Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Diagnose stalled, looping, or over-recovered Paperclip issue trees and propose a no-code product-rule plan. Use when asked why work stopped, why it looped, or how to prevent a tree from going too deep.
Create a new agent skill with progressive disclosure and bundled resources. Use when the user wants to create, write, or build a new skill.
Take a working agent prototype to a scalable, observable production deployment on Microsoft Foundry. Covers deployment patterns (client-hosted, hosted agents, agent workflows), the agent lifecycle, model routing, response caching, evaluation gates, human-in-the-loop approval, observability with OpenTelemetry, cost optimisation, and smoke-testing deployed agents with the AI Smoke Test action. Based on Lesson 16 of AI Agents for Beginners. USE FOR: deploy an agent to production, scale an agent, Microsoft Foundry hosted agent, Foundry Agent Service, model routing, response caching, evaluation gate, release gate, human approval workflow, agent observability, agent tracing, agent cost optimisation, smoke test a hosted agent, production customer support agent. DO NOT USE FOR: building your first agent (start with Lesson 01), running agents locally on-device (use local-ai-agents / Lesson 17), Azure infrastructure provisioning unrelated to agents, non-Foundry deployment targets.
End-to-end workflow for creating complete JavaScript concept documentation, orchestrating all skills from research to final review
Multi-baseline counterfactual cost analysis. Compares actual session spend to hypothetical always-haiku / always-sonnet / always-opus routing baselines. Answers "is the routing earning its keep?" Negative savings flag over-escalation; positive savings quantify the router's win.
Route tasks via the 3-tier model selector and learned patterns; emits a routing rationale via hooks_explain
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
Use when refreshing the default LLM presets shipped with Discourse AI — model versions, pricing, context windows, vision flags, and the matching `model_description` i18n keys.
Adopt and maintain the apache-magpie framework in a project repo via the snapshot-based adoption mechanism. The only framework skill committed in an adopter's repo; every other skill is a symlink the adopt sub-action wires up. Sub-actions: `/magpie-setup` - first-time adoption (default; main-checkout only) `/magpie-setup upgrade` - refresh the gitignored snapshot per the committed lock (main-checkout only) `/magpie-setup worktree-init` - symlink a worktree's snapshot to the main's `/magpie-setup verify` - health check + drift detection `/magpie-setup skill-sources` - fetch/pin/symlink skills from trusted external sources listed in the adopter trust list (main-checkout only) `/magpie-setup override <skill>` - open or scaffold an agentic override in `.apache-magpie-overrides/` `/magpie-setup unadopt` - reverse the adoption (snapshot, locks, symlinks, hook, doc sections); preserves `.apache-magpie-overrides/` by default (main-checkout only)
Inspect and optionally adjust the agent's runtime state. Use to check the current model or preset, context window, iteration progress and limits, token usage, workspace and tool configuration, subagent status, and request routing metadata such as channel, chat ID, and sender ID; diagnose unavailable capabilities; change allowed runtime settings; or store temporary session scratchpad values.