Borrowing it
Nothing to install: this file belongs to tomdwipo/claude-soul. 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/tomdwipo/claude-soul/main/.claude/commands/production-to-jira.mdgit clone --depth 1 https://github.com/tomdwipo/claude-soulWrote 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/tomdwipo/claude-soul/production-to-jira)<a href="https://agentmods.dev/commands/tomdwipo/claude-soul/production-to-jira"><img src="https://agentmods.dev/badge/commands/tomdwipo/claude-soul/production-to-jira/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/tomdwipo/claude-soul/production-to-jira"><img src="https://agentmods.dev/badge/commands/tomdwipo/claude-soul/production-to-jira.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 | $0.00000 | $0.03593 |
| Opus 5 | $0.00000 | $0.01796 |
| Sonnet 5 | $0.00000 | $0.00719 |
| Haiku 4.5 | $0.00000 | $0.00359 |
Grade A, and why
production-to-jira 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 12d 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 — 275 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Production-to-Jira Command
Read Production Intelligence agent output, deduplicate findings against existing Jira tickets, and create actionable Task tickets in Backlog with full field mapping.
This is a focused subset of /quality-to-jira that ONLY processes the production
data sink, used by ~/w production run so every production scan (Tue + Fri) files
its own tickets — without depending on the Friday Quality Scan handoff.
Arguments: $ARGUMENTS
Prerequisites
-
Check that
/tmp/auto-workflow/production/production-summary.jsonexists. If it does NOT exist, stop and tell the user: "No production summary found. Run~/w production runfirst." -
Read the
generated_atfield. If older than 24 hours, WARN: "Production summary is from {generated_at} (>24h old). Run~/w production runfor fresh data?" Continue if running non-interactively.
Step 1: Read Production Summary
Read /tmp/auto-workflow/production/production-summary.json — JSON with a findings[]
array. Each finding has at least: title, severity, category, evidence, plus
category-specific fields (e.g., affected_users, event_count, version, screen).
Production categories surfaced by the AI agent:
- CRASH — Crashlytics-derived crash issues (FATAL or NON_FATAL)
- ANR — Application Not Responding patterns
- PERFORMANCE — App start latency, slow screen traces, slow network calls
- USER_FEEDBACK — Play Console review themes
- FUNNEL_DROP — Registration / KYC / transaction funnel drop-offs
- REMOTE_CONFIG — Stale or deprecated Remote Config flags still shipped
If the AI agent emitted other categories (e.g., INSTALL_CHURN, WAU_REGRESSION),
map them to the closest match above (likely FUNNEL_DROP or PERFORMANCE); if
truly unmappable, treat as USER_FEEDBACK so it still files.
Step 2: Group Findings Into Candidate Tickets
Each finding in production-summary.json is already grouped by the AI prompt
(one finding = one candidate ticket). Use these summary formats:
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.
- 12d ago First seen · 275 lines · 0 tokens per session scan A 0335e6847a7a
production-to-jira is a command published in the GitHub repository tomdwipo/claude-soul (24 stars, last pushed 4d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,593 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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