Execute DAG-ordered workflow templates with parallel batches and resumable checkpoints.
npx skills add https://github.com/tcytan/tecton --skill wf-player
Please help me install this Agent Skill. Skill: wf-player Source: https://github.com/tcytan/tecton/tree/main/.claude/skills/wf-player Command: npx skills add https://github.com/tcytan/tecton --skill wf-player
wf-player eliminates the hassle of manually coordinating multi-step workflows by automatically loading a workflow template, executing its DAG nodes in the correct order, and persisting progress so you can resume after pauses or interruptions.
--context, validates required inputs, and supports dry-run planning.session-state.json and checkpoint snapshots, supports --resume <session-id>, and can pause at checkpoints for user review.Run your saved template by providing its slug and context, then let wf-player execute the workflow with checkpoints: invoke Skill(wf-player) with the arguments "<template-slug> --context goal='...'".
High-intent search queries and answers about installing and using this skill.
DAG execution with parallel batches works by deriving a topological order from the workflow template's JSON definition. It then runs nodes sequentially or in parallel batches, invoking node executors deterministically per node type.
Yes, you can validate workflow template inputs before execution using a dry-run planning mode. This checks that all required context variables from --context are properly bound and available before running any nodes.
Yes, the workflow player supports parallel node batches within its DAG execution. It resolves node argument references and runs grouped nodes in parallel batches alongside the standard topological order execution.
Workflow orchestration fails when context variables are missing because the system validates required inputs during context binding. You must provide all necessary variables via --context arguments before DAG execution begins.
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