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LLM Prompt Optimizer
optimizing-prompts
jeremylongshore/claude-code-plugins-plus-skills
99
Optimizes prompts for LLMs to cut token usage, lower costs, and boost response quality by analyzing, condensing, and rewriting instructions; useful when teams need faster, cheaper, clearer AI conversations.
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Perplexity Cost Tuning
perplexity-cost-tuning
jeremylongshore/claude-code-plugins-plus-skills
159
Optimize Perplexity AI search billing by routing queries to the right model, caching responses, limiting token output, filtering domains, and monitoring usage so your API spend stays predictable.
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Perplexity Pitfalls Guide
perplexity-known-pitfalls
jeremylongshore/claude-code-plugins-plus-skills
478
A concise reference for spotting and avoiding Perplexity Sonar anti-patterns during reviews, onboarding, or audits, covering citation parsing, model selection, recency handling, and context limits so integrations stay accurate and cost-effective.
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Perplexity Performance Tuning
perplexity-performance-tuning
jeremylongshore/claude-code-plugins-plus-skills
211
Optimize Perplexity Sonar API calls by selecting appropriate models, caching query hashes, streaming long research, and batching deduplicated requests for faster, cost-effective research or fact-checking workflows.
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Perplexity Policy Guardrails
perplexity-policy-guardrails
jeremylongshore/claude-code-plugins-plus-skills
82
Implements Perplexity Sonar guardrails covering query moderation, tiered model selection, citation scoring, and per-user quotas, so teams can plug policy checks into pre-commit hooks or CI to keep live searches safe and cost-effective.
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LLM Prompt Caching
prompt-caching
sickn33/antigravity-awesome-skills
271
Designs multi-tier prompt caching that cuts LLM costs by up to 90% through prefix, full-response, and semantic similarity caching, while emphasizing sensible invalidation to keep stored outputs accurate for production workloads.
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LLM Prompt Engineering
prompt-engineer
Jeffallan/claude-skills
248
Designs, refines, and evaluates prompts for LLM workflows, covering zero/few-shot, chain-of-thought, system personas, structured outputs, and testable evaluation suites to boost accuracy and efficiency.
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Ray Train Distributed Orchestration
ray-train
Orchestra-Research/AI-Research-SKILLs
60
Ray Train orchestrates distributed PyTorch/TensorFlow/HuggingFace training from laptops to thousands of nodes, adding elastic scaling, hyperparameter search, and fault tolerance while keeping most code unchanged.
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Retell AI Cost Tuning
retellai-cost-tuning
jeremylongshore/claude-code-plugins-plus-skills
393
Guides teams through analyzing Retell AI billing, tuning call durations, choosing cheaper voice models, and monitoring usage so they can reduce minute-based voice-agent costs and set budget alerts.
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Retell AI Policy Guardrails
retellai-policy-guardrails
jeremylongshore/claude-code-plugins-plus-skills
341
Implements lint rules, policy enforcement, and guardrails for Retell AI voice agents, covering content filtering, consent flows, cost caps, and escalation checks for CI or pre-commit setups.
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Production ML Engineering Guide
senior-ml-engineer
alirezarezvani/claude-skills
61
Guides ML engineers through deploying models, building MLOps pipelines, integrating LLMs, implementing RAG systems, and monitoring drift while emphasizing production readiness, cost control, and operational reliability.
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SkyPilot Cloud Orchestration
skypilot-multi-cloud-orchestration
Orchestra-Research/AI-Research-SKILLs
396
SkyPilot Multi-Cloud Orchestration codifies ML workload management by spinning up clusters across AWS/GCP/Azure, auto-optimizing costs, recovering spot instances, and unifying distributed GPU training and serving workflows.
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