workflow

Orchestrates multi-agent parallel workflows with verification stages and structured output schemas.

Updated Aug 22, 2026
One-click install
npx skills add https://github.com/Bascht74/videopodcast-magic --skill workflow-bascht74
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: workflow
Source: https://github.com/Bascht74/videopodcast-magic/tree/main/.claude/skills/workflow
Command: npx skills add https://github.com/Bascht74/videopodcast-magic --skill workflow-bascht74

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Wide tasks like audits across many files, sweeps for one class of fault, or findings that must be refuted before they are believed exceed what a single agent can do reliably. This Skill defines how to cut such work into parallel agent strands that run fast and come back trustworthy. ## Core Features & Use Cases - Pipeline over barrier: Uses pipeline() so each item runs its whole chain independently, reserving parallel() barriers only for stages that genuinely need all prior results at once. - Finder-refuter pattern: Every finding goes to an agent whose job is to prove it wrong, with structured verdict schemas and rejections reported with reasons. - Cost control and resumability: Counts strands and estimates tokens before a run, asks past the 2.5 million token line, and has strands save after every block so a stop costs a block, not a strand. - Use Case: Auditing every test file in a repository for one class of fault: scout the file list with grep, fan out one reader per file at low effort, then send each file's findings to a single high-effort reviewer that attacks them all. ## Quick Start Use the workflow skill to plan and run a multi-agent audit across these files, with a refuting agent checking every finding.

Frequently Asked Questions about workflow

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run multiple AI agents in parallel on one task?▼

Scout the work-list yourself first with grep or file listing, then hand it to a workflow script. Use pipeline() so each item runs its full chain independently, and set effort per stage: low for mechanical reading, high where decisions are made.

What is the difference between pipeline and parallel in agent workflows?▼

parallel() is a barrier: nothing in the next stage starts until everything in the current one returns. pipeline() lets each item proceed through its whole chain on its own, so a finding is verified the moment it exists rather than waiting for the slowest sweep.

How do I verify findings from AI agents before trusting them?▼

Send every finding to a refuter agent instructed to assume it is wrong until the file says otherwise, and to reject when unsure. Use a structured verdict schema and ask for rejections with reasons, since rejections reveal where finders are careless.

When should I not use a multi-agent workflow?▼

Skip a workflow for single edits with a known cause, which are faster by hand. Workflows are worth it only to be thorough, to be sure through independent refutation, or to cover more ground than one context holds.

How do I control token costs in large agent runs?▼

Count strands and estimate tokens before starting; runs over roughly 2.5 million tokens should be proposed, not started. Prefer one reviewer per file over one verifier per finding, which measured a fortieth of the cost for the same coverage.

What happens to agent work when a workflow is stopped?▼

A workflow cannot be paused, only stopped, and running strands receive no messages. Work survives only if already on disk, so strands should write after every block and resume from the last entry in an existing file.