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Found 13,279 Skills
Use when an agent needs to drive the full agent-manager lifecycle through `amctl` — install the CLI, log in, create/deploy an agent, list projects and agents, watch build progress, fetch build/runtime logs and metrics, and pull traces.
Design agent-native applications where agents replace UI users as the primary actor. Use when designing MCP tools, agent-loop architectures, system prompt design, hooks policy, shared-workspace file patterns, or self-modifying agent systems.
Builds and improves Browserbase Agent API demos through an Autobrowse-style outer loop: run a fixed task, collect Agent messages and session logs, score the result, revise one system-prompt heuristic, and confirm convergence. Use when creating a Browserbase Agents demo or POC, optimizing an Agent system prompt, diagnosing flaky Agent runs, or applying auto-research/autobrowse to the Browserbase Agents API.
Check if the CLI functions newly merged into te-cli are reasonable. Scan from six dimensions: command registration, business domain ↔ skill pairing, skill document coverage, skill internal consistency, user document synchronization, and engineering robustness, to locate issues such as command loading failure, AI agent guessing parameters due to empty documents, and outdated user documents. Use it when merging new command domains/commands, adding or modifying skills, performing pre-release self-checks, or evaluating the completeness of CLI functions.
AE Agent platform CLI for Agent, archived conversation, automation, model, MCP, Skill, attachment, and user-memory work. Use when managing these resources, browsing Agent markets, restoring archived conversations, creating scheduled automations, persisting user memory, or answering from user preferences, background, stable workflows, or historical conventions.
Agent testing methodology - run agents with test inputs, observe outputs, iterate until outputs are accurate and well-structured.
Concurrent investigation pattern - dispatches multiple AI agents to investigate and fix independent problems simultaneously.
Evaluate and improve Claude Code commands, skills, and agents. Use when testing prompt effectiveness, validating context engineering choices, or measuring improvement quality.
Understand the components, mechanics, and constraints of context in agent systems. Use when writing, editing, or optimizing commands, skills, or sub-agents prompts.
Run agentlint CLI after code changes to catch patterns for AI evaluation. Activate when finishing code modifications, before committing, or when the developer asks to lint, scan, or review code with agentlint. Covers agentlint check, agentlint list, agentlint review, agentlint init, inline suppression, and output interpretation.
Classify a code quality concern into the right enforcement tool and act on it. Activate when the user wants to enforce a pattern, catch a mistake, add a check, create a rule, prevent a practice, guard against regressions, set up linting, improve their feedback loop, or asks "how do I make sure X."
Use when executing implementation plans with independent tasks in the current session