dburkart

Seed the auto-engineer SDLC toolkit into a project. Asks for the target project path, detects the tech stack and GitHub configuration, collects any missing values interactively, then writes customized skill files and Docker infrastructure into that project. Use when the user says "seed a project", "set up auto-engineer", "install the loop", or invokes `/seed`.

Two-way sync between a seeded project and this auto-engineer repo's templates — covers skills, Dockerfile, and scripts. Detects drift in either direction, presents per-file diffs, and lets you pull from template, push to template, or skip. Harness-internal tool — never seeded into target projects. Invoked as /sync.

Autonomously drive project work end-to-end — pick an unblocked issue, plan it, implement, push a PR, resolve CI and review-bot feedback, merge when green, then repeat. Use when the user says "auto-engineer", "auto-pilot", "go run the loop", or invokes `/auto-engineer`.

Drive a large body of work end-to-end — either from a single scoping issue or a fuzzy topic ("ship GA", "harden the review app") — by discovering scope in the backlog + codebase, filing sub-issues, iterating with the user, planning parallel workstreams, then spawning `/auto-engineer` subagents to ship the whole epic autonomously. Use when the user says "auto-manager", "manage this epic", "orchestrate /auto-engineer across…", or invokes `/auto-manager`.

File a new tracker issue with the correct priority and area labels applied up front. Use whenever an agent needs to capture a new unit of work — follow-ups from a merged PR, TODOs discovered while coding, or manually-requested issues. Invoked as `/file-issue`.

Autonomously drive project work end-to-end — pick an unblocked issue, plan it, implement, push a PR, resolve CI and review-bot feedback, merge when green, then repeat. Use when the user says "auto-engineer", "auto-pilot", "go run the loop", or invokes `/auto-engineer`.

Guidance on shrinking or fully resetting conversation context between auto-engineer iterations — when to use /compact (built-in summarise), /clear (built-in wipe), or a full process restart via scripts/restart-loop.sh.

The project's software-delivery workflow — how changes move from idea to merged on main. Use when starting a new task, committing, or opening/updating a PR.

Drive a large body of work end-to-end — either from a single scoping issue or a fuzzy topic ("ship GA", "harden the review app") — by discovering scope in the backlog + codebase, filing sub-issues, iterating with the user, planning parallel workstreams, then spawning `/auto-engineer` subagents to ship the whole epic autonomously. Use when the user says "auto-manager", "manage this epic", "orchestrate /auto-engineer across…", or invokes `/auto-manager`.

Autonomously drive vibix work end-to-end — pick an unblocked issue, plan it, implement, push a PR, resolve CI and review-bot feedback, merge when green, then repeat. Use when the user says "auto-engineer", "auto-pilot", "go run the loop", or invokes `/auto-engineer`.

Given an OS topic, deeply research it from canonical sources, draft an RFC, run it through simulated peer review, defend it, merge when approved, then break the work into tracked issues. Use when the user says "research <topic>", "write an RFC for <topic>", or invokes `/os-researcher <topic>`.

Post a daily project report as a GitHub Discussion summarizing wins, issues, risks, and low-hanging fruit from the last 24h of commits and new issues in dburkart/vibix. On first invocation, schedules itself to run every 24h via CronCreate (auto-expires after 7 days). Use when the user invokes `/project-report` or asks for a daily/recurring project status post.

Report real Claude Code session-quota state — percent of the 5-hour and 7-day windows used, reset times, and status (ok/warning/rejected). Same numbers Claude Code's own statusline reports. Use when the user asks about usage, quota, rate limits, or when auto-engineer's quota gate calls `/usage`.

File a new vibix GitHub issue with the correct priority/area/track labels applied up front. Use whenever an agent needs to capture a new unit of work — follow-ups from a merged PR, TODOs discovered while coding, or manually-requested issues. Invoked as `/file-issue`.

