Use this agent when you need to create unit tests for your code in unittest.TestCase format, organized in a tests folder with concept-based subfolders. Examples: Context: User has just written a new authentication module and needs comprehensive unit tests. user: 'I just finished writing my user authentication…
Plugins package reusable slash commands such as /find, /peek, or /edit-last, post-user-turn displays, or both. They install into the active project or global fast-agent home under plugins/ and are enabled by name from that scope's configuration file.
Agent "prompting" from evalstate/fast-agent, covering prompting agents, sending messages, using generate() and multipart content, using structured() for typed responses and define your expected response structure.
Auto-generated tags (json, JSON, Json, json-parsing) fragment the routing index. A curated list makes the orchestrator's job tractable: when it looks up bytag.security, it gets every agent that should be there, not half of them plus a scatter of secure, sec, security-audit.
Expert in synthesizing information from multiple sources into well-structured, professional reports. Specializes in technical documentation and analysis summaries.
Expert in searching and retrieving technical documentation, best practices, and relevant resources from the web. Synthesizes findings from multiple sources.
Use this agent when the user asks for /meta-agent, a single agent builder, multi-agent team builder, or packaging existing agents into Agentlas architecture.
Every Promptise agent is powered by a Reasoning Graph. By default, buildagent() creates a ReAct graph (single node with tools) — and that default is smart by default: it manages context automatically (contextscope="auto"), so simple tasks are unchanged and deep tool loops stay token-efficient without you choosing…
Weekly movement briefing on the AI agent harness space — what climbed, what entered the radar, what died — from the best-of-Agent-Harnesses dataset, which rescores itself every week. Use on a schedule (cron, /loop, a routine) or whenever the user asks "what changed in agent land".
Picks the right AI agent harness for a described task or project. Use when the user asks "what harness/framework/agent tool should I use", compares agent frameworks, or starts an agent project without a stack decision. Grounded in the live best-of-Agent-Harnesses dataset, never in training-data memory.
Audits a codebase's AI agent stack against the live best-of-Agent-Harnesses dataset — finds which harnesses the repo uses, flags dead or graveyarded ones, and names live replacements. Use when the user asks "is my agent stack current", "audit my agent dependencies", or inherits an agent project of unknown vintage.
An AI and machine-learning engineering agent for adding language models and other AI features to software. It covers prompts, document search with generated text, and multi-step agent workflows.
An agent that designs complex business workflows using state machines, events, diagrams, and failure-recovery steps. A state machine describes the allowed stages of a process and the events that move it between stages.