202 skills · sorted by relevance
Find bottlenecks in large data transfers, imports, transformations, and historical data catch-up. Benchmark alternative processing methods, turn the fastest correct approach into a repeatable workflow, and verify file counts, row counts, and timestamps after execution.
Define success criteria before AI coding, then assess new capabilities and check that existing behavior still works using code, rules, models, or human review. Track repeated-attempt success metrics and reports to compare prompt changes, agent changes, or model versions.
Compare competitors using evidence-backed scores for positioning, brand expression, services, credibility, and other dimensions. Produce consistent profile cards