Drive one vibix issue-to-PR cycle inside Cursor Cloud — pick an unblocked issue, plan the change, implement it, validate it, push a branch, and open or update a PR. Use when the user says "auto-engineer", "auto-pilot", "go run the loop", or invokes `/cursor-cloud-auto-engineer`.

Build the vibix kernel, assemble a bootable ISO, or run it under QEMU. Use when the user asks to build, compile, make an ISO, boot, or run the kernel.

Run the Deep Researcher Reflect Evolve loop to produce a comprehensive implementation plan for a given topic, searching both a target codebase and the web for SOTA approaches. Adapts the architecture from Prateek 2026 (arXiv:2601.20843) to planning: sequential investigation-plan refinement via reflection, candidate crossover for dual-source answers, and one-shot final plan generation. Use when the user asks for a "deep plan", an implementation plan that requires codebase understanding + SOTA research, or invokes /deep-plan. Args: <topic> [--codebase <path>].

Planner-agent operations for the Deep Plan system: create an initial investigation plan (analyzing both codebase and SOTA), reflect on the current plan against accumulated findings, update the plan, or score planning progress (0-100). Use when a planning session needs a one-off planner operation outside the full /deep-plan loop, or when the user invokes /plan-planner with one of the modes below. Args: <mode> [<topic-slug>] [--codebase <path>]. Modes: create, reflect, update, progress.

Plan-writer operation for the Deep Plan system: produce a one-shot, comprehensive implementation plan from an existing investigation plan and global research context. The deliverable is detailed enough for another AI agent to implement at a high bar of quality. Use when a /deep-plan session has accumulated enough findings and the user wants the plan generated immediately, or when the user invokes /plan-report. Args: [<topic-slug>].

Searcher-agent operations for the Deep Plan system: generate the next search query for an active planning session, or answer a query using the Candidate Crossover algorithm with dual-source search (web + codebase). Three parallel candidates with diverse lenses search both the internet and the target codebase, then merge into one consolidated answer appended to the global research context. Use for one-off search operations outside the full /deep-plan loop, or when the user invokes /plan-search. Args: <mode> [<topic-slug>] [<query>]. Modes: query, answer.

Run the Deep Researcher Reflect Evolve loop on a topic to produce a PhD-level research report. Implements the architecture from Prateek 2026 (arXiv:2601.20843): sequential research-plan refinement via reflection, candidate crossover for web search answers, and one-shot final report generation. Use when the user asks for "deep research", a long-form report, an investigation that requires iterative web search + planning, or invokes /deep-research. Args: <research topic>.

Planner-agent operations for the Deep Researcher Reflect Evolve system: create an initial research plan, reflect on the current plan against the global research context, update the plan, or score research progress (0-100). Use when a research session needs a one-off planning operation outside the full /deep-research loop, or when the user invokes /research-plan with one of the modes below. Args: <mode> [<topic-slug>]. Modes: create, reflect, update, progress.

Reporter-agent operation for the Deep Researcher Reflect Evolve system: produce a one-shot, PhD-level research report from an existing research plan and global research context. Use when a /deep-research session has accumulated enough findings and the user wants the final write-up generated immediately, or when the user invokes /research-report. Args: [<topic-slug>].

Searcher-agent operations for the Deep Researcher Reflect Evolve system: generate the next search query for an active research session, or answer a query using the Candidate Crossover algorithm (3 parallel candidates with diverse lenses, merged into one consolidated answer that appends to the global research context). Use for one-off search operations outside the full /deep-research loop, or when the user invokes /research-search. Args: <mode> [<topic-slug>] [<query>]. Modes: query, answer.

Plan before executing long tasks: brainstorm lightly, write a durable plan to memory, then execute against it as a living contract. Use when a task involves multi-file changes, refactors, architectural decisions, >=3 steps, or anything expensive to get wrong. Also use when resuming a task that may have an existing in-progress plan — search memory for 'Status: in-progress' plans first. Do NOT use for single-file edits, read-only exploration, running a known command, or answering a factual question.