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/commands/catlog22/claude-code-workflow/discover)<a href="https://agentmods.dev/commands/catlog22/claude-code-workflow/discover"><img src="https://agentmods.dev/badge/commands/catlog22/claude-code-workflow/discover/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/commands/catlog22/claude-code-workflow/discover"><img src="https://agentmods.dev/badge/commands/catlog22/claude-code-workflow/discover.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 | $0.00049 | $0.04140 |
| Opus 5.5 | $0.00020 | $0.01656 |
| Sonnet 5.5 | $0.00010 | $0.00828 |
| Haiku 4.5 | $0.00005 | $0.00414 |
Grade A, and why
issue:discover 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 — 484 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auto Mode
When --yes or -y: Auto-select all perspectives, skip confirmations.
Issue Discovery Command
Quick Start
# Discover issues in specific module (interactive perspective selection)
/issue:discover src/auth/**
# Discover with specific perspectives
/issue:discover src/payment/** --perspectives=bug,security,test
# Discover with external research for all perspectives
/issue:discover src/api/** --external
# Discover in multiple modules
/issue:discover src/auth/**,src/payment/**
Discovery Scope: Specified modules/files only
Output Directory: .workflow/issues/discoveries/{discovery-id}/
Available Perspectives: bug, ux, test, quality, security, performance, maintainability, best-practices
Exa Integration: Auto-enabled for security and best-practices perspectives
CLI Tools: Gemini → Qwen → Codex (fallback chain)
What & Why
Core Concept
Multi-perspective issue discovery orchestrator that explores code from different angles to identify potential bugs, UX improvements, test gaps, and other actionable items. Unlike code review (which assesses existing code quality), discovery focuses on finding opportunities for improvement and potential problems.
vs Code Review:
- Code Review (
review-module-cycle): Evaluates code quality against standards - Issue Discovery (
issue:discover): Finds actionable issues, bugs, and improvement opportunities
Value Proposition
- Proactive Issue Detection: Find problems before they become bugs
- Multi-Perspective Analysis: Each perspective surfaces different types of issues
- External Benchmarking: Compare against industry best practices via Exa
- Direct Issue Integration: Discoveries can be exported to issue tracker
- Dashboard Management: View, filter, and export discoveries via CCW dashboard
How It Works
Execution Flow
Phase 1: Discovery & Initialization
└─ Parse target pattern, create session, initialize output structure
Phase 2: Interactive Perspective Selection
└─ AskUserQuestion for perspective selection (or use --perspectives)
Phase 3: Parallel Perspective Analysis
├─ Launch N @cli-explore-agent instances (one per perspective)
├─ Security & Best-Practices auto-trigger Exa research
├─ Agent writes perspective JSON, returns summary
└─ Update discovery-progress.json
Phase 4: Aggregation & Prioritization
├─ Collect agent return summaries
├─ Load perspective JSON files
├─ Merge findings, deduplicate by file+line
└─ Calculate priority scores
Phase 5: Issue Generation & Summary
├─ Convert high-priority discoveries to issue format
├─ Write to discovery-issues.jsonl
├─ Generate single summary.md from agent returns
└─ Update discovery-state.json to complete
Phase 6: User Action Prompt
└─ AskUserQuestion for next step (export/dashboard/skip)
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 · 484 lines · 49 tokens per session scan A 3f962d1b2e9d
issue:discover is a command published in the GitHub repository catlog22/Claude-Code-Workflow (2,124 stars, last pushed 4mo ago), licensed MIT. It adds 49 tokens to every session and 4,140 once invoked, about $0.0002 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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