jamesob
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local-llm
A comprehensive guide for building and configuring a high-end local machine to run state-of-the-art LLMs, with detailed hardware choices, BIOS tuning, and Docker-based model serving.
AI repository intelligence
FR-AI / ANALYSISWhy it is worth attention
It mimics a senior QA engineer's workflow with adversarial, evidence-first review, separate regression focus, and cost-aware context isolation, all in a read-only skill.
Who it is for
- Claude Code users
- Software developers
- QA engineers
- Team leads reviewing pull requests
Use cases
- Pre-merge QA review of feature branches
- Quick safety check of working changes before commit
- Pull request review via PR number or branch diff
- Staging-level verification of high-risk changes
Strengths
- Two independent specialized reviewers run in parallel for breadth
- Each finding requires file:line evidence, reducing noise
- Regressions are treated as first-class concerns, not afterthoughts
- Context isolation keeps heavy playbooks out of the main Claude session
Considerations
- Token cost scales with review depth; a thorough review is not free
- Requires Claude Code with Skills enabled, limiting to that ecosystem
- Residual risks and manual tests are honestly flagged but not automated
README quick start
Usage
Description
🚦 Senior-QA review skill for Claude Code: runs a regression hunter + a change reviewer in parallel, then gives one read-only SIGN OFF / DO NOT SHIP verdict with file:line evidence.
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