Execute structured task plans with status tracking. Use when the user provides a plan file path in the format `plans/{current-date}-{task-name}-{version}.md` or explicitly asks you to execute a plan file.
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Execute structured task plans with automatic status tracking and progress updates.
When a plan is provided, all tasks in the plan must be completed. Before starting execution, recite:
"I will execute this plan to completion. All the 20 tasks will be addressed and marked as DONE."
STEP 1: Recite the commitment to complete all tasks in the plan.
STEP 2: Read the entire plan file to identify pending tasks based on task_status.
STEP 3: Announce the next pending task and update its status to IN_PROGRESS in the plan file.
STEP 4: Execute all actions required to complete the task and mark the task status to DONE in the plan file.
STEP 5: Repeat from Step 3 until all tasks are marked as DONE.
STEP 6: Re-read the plan file to verify all tasks are completed before announcing completion.
Use these status indicators in the plan file:
[ ]: PENDING
[~]: IN_PROGRESS
[x]: DONE
[!]: FAILED
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Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.
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Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
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Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
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This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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