workflow

End-to-end, phase-gated software development lifecycle for AI agents. Turns a ticket, task, plan, or migration into a validated design, a dependency-graphed task board, and verified code. Triggers on "build this end-to-end", "plan and build", "break this into tasks", "pick up this ticket", "grill me on this", "run this migration", "absolute-work this", or any multi-step development task. Relentlessly interviews to a shared design, writes a reviewed spec, decomposes into atomic tasks on a persistent markdown board, then peels tasks one safe wave at a time with test-first verification. Handles features, bugs, refactors, greenfield projects, planning breakdowns, and migrations.

Advanced swarm patterns with dynamic topology switching and self-organizing behaviors for complex multi-agent coordination

Complex multi-agent swarm orchestration with task decomposition, distributed execution, and result synthesis

Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows

Bridge web interfaces with CLI workflows for seamless bidirectional integration

Create ergonomic slash commands for fast access to micro-skills with auto-discovery and parameter validation

Chain agent outputs as inputs in sequential or parallel pipelines for data flow orchestration

Detailed development workflow with modular patterns for git, review, testing, and deployment.

Executes implementation plans with progress tracking, checkpoint validation, and quality gates. Use after planning is complete and tasks are ready to implement.

Guides project ideation via Socratic questioning to produce a validated brief. Use before specification when requirements are unclear.

Polishes working code through successive quality passes in fresh subagents. Use after tests pass when code needs multi-dimension refinement before release.

Manages Claude Code sessions with naming, checkpointing, and resume strategies. Use when organizing long-running work or resuming across sessions.

Guides when to ask clarifying questions versus proceed autonomously. Use to reduce unnecessary clarifying questions when intent is clear.

Converts a specification into a phased, dependency-ordered implementation plan. Use after specification is complete and before execution begins.

Transforms project briefs into testable specifications with user stories and acceptance criteria. Use after brainstorming, before planning.

Autonomously simplifies code in your working changes or targeted files. Detects staged or unstaged git changes, analyzes for simplification opportunities following clean code and clean architecture principles, applies improvements directly, runs tests to verify nothing broke, and shows a structured summary with reasoning. Triggers on "simplify this", "refactor this", "clean up my changes", "absolute-simplify", "simplify my code", "make this cleaner", "tidy this up", "reduce complexity", "flatten this", "remove dead code", or when code needs clarity improvements, nesting reduction, or redundancy removal. Language-agnostic at base with deep opinions for JS/TS/React, Python, and Go.

Upgrade the Sentry JavaScript SDK across major versions. Use when asked to upgrade Sentry, migrate to a newer version, fix deprecated Sentry APIs, or resolve breaking changes after a Sentry version bump.

Find and fix issues from Sentry using MCP. Use when asked to fix Sentry errors, debug production issues, investigate exceptions, or resolve bugs reported in Sentry. Methodically analyzes stack traces, breadcrumbs, traces, and context to identify root causes.

Analyze and resolve Sentry comments on GitHub Pull Requests. Use this when asked to review or fix issues identified by Sentry in PR comments. Can review specific PRs by number or automatically find recent PRs with Sentry feedback.

Use when shipping code - complete workflow: merge main, run tests, multi-review with auto-fix, commit, push, create PR. Replaces manual commit-push-pr flows.

Use when building a managed team skills library for a real stack. Map work to shelves, browse before curating, write meaningful `whyHere` notes, and create a starter pack once the first pass is solid.

Review a project's PRs to check for issues detected in code review by Seer Bug Prediction. Use when asked to review or fix issues identified by Sentry in PR comments, or to find recent PRs with Sentry feedback.

Process multiple documents in bulk with parallel execution

Chain document operations into reusable pipelines

MCP server with 39 tools for Word, Excel, PowerPoint, PDF, OCR operations

Use when moving skills between library workspaces or upgrading from a personal library to a team library. Export from one workspace, import into another.

Automate document workflows with n8n - 7800+ workflow templates

Use when checking the overall health of a skills library. Run doctor, validate, check for stale skills, and verify generated docs are in sync.

Use when exploring the ai-agent-skills catalog to find, compare, and evaluate skills before installing. Always use --fields to limit output size and --dry-run before committing to an install.

Use when installing skills from a shared ai-agent-skills library repo. Inspect with `--list` first, prefer `--collection`, and preview with `--dry-run` before installing.

Access 1200+ AI Agent tools via Model Context Protocol (MCP)

Use when evaluating whether a skill belongs in a library. Preview content, check frontmatter, validate structure, and decide whether to keep, curate, or remove.

Use when a managed library is ready to publish to GitHub and hand to teammates as an install command. Run the GitHub publishing steps, then return the exact shareable install command.

Use when syncing or updating previously installed skills to their latest version. Always dry-run updates before applying, and check for breaking changes.

Use when regenerating README.md and WORK_AREAS.md in a managed library workspace. Always dry-run first to preview changes.