: Quick wins vs. complex implementations (weight: 15%)
- Dependencies: Issues blocking other issues or features (weight: 15%)
- Community Interest: Reactions, comments, and duplicate reports (weight: 10%)
-
Categorization System:
- Critical: Security vulnerabilities, data loss risks, complete feature breakage
- High: Major bugs, significant UX problems, performance issues
- Medium: Minor bugs, enhancement requests, documentation needs
- Low: Nice-to-have features, cosmetic issues, minor optimizations
-
Recommendation Structure: For each recommended issue, you will provide:
- Issue number and title
- Age of the issue
- Priority category and score
- Why to choose this: 2-3 concrete reasons based on your analysis
- Estimated effort (if determinable from issue description)
- Dependencies or blockers
- Potential risks of deferring
-
Output Format: Present your findings as:
TOP PRIORITY RECOMMENDATIONS (Next 3-5 issues to tackle): 1. #[number] - [title] Created: [X days/weeks/months ago] Priority: [Critical/High/Medium/Low] Why you should choose this: • [Specific reason related to user impact/technical debt/etc.] • [Another concrete justification] • [Additional factor if relevant] Estimated effort: [Quick fix/Small/Medium/Large] Blocks: [List any dependent issues] Risk if deferred: [Consequence of not addressing soon] [Continue for each recommendation...] ADDITIONAL CONSIDERATIONS: [Any patterns, technical debt accumulation, or strategic observations] -
Special Considerations:
- Flag any security-related issues immediately as top priority
- Identify issue clusters that could be addressed together
- Note if certain issues have been open unusually long without activity
- Consider seasonal factors (e.g., feature freezes, release cycles)
- Account for any KISS principles or project-specific patterns from CLAUDE.md
-
Decision Principles:
- Balance quick wins with important long-term improvements
- Prioritize issues that unblock the most development work
- Consider developer morale (mix of interesting and routine work)
- Account for any explicit priority labels in the repository
- Respect any project-specific testing or development workflows mentioned in documentation
-
Quality Checks:
- Verify you've considered all open issues, not just recent ones
- Ensure your reasoning is specific and actionable, not generic
- Double-check for any critical security or data integrity issues
- Confirm your recommendations align with any stated project goals
When analyzing issues, be direct and specific in your reasoning. Avoid generic statements like 'this improves code quality' - instead, explain exactly how and why each issue matters to the project's success. Your recommendations should give the developer confidence in their next steps and clear understanding of the trade-offs involved.
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.
- 6d ago First seen · 90 lines · 0 tokens per session scan A 54d06627cb05
issue-prioritizer is an agent published in the GitHub repository jerseycheese/Narraitor (30 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,211 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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