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Found 196 Skills
Autonomous research agent that reads RESEARCH.md, infers what's needed, dynamically adjusts TODOs, and delegates to the right skill. Supports opt-in BFS mode for autonomous design space search. Respects a configurable supervision policy (presets: manual / checkpointed / autonomous / wild) governing notifications, approval gates, resource limits, and idea-change handling. Proactively surfaces gaps and asks before acting. Trigger phrases: "start research", "continue project", "what's next?", "explore design space", "autoresearch".
Create, review, and manage first-class goal artifacts that turn a broad ambition into an externally verifiable outcome. Goals are optional, long-lived workflow artifacts stored at goals/<slug>.md. They own intent, a validation contract, lifecycle, concise review evidence, and linked work. Use when the user wants to set a goal, review a goal, pause or reactivate a goal, achieve or abandon a goal, or change what a goal means. Not for specs, plans, memory, or checkpoints.
Review the current session for durable learnings, then route each one through the five-way matrix: wiki page, guidance file, checkpoint, task annotation/plan, or discard. Use when the session uncovered decisions, architecture facts, commands, conventions, gotchas, or open questions that future sessions should inherit. Not for source ingestion or correcting stale wiki claims; use /loam::adding-to-memory or /loam::amending-memory. Routes goal-specific progress to /loam::setting-goals.
Use when pausing, shutting down, handing off, or context-switching active work and future sessions need a compact resumable checkpoint derived from the current session context. Writes a small checkpoint note under wiki/checkpoints/ and then optionally records the user's intended return step. Not for durable learnings capture, wiki correction, or source ingestion.
Read a local source file or synthesize conversation context, then integrate admitted content directly into topic, entity, concept, and analysis pages in existing memory (the wiki substrate). Use this when the user wants to add a source to the wiki, add a document, ingest a local note, transcript, article, report, or PDF, or explicitly preserve the current conversation as a topic note. For session-learning routing across wiki, guidance, checkpoint, task annotation, or discard, use /loam::learning-from-session. Must not ingest a goal wholesale; admit only independently reusable findings.
The always-on protocol for the loam skill namespace. Use at session start and whenever a loam task appears. Routes goals and other loam work, explains the memory model (memory = umbrella; wiki, guidance, and checkpoints are substrates), and lists cross-cutting rules. This is a routing/meta skill — delegate to a specific loam skill rather than performing work itself.
Mark a checkpoint in the current conversation — compact it into a durable handoff document so a fresh agent can resume the work without context loss. Use when the user wants to preserve session state for a later or parallel session — phrases like "hand this off", "write a handoff", "drop a wheypoint", "checkpoint this", "compact the conversation", "I'm running low on context", "save where we are for the next session", "prep a handoff for another agent", "/wheypoint". Use even when the user just says "wrap up" or "I need to clear context" mid-task. Do NOT use for per-phase pipeline handoffs — those belong to `/cook`, `/press`, `/age`, and `/cure`.
LangGraph checkpointing and persistence. Use when implementing fault-tolerant workflows, resuming interrupted executions, debugging with state history, or avoiding re-running expensive operations.
Guides SwiftUI navigation using the Navigator/NavigatorUI library—NavigationDestination enums, ManagedNavigationStack, NavigationLink(to:label), deep linking (send/onNavigationReceive), checkpoints, dismissible views, and modular/provided destinations. Use when implementing or discussing SwiftUI navigation with Navigator, deep linking, checkpoints, or NavigatorUI.
LangGraph framework for building stateful, multi-agent AI applications with cyclical workflows, human-in-the-loop patterns, and persistent checkpointing.
Use when executing implementation plans. Dispatches independent subagents for individual tasks with code review checkpoints between iterations for rapid, controlled development.
Track long-horizon objectives across multiple sessions with milestone checkpoints, progress persistence, and drift detection