Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Generate standalone skills from MCP servers. Use when users want to create a reusable skill for an MCP service. Triggers on "create skill for MCP", "generate MCP skill", "make skill from MCP server".
Orchestrate subagents in pi with git-based logging and Mission Control. Use when spawning multiple agents that need audit trails, rollback, branching, and real-time monitoring. Covers per-agent git repos, turn-level commits, workspace setup, and the shadow-git extension.
Create and configure AiderDesk agent profiles by defining tool groups, approval rules, system prompts, subagent settings, subagent filtering, and provider/model selection. Use when setting up a new agent, creating a profile, or configuring agent tools, permissions, and subagent behavior.
Build and run multi-agent pipelines using AgentFlow. Use when the user wants to orchestrate codex, claude, or kimi agents in parallel, in sequence, or in iterative loops. Trigger when the user mentions multi-agent workflows, fan-out tasks, code review pipelines, iterative implementation loops, running agents on EC2/ECS, or any task that needs multiple AI agents coordinated together. Also trigger for "agentflow", "pipeline", "graph of agents", "fanout", "shard", or "run codex on remote".
Build the project's vector-indexed knowledge base from files plus database metadata — optionally scoped to specific files / tables / datasources / domains. Scan the in-scope material, classify it into business domains, explore each domain's tables and docs in parallel with explore subagents (the validated-query SQL corpus is enumerated directly, no explore needed), then (after the user confirms a generation manifest — or directly, in the same turn, when the user has waived confirmation) route every artifact to its store via storage-classify, generating semantic_models / metrics / reference_sql (and mining any extra knowledge), and refresh AGENTS.md's KB index. The lightweight /init handles the AGENTS.md inventory plus file-based knowledge/memory; this skill owns the heavy vector-store generation.
Audit and reorganize every persistent store — semantic_models, metrics, reference_sql, knowledge, memory, AGENTS.md, skills — verifying each item sits in the correct store per storage-classify, and surfacing duplicates, misclassifications, conflicts, and stale/erroneous entries. Produce a Remediation Plan, STOP for confirmation, then execute. Use ask_user only for genuine decisions during analysis. If nothing needs fixing, report it and stop.
Optimize and improve existing Datus skills. Use when users want to edit a skill, improve its instructions, optimize its description for better triggering, or analyze skill performance based on usage sessions. Trigger phrases include "optimize skill", "improve skill", "edit skill", "fix skill", "skill not triggering".
Decide where a produced artifact must be persisted before writing it, then route it the prescribed way — semantic_models / metrics / reference_sql via the matching task() subagent, knowledge via extract-knowledge (lite), memory via add_memory, skills via create-skill, and AGENTS.md edited directly. Load before persisting any business fact, validated SQL, metric/model definition, session preference, project convention, or reusable workflow.
Workflow guidelines for durable Pi package, extension, skill, prompt, and theme changes in Gabs's Nix-managed Pi config. Use before editing ~/.pi/agent or home/gabriel/features/pi.