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Agent Memory
Command

/issue-reviews

Review open GitHub issues for priority, feasibility, project alignment, and risks.

From plugin
mulch
3224 skills4 commands

How it fires

How this command gets triggered: by you, by Claude, or both.

  • Fires itselfClaude auto-loads it when your prompt matches the work.
  • You can call itInvoke it directly when you want it.
  • Slash command/issue-reviews

Context preview

What this command does when you run it.

Review open GitHub issues for priority, feasibility, project alignment, and risks.

Command definition

issue-reviews.md
name: issue-reviews

intro

Review open GitHub issues for priority, feasibility, project alignment, and risks.

**Argument:** `$ARGUMENTS` — optional issue number(s) to review (e.g., `5` or `5 8 12`). If empty, review all open issues.

Steps

1. Discover issues to review

  • If `$ARGUMENTS` contains issue number(s), use those
  • Otherwise, run `gh issue list --state open --json number,title,author,labels,createdAt,updatedAt,comments` to get all open issues
  • If there are no open issues, say so and stop

2. Spawn a review team

Use the Task tool to spawn parallel agents (one per issue, or batch small sets if there are many). Each agent should:

a. Gather context

  • `gh issue view <number> --json title,body,author,labels,comments,createdAt,updatedAt`
  • Read any files referenced in the issue body or comments
  • Search the codebase for related code (`Grep`/`Glob` for keywords, function names, file paths mentioned)
  • Check if there are related open PRs: `gh pr list --state open --search "<issue-title-keywords>"`

b. Feasibility assessment

  • Is the issue well-defined enough to act on?
  • What files/subsystems would need to change?
  • Estimate scope: small (1-2 files), medium (3-5 files), large (6+ files / architectural)
  • Are there prerequisite changes or dependencies on other issues?
  • Are there technical blockers or unknowns?

c. Project alignment review

  • Does this issue align with mulch's goals (structured expertise management, CLI simplicity, Bun-native)?
  • Does it conflict with existing architecture decisions?
  • Is it a feature request, bug fix, improvement, or maintenance task?
  • Would addressing it create technical debt or reduce it?

d. Risk assessment

  • What could go wrong if this is implemented naively?
  • Are there breaking changes or migration concerns?
  • Does it touch critical infrastructure (config, expertise storage, schemas, JSONL)?
  • Could it introduce performance regressions?
  • Are there security implications?

e. Priority recommendation

  • **Critical** — Blocks users or breaks core functionality
  • **High** — Significant improvement, clear path to implement
  • **Medium** — Useful but not urgent, well-scoped
  • **Low** — Nice-to-have, unclear scope, or minimal impact
  • **Wontfix** — Doesn't align with project direction, or cost outweighs benefit

f. Produce a review summary

Each agent should return a structured review:

  • **Issue:** `#<number> — <title>` by `<author>`
  • **Type:** Bug / Feature / Improvement / Maintenance
  • **Recommended priority:** Critical / High / Medium / Low / Wontfix
  • **Scope:** Small / Medium / Large
  • **Summary:** 2-3 sentence assessment
  • **Alignment:** How well it fits mulch's direction
  • **Risks:** Potential pitfalls or concerns
  • **Suggestions:** Refinements to the issue, alternative approaches, or related work
  • **Related code:** Key files/subsystems that would be affected

3. Present consolidated report

After all agents complete, present a single consolidated report with:

  • A priority-sorted summary table of all reviewed issues
  • The detailed review for each issue
  • Cross-cutting themes (are multiple issues pointing to the same underlying problem?)
  • Recommended action plan: which issues to tackle first, which to defer, which to close
  • Any issues that should be split, merged, or rewritten for clarity
Read more
Ships withmulch

Structured expertise management for AI agent workflows. Agents start every session from zero. The pattern your agent discovered yesterday is forgotten today. Mulch fixes this: agents call ml record to write learnings, and ml query to read them.

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MIT
License
6d ago
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Repo: jayminwest/mulch