ai-workflows

Diagnostic and repair workflow that analyzes error logs, traces root causes, implements fixes, and verifies with regression tests. Use when fixing bugs, debugging runtime errors or exceptions, investigating test failures or crashes, or submitting bug-fix pull requests. Activated by commands: /unattended, /assess, /diagnose, /reproduce, /fix, /test, /review, /document, /pr, /feedback, /start.

Bulk-triage unresolved Jira bugs with AI-driven recommendations and an interactive HTML report. Scan also loads recently resolved bugs for regression matching in analyze. Use when triaging a project backlog, prioritizing bug fixes, identifying candidates for automated fixing, or reviewing stale issues. For one bug in depth (no artifacts), use /assess. Activated by commands: /run, /start, /scan, /analyze, /report, and /assess.

Design-and-decompose workflow that takes a PRD, researches the problem space, drafts a technical design document, decomposes work into Jira-ready epics and stories, and manages review via GitHub PRs. Use when creating design documents, researching external integrations or standards, breaking features into epics/stories, or syncing task breakdowns to Jira. Activated by commands: /ingest, /research, /draft, /decompose, /revise, /publish, /respond, /sync.

Requirements-to-PRD workflow that ingests requirements from Jira, clarifies ambiguities through iterative Q&A, drafts a Product Requirements Document, and manages review via GitHub PRs. Use when creating PRDs, analyzing requirements, or preparing feature specifications for review. Activated by commands: /ingest, /clarify, /draft, /revise, /publish, /respond.

AI-driven code review workflow that reviews uncommitted changes using a discoverable reviewer profile, presents findings for human decision, and iterates until approved. Supports --unattended for automated iteration. Use when reviewing code before commit or PR. Activated by commands: /start, /continue, /clean.

Automated CVE remediation that reads vulnerability details from Jira vulnerability tickets, applies multi-strategy dependency fixes, validates results, and creates pull requests with full justification. Language-agnostic: supports Go, Node.js, Python, Java, Rust, Ruby. Use when patching CVEs, updating vulnerable dependencies, or responding to Jira vulnerability tickets. Activated by commands: /start, /scan, /patch, /validate, /pr, /backport, /close, /report.

Story-to-e2e-test workflow that takes a Jira [QE] Story, discovers the project's e2e testing infrastructure, plans test scenarios, writes e2e tests matching project conventions, validates them, and manages review via GitHub PRs. Use when implementing [QE] stories produced by the design workflow. Activated by commands: /ingest, /plan, /revise, /code, /validate, /publish, /respond.

Story-to-code workflow that takes a Jira Story, plans the implementation, writes contract-based tests and production code via TDD, validates against the project's CI expectations, and manages review via GitHub PRs. Use when implementing Jira stories produced by the design workflow. Activated by commands: /ingest, /plan, /revise, /code, /validate, /publish, /respond.

Scans a codebase structure, audits AI convention files, and creates or updates AGENTS.md with project-specific build commands, test patterns, and coding standards. Use when onboarding a project for AI agents, setting up AI instructions, after significant codebase changes, or to audit AI convention files like AGENTS.md or .cursorrules.

Rebases a stacked-branch chain onto an updated base branch using gh-stack, guides through conflict resolution if needed, validates each branch, and pushes all updated branches. Creates PRs for any branch that lacks one, with fork-aware targeting. Works on repos with or without Stacked PRs enabled. Use when rebasing stacked stories or PRs onto main or another updated branch. Activated by commands: /start, /continue, /validate, /push.

Documentation workflow that converts requirements into structured AsciiDoc sections, runs Vale for style compliance, and produces merge-ready content. Use when creating or updating AsciiDoc documentation from Jira tickets, GitHub issues, or feature descriptions.

KCS article workflow that gathers bug context from Jira and user input, drafts a KCS Solution article in markdown, validates it against the KCS Content Standard, and produces a handoff message for the support engineer. Use when writing KCS articles for known issues with workarounds. Activated by commands: /gather, /draft, /validate, /handoff.

Pre-cycle Feature sizing workflow that assesses Features from Jira using T-shirt sizes (XS–XXL), produces per-team effort breakdowns (DEV, QE, UX, UI, DOCS), and writes results back to Jira. Accepts a single Feature or all Features in a Fix Version for batch sizing. Use when sizing Features for cycle planning, prioritizing a release backlog, or evaluating whether a Feature fits in a cycle. Activated by commands: /ingest, /assess, /apply.

Deep review of an AI skill directory. Critically evaluates structure, clarity, completeness, and consistency of SKILL.md, skills/*.md, commands/*.md, and guidelines.md. Use when reviewing, auditing, or validating an AI workflow skill. Activated by commands: /review.