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OpenSmartRoute

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Everything your AI needs, in one place.

Ready-made agents, skills, personas, prompts, templates and tools. Each one is checked before it goes live, works with any model, and installs in a click. Rate what you use so the best rises to the top.

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33 results
Skill

skill-optimizer

Optimizes AI skills for activation, clarity, and cross-model reliability. Use when creating or editing skill packs, diagnosing weak skill uptake, reducing regressions, tuning instruction salience, imp

by mcollinaskills.sh
(0)
0Free
Skill

competitive-analysis

Analyze competitors systematically. Compare products, features, pricing, positioning, and market strategies. Generate comprehensive competitive intelligence reports.

by claude-office-skillsskills.sh
(0)
0Free
Skill

agent-evaluation-reporting

Use when summarizing agent evaluations where autonomous, assisted, failed, timed-out, or invalid outcomes must remain distinct and comparable.

by sickn33skills.sh
(0)
0Free
Skill

performance-expert

Expert-level performance optimization, profiling, benchmarking, and tuning. Use when the user mentions optimization, profiling, benchmarking, or scalability, or when the task involves Performance Fund

by personamanagmentlayerskills.sh
(0)
0Free
Skill

evaluating-code-models

Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language suppor

by davila7skills.sh
(0)
0Free
Skill

evaluating-llms-harness

Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training p

by davila7skills.sh
(0)
0Free
Skill

nemo-evaluator-sdk

Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud p

by davila7skills.sh
(0)
0Free
Skill

deepstream-profile-pipeline

Profile a DeepStream pipeline with Nsight Systems and derive its configs from the measurement. Use when the user asks for an efficient, performant, or profiled pipeline — or to benchmark, tune, or mea

by nvidiaskills.sh
(0)
0Free
Skill

rag-eval

Filesystem RAG benchmarks: corpus/, train.json, evaluate_rag.py (RAGAS quality). Not for prod monitoring, latency/throughput benchmarking (use rag-perf), or evals outside this repo layout.

by nvidiaskills.sh
(0)
0Free
Skill

rag-perf

Performance benchmarking for a deployed NVIDIA RAG Blueprint server: profiling pass + aiperf load test driven by a single YAML config. Not for accuracy / RAGAS scoring (use rag-eval) or for deploying

by nvidiaskills.sh
(0)
0Free
Skill

evaluating-code-models

Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language suppor

by orchestra-researchskills.sh
(0)
0Free
Skill

evaluating-llms-harness

Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training p

by orchestra-researchskills.sh
(0)
0Free
Skill

nemo-evaluator-sdk

Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud p

by orchestra-researchskills.sh
(0)
0Free
Skill

eval-designer

Use this skill when building evaluation frameworks to measure LLM quality, safety, accuracy, or alignment including test suites, human eval rubrics, automated evals, and metrics design. Not for traini

by nickcrewskills.sh
(0)
0Free
Skill

model-comparator

Use this skill when comparing AI or LLM models on benchmarks, capability, cost, latency, context window, or task-specific fit to help teams select the right model for their use case and budget. Not fo

by nickcrewskills.sh
(0)
0Free
Skill

testing-perf

Performance and load testing patterns — k6 load tests, Locust stress tests, pytest execution optimization (xdist parallel, plugins), test type classification, and performance benchmarking. Use when wr

by yonatangrossskills.sh
(0)
0Free
Skill

evaluating-llms-harness

Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training p

by ovachieverskills.sh
(0)
0Free
Skill

when-profiling-performance-use-performance-profiler

Comprehensive performance profiling, bottleneck detection, and optimization system

by aiskillstoreskills.sh
(0)
0Free
Skill

skill-autoresearch

Route reusable skill-improvement work into one bounded repo-local ratcheting packet: ratchet eligibility, benchmark readiness, loop charter freeze, baseline scoring, one-change mutation, support-surfa

by akillnessskills.sh
(0)
0Free
Skill

api-load-tester

Generates and executes load test scripts for APIs using k6, wrk, or autocannon. Creates realistic test scenarios from OpenAPI specs, route files, or endpoint descriptions. Use when someone needs to lo

by terminalskillsskills.sh
(0)
0Free
Skill

deepeval

Expert guidance for DeepEval, the open-source framework for unit testing LLM applications. Helps developers write test cases, define custom metrics, and integrate LLM quality checks into CI/CD pipelin

by terminalskillsskills.sh
(0)
0Free
Skill

k6

When the user wants to perform load testing, stress testing, or performance testing of APIs and websites using k6. Also use when the user mentions "k6," "load test," "performance test," "stress test,"

by terminalskillsskills.sh
(0)
0Free
Skill

agent-agent-evaluator

Expert en évaluation d'agents IA (benchmarking, human eval, latence, coût, précision, safety testing)

by ziri22GitHub
(0)
0Free
Skill

rag-eval

Filesystem RAG benchmarks: corpus/, train.json, evaluate_rag.py (RAGAS quality). Not for prod monitoring, latency/throughput benchmarking (use rag-perf), or evals outside this repo layout.

by SolizardkingGitHub
(0)
0Free
1

Find

Search or browse by kind. Every card shows who made it, how many people installed it and what they think.

2

Install

One click. You get a manifest the router understands, plus copy-paste snippets for the CLI, Python and YAML.

3

Rate and publish

Leave a star rating after you have used it. Made something useful? Publish it - free listings go live immediately.

Prefer the terminal? osr stack apply registry://starter installs the starter template.