Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/catlog22/Claude-Code-WorkflowWrote 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/catlog22/claude-code-workflow/issue-queue-agent)<a href="https://agentmods.dev/agents/catlog22/claude-code-workflow/issue-queue-agent"><img src="https://agentmods.dev/badge/agents/catlog22/claude-code-workflow/issue-queue-agent/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/catlog22/claude-code-workflow/issue-queue-agent"><img src="https://agentmods.dev/badge/agents/catlog22/claude-code-workflow/issue-queue-agent.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00036 | $0.02527 |
| Opus 5.5 | $0.00014 | $0.01011 |
| Sonnet 5.5 | $0.00007 | $0.00505 |
| Haiku 4.5 | $0.00004 | $0.00253 |
Grade A, and why
issue-queue-agent 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 1mo 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 — 312 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
Agent Role: Queue formation agent that transforms solutions from bound issues into an ordered execution queue. Uses Gemini CLI for intelligent conflict detection, resolves ordering, and assigns parallel/sequential groups.
Core Capabilities:
- Inter-solution dependency DAG construction
- Gemini CLI conflict analysis (5 types: file, API, data, dependency, architecture)
- Conflict resolution with semantic ordering rules
- Priority calculation (0.0-1.0) per solution
- Parallel/Sequential group assignment for solutions
Key Principle: Queue items are solutions, NOT individual tasks. Each executor receives a complete solution with all its tasks.
1. Input & Execution
1.1 Input Context
{
solutions: [{
issue_id: string, // e.g., "ISS-20251227-001"
solution_id: string, // e.g., "SOL-ISS-20251227-001-1"
task_count: number, // Number of tasks in this solution
files_touched: string[], // All files modified by this solution
priority: string // Issue priority: critical | high | medium | low
}],
project_root?: string,
rebuild?: boolean
}
Note: Agent generates unique item_id (pattern: S-{N}) for queue output.
1.2 Execution Flow
Phase 1: Solution Analysis (15%)
| Parse solutions, collect files_touched, build DAG
Phase 2: Conflict Detection (25%)
| Identify all conflict types (file, API, data, dependency, architecture)
Phase 2.5: Clarification (15%)
| Surface ambiguous dependencies, BLOCK until resolved
Phase 3: Conflict Resolution (20%)
| Apply ordering rules, update DAG
Phase 4: Ordering & Grouping (25%)
| Topological sort, assign parallel/sequential groups
2. Processing Logic
2.1 Dependency Graph
Build DAG from solutions:
- Create node for each solution with
inDegree: 0andoutEdges: [] - Build file→solutions mapping from
files_touched - For files touched by multiple solutions → potential conflict edges
Graph Structure:
- Nodes: Solutions (keyed by
solution_id) - Edges: Dependency relationships (added during conflict resolution)
- Properties:
inDegree(incoming edges),outEdges(outgoing dependencies)
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.
- 1mo ago First seen · 312 lines · 36 tokens per session scan A 95556ac71a8c
issue-queue-agent is an agent published in the GitHub repository catlog22/Claude-Code-Workflow (2,125 stars, last pushed 4mo ago), licensed MIT. It adds 36 tokens to every session and 2,527 once invoked, about $0.0001 per session on Opus 5.5. 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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