Like /gh-issue-use-aws-browser (correlated browser + read-only AWS full-stack dive), but the browser phase is pinned to the Playwright MCP. Example — /gh-issue-use-aws-playwright "file export > Download" clicking Download 403s on staging effort:max.
Same as /gh-issue, plus an AWS deep-dive — diagnose the reported symptom through the operator's live AWS CLI session using strictly read-only calls (logs, metrics, resource state, CloudTrail) before filing the groomed GitHub issue with cloud-traced evidence. Example — /gh-issue-use-aws "file export" presigned download…
Same as /gh-issue, plus a live-browser deep-dive using whatever browser MCP is connected (Chrome, Playwright, or Cypress): reproduce as the signed-in user, watch network + console, capture the failing request/response, mock to isolate, and record a repro before filing the GitHub issue. Example — /gh-issue-use-browser…
Like /gh-issue-use-browser, but pinned to the Chrome MCP (claude-in-chrome) — use when you want your real signed-in Chrome session for the repro. Example — /gh-issue-use-chrome "Composer > Save" saving toasts failure but the record persists.
Like /gh-issue-use-browser, but pinned to the Cypress MCP — use when your project runs the Cypress MCP for browser automation. Example — /gh-issue-use-cypress "Composer > Save" saving toasts failure but the record persists.
Like /gh-issue-use-browser, but pinned to the Playwright MCP — use for a clean automated browser (public page or test credentials, no real session needed). Example — /gh-issue-use-playwright "Composer > Save" saving toasts failure but the record persists.
File a groomed, dispatchable GitHub issue from a breadcrumb (component/page/object) plus a problem description — recon the code, verify evidence, dedupe, label, file. Optionally pin the dispatch model/effort. Example — /gh-issue "Settings > Billing > Invoice row" clicking an invoice opens a 404 model:opus effort:high.
Groom a not-yet-dev-ready Jira issue (pre-technical-recon) — drop one or more issue keys and dispatch an agent per issue to ask the engineering/project lead about intent, scope, and direction until ≥90% certain the written story reflects the ask, then fill the description to the six-section groomed anatomy via the…
Technical recon of groomed-but-unvetted Jira issue(s) — drop one or more issue keys and dispatch a read-only sub-agent per issue (in parallel) to trace the ask into the codebase, produce a verified implementation approach, assert a level-of-effort estimate with confidence, surface risks/dependencies, recommend a split…
The full-stack deep dive — /jira-issue plus BOTH the live-browser and read-only AWS diagnosis phases, correlated: reproduce the symptom as the signed-in user, capture the failing request, then chase that exact request through the cloud (edge, logs, service, data) until the fault layer is found. Use for symptoms nobody…
Like /jira-issue-use-aws-browser (correlated browser + read-only AWS full-stack dive), but the browser phase is pinned to the Chrome MCP (claude-in-chrome). Example — /jira-issue-use-aws-chrome "file export > Download" clicking Download 403s on staging effort:max.
Like /jira-issue-use-aws-browser (correlated browser + read-only AWS full-stack dive), but the browser phase is pinned to the Cypress MCP. Example — /jira-issue-use-aws-cypress "file export > Download" clicking Download 403s on staging effort:max.
Like /jira-issue-use-aws-browser (correlated browser + read-only AWS full-stack dive), but the browser phase is pinned to the Playwright MCP. Example — /jira-issue-use-aws-playwright "file export > Download" clicking Download 403s on staging effort:max.
Same as /jira-issue, plus an AWS deep-dive — diagnose the reported symptom through the operator's live AWS CLI session using strictly read-only calls (logs, metrics, resource state, CloudTrail) before filing the groomed Jira issue with cloud-traced evidence. Example — /jira-issue-use-aws "file export" presigned…
Same as /jira-issue, plus a live-browser deep-dive using whatever browser MCP is connected (Chrome, Playwright, or Cypress): reproduce as the signed-in user, watch network + console, capture the failing request/response, mock to isolate, and record a repro before filing the Jira issue. Example …
Like /jira-issue-use-browser, but pinned to the Chrome MCP (claude-in-chrome) — use when you want your real signed-in Chrome session for the repro. Example — /jira-issue-use-chrome "Composer > Save" saving toasts failure but the record persists.
Like /jira-issue-use-browser, but pinned to the Cypress MCP — use when your project runs the Cypress MCP for browser automation. Example — /jira-issue-use-cypress "Composer > Save" saving toasts failure but the record persists.
Like /jira-issue-use-browser, but pinned to the Playwright MCP — use for a clean automated browser (public page or test credentials, no real session needed). Example — /jira-issue-use-playwright "Composer > Save" saving toasts failure but the record persists.
File a groomed, dispatchable Jira issue from a breadcrumb (component/page/object) plus a problem description — recon the code, verify evidence, dedupe via JQL, set fields, file via the Atlassian MCP. Optionally pin the dispatch model/effort. Example — /jira-issue "Settings > Billing > Invoice row" clicking an invoice…
Emergency load-shedding when sub-agents overload the machine — OOM, runaway pre-push hooks, memory-compressor or swap pressure. Kills the newest agents first (least invested work) until pressure stabilizes, then salvages their worktrees. Example — /kill, /kill 3, /kill all.
Rescue a stuck PR or a failing environment — diagnose and fix failing CI/CD, PR checks, blocking review threads, and merge conflicts, then re-arm the merge path. Example — /rescue 1234, or /rescue dev.
Deep-dive the codebase and distil it into living, numerically-referenced domain specs — spawn survey agents to auto-discover the domains, fan out one agent per domain to infer conventions/standards/assumptions/invariants with verified file:line evidence, verify the claims, and write the spec set (specs/ - .spec.md +…
Refresh the living codebase specs after the code has moved — re-recon a spec reference, a domain, a path, or (no arg) the stalest specs; diff each against current code, update discovered behaviour and conventions in place, transition confidence with evidence, flag regressions as drift, append new references for…