Claude Skills by monoes

Start a saved org via the Org Runtime v2 daemon (monomind org run/serve). Migrates v1-shaped configs first. The legacy prompt-orchestrated path lives in runorgv1.

Mastermind search — fuzzy cross-org search across tasks, goals, agents, approvals, routines, projects, and activity log. Returns ranked results with org context.

Mastermind secrets — manage org-scoped API keys and secrets consumed by agents. Store, rotate, list, and audit secrets without exposing values in logs or state files.

Use when creating new mastermind skills, editing existing mastermind skills, or verifying mastermind skills work before deployment

Mastermind skills — list, sync, and map skills available to org agents. Scans .claude/skills/ directory and shows which roles have access to which skill domains.

Mastermind stoporg — stop a running scheduled org loop by setting its status to "stopped". The next scheduled wakeup will read the status, skip all work, and not reschedule. Loop dies within one interval — no orphaned wakeups.

Mastermind tasks — view, create, assign, and move tasks on an org's task board. Supports parent-child chains, goal linkage, and status filtering.

Tech Port — deep-analyzes a foreign project, reviews the current monomind target to avoid conflicts and duplication, produces a scored port plan with mono-branded naming, and executes approved ports with full brand normalization.

Mastermind threads — list, view, and create conversation threads within an org. Threads are human-or-agent discussions attached to issues, goals, or the org itself. Reads from -threads.jsonl org state files.

Mastermind tree-control — pause, hold, release, or preview recovery for an issue/task tree in an org. Lets board members stop runaway loops, hold trees during review, and resume work when ready. Mirrors Paperclip's issue-tree-control API.

Mastermind workspace-detail — deep per-execution-workspace inspection and runtime control. Manage services, provision/teardown/cleanup commands, linked issues and routines, runtime logs, and configuration for a single execution workspace.

Mastermind workspaces — manage isolated git worktree workspaces per project. List, attach, detach, stop, and prune workspaces. Grouped by project with running service counts and agent assignments.

Use when starting feature work that needs isolation from the current workspace or before executing implementation plans — ensures an isolated workspace exists via native tools or git worktree fallback

Use when a request may need a Mastermind workflow such as planning, review, debugging, research, execution, organization work, or memory.

Use monomind's persistent memory store (local SQLite + local embeddings) to save, search, list, and retrieve cross-session knowledge — patterns, solutions, decisions. Trigger on "remember this", "store in memory", "search memory", "have we solved this before", "recall past decisions", or before starting work that benefits from prior context (debugging, refactors, repeated tasks).

Whole-repo audit for over-engineering. Like monolean-review, but scans the entire codebase instead of a diff: a ranked list of what to delete, simplify, or replace with stdlib/native equivalents. Use when the user says "audit this codebase", "audit for over-engineering", "what can I delete from this repo", "find bloat", "monolean-audit", or "/monolean-audit". One-shot report, does not apply fixes.

Harvest every `monolean:` comment in the codebase into a debt ledger, so the deliberate shortcuts and deferrals monolean leaves behind get tracked instead of rotting into "later means never". Use when the user says "monolean debt", "/monolean-debt", "what did monolean defer", "list the shortcuts", "monolean ledger", or "what did we mark to do later". One-shot report, changes nothing.

Quick-reference card for all monolean modes, skills, and commands. One-shot display, not a persistent mode. Trigger: /monolean-help, "monolean help", "what monolean commands", "how do I use monolean".

Forces the laziest solution that actually works, simplest, shortest, most minimal. Channels a senior dev who has seen everything: question whether the task needs to exist at all (YAGNI), reach for the standard library before custom code, native platform features before dependencies, one line before fifty. Supports intensity levels: lite, full (default), ultra. Use whenever the user says "monolean", "be lean", "lean mode", "simplest solution", "minimal solution", "yagni", "do less", or "shorte...

HTML-native animation system using GSAP — timeline-driven, API-controllable animations that run in the browser without video rendering or React. Covers timeline control, WebSocket/REST-driven playback, effects, and sequencing.

Coordinate in-process multi-agent work with monomind — topology selection, agent lifecycle, shared memory, and vote-based consensus. Use when scaling beyond a single agent on tasks with clear decomposition, or when a decision needs a recorded vote before work proceeds.

AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.

Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Monomind swarms

Create new Claude Code Skills with proper YAML frontmatter, progressive disclosure structure, and complete directory organization. Use when you need to build custom skills for specific workflows, generate skill templates, or understand the Claude Skills specification.

Find the single best specialized agent from the verified ~98-agent roster using two-stage LLM domain→agent selection

Remove AI writing patterns from prose. Use when drafting, editing, or reviewing any text to eliminate predictable AI tells — phrases, structural clichés, false agency, passive voice.

Comprehensive truth scoring, code quality verification, and automatic rollback system with a 0.95 confidence threshold for ensuring high-quality agent outputs and codebase reliability.