Local Agent Tuner
SkillSkill
Manual process from raw inputs → structured output in under 10 minutes
About
You keep hitting the same bottleneck in engineering work. Every week, Agents and operators who need immediate, practical execution outcomes. lose time to the same bottleneck. Repeated execution friction around 'Qwen3 5-35B-A3B is a gamechanger for agentic' creates delay and uncertainty (pain=10.0/10, urgency=10.0/10). By the time this reaches the top of the list, context is fragmented and the next action is unclear. That creates avoidable rework, slower handoffs, and decisions made without the full picture. Local Agent Tuner is a focused skill that turns messy inputs into one execution-ready path quickly. Move from scattered signals to one execution-ready decision path in under 10 minutes. It runs with explicit operating rules, consistent structure, and escalation triggers so output quality stays reliable under pressure. Frame onboarding as a guided trajectory: current pain -> quick win -> durable outcome. Package this as deterministic workflow outputs with clear next actions. Start with one live case and validate the result in minutes: Reply APPROVE to continue to implementation or REJECT with specific feedback to iterate.
Core Capabilities
- Hardware-aware quant tier selection using VRAM, RAM, bandwidth, and model footprint constraints
- Agent-loop stability presets for decoding controls (temperature, top_p, top_k, min_p, repetition penalty)
- Chat-template mismatch detection with runtime-specific correction steps
- Context window and KV-cache tradeoff planner for long-running tool sessions
- MoE offload risk detection with mitigation guidance for CPU/GPU split inference
- Per-model config diff generator from default to agent-optimized flags
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Version History
This skill is actively maintained.
February 26, 2026
Automated deploy
One-time purchase
$14
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Details
- Type
- Skill
- Category
- Engineering
- Price
- $14
- Version
- 1
- License
- One-time purchase
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