Explains graph engineering versus loop engineering, highlighting parallelism, cost-control and verification challenges. Demonstrates Anthropic/Claude Code tools and the Skill Creator plugin, and recommends building custom verification skills (standalone, embedded, second-opinion, orchestrator). Includes practical tips for model choice and token cost management.
– What graphs are: tasks split into nodes (independent agents) tied by edges to move data; shapes include diamond and fan-in for parallel review.
– Why graphs outperform loops: speed and selective model usage, but higher token usage and harder-to-trace failures.
– Verification approaches: standalone deep reviews, embedded checks during workflows, and second-opinion sessions for unbiased reviews.
– Implementation tips: use Skill Creator to build tested skills, pick stronger models for reviewers, chain specialized review skills, and prefer headless Chrome for faster visual tests.
Quotes:
One error in a small part of the graph disturbs the entire output.
A graph splits the main task into smaller parts and each part gets its own agent.
The agent that built the thing is the worst possible one to review it.
Statistics
| Upload date: | 2026-07-29 |
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| Likes: | 2413 |
| Comments: | 85 |
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| Statistics updated: | 2026-08-28 |
