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Found 6,688 Skills
Execute one role inside the loop CLI orchestrator. Use when the CLI asks you to act as planner, coding, or review agent and return strict handoff JSON while using loop-owned commands for Git integration.
How agentmemory is built, the iii engine primitives it runs on, its storage model, ports, and the viewer. Use when reasoning about how memory is stored or retrieved end to end, when extending the system, or when answering how agentmemory works under the hood.
Groom and route tickets on ANY project board through a shared status vocabulary (from raw `Triage` to a fully spec'd `Ready for Agent` or `Ready for Human`) so engineer only ever executes work that is already specified. Tracker-agnostic (GitHub Projects, Linear, or any board via a small adapter). Use to create/triage/groom issues, prepare work for an AFK agent, or manage the pre-implementation flow. Pairs with engineer + reviewer.
Run /develop to build a feature, UI or backend, from an approved design, a page, component, API, service, or data slice. If something load bearing is undecided and no spec records it, it stops and routes you to /architect; otherwise it reads the spec plus AGENTS.md, builds, and advances the scope.
Explain and write effective instructions for the `/goal` feature — the persistent self-checking agent loop (plan → act → test → review → iterate), available in agents like Codex, Claude Code, and Hermes Agent. Use when the user mentions `/goal`, "goal loop", "Ralph loop", wants to kick off a long-running autonomous agent run, asks how to write a goal prompt, or wants a one-paragraph goal instruction drafted.
Enter a friendly OpenSEO coach mode that explains workflows, recommends next steps, and helps users use agents, web search, scraping, and MCP data effectively.
Iteratively inspect an agent repository and optional traces, interview the user, and create, run, and audit Harbor evals one at a time. Use for agent evals, benchmark tasks, regression cases, trace-informed evals, verifier design, or controlled agent environments.
Designs, builds, and deploys AI agents or multi-agent systems on Google Cloud. Provides an interactive workflow to gather requirements, recommend a tailored architecture, and generate deployment instructions. Use when designing or implementing agentic systems on Google Cloud. Don't use for general Google Cloud solution architecture (use google-cloud-solution-architecture instead) or for narrow tasks targeting a single product without agent context.
Scaffold a minimal local Deep Agent in TypeScript by following the official quickstart, using provider-native web search instead of Tavily. Use when the user wants to quickly build or try a Deep Agent locally.
Version and manage your agent's prompts with LangWatch Prompts CLI. Use for both onboarding (set up prompt versioning for an entire codebase) and targeted operations (version a specific prompt, create a new prompt version). Supports Python and TypeScript.
Restore and read workflow state after a context break — re-inject workflow phase, task progress, and behavioral guidance into the current session, reconcile state against git reality, and verify whether a workflow exists. Use when the user says 'resume', 'rehydrate', 'where were we', or runs /rehydrate, or when the agent has drifted after context compaction. Do NOT use for saving or mutating state (that is /checkpoint).
Audit how agent context (CLAUDE.md / AGENTS.md / rules / skills) lines up with the code across a set of repositories and generate a self-contained HTML report — a short list of specific "things to check" (context behind the code, thin coverage for the codebase, oversized files, no per-area context), plus per-repo raw metrics and a folder tree comparing folder LOC to context coverage. Use when the user wants to audit context coverage across repos, "which repos are missing CLAUDE.md", "where is our agent context thin or stale", "context coverage across my org / projects folder", or "/context-coverage". Works on a local folder of clones or a whole GitHub org via the gh CLI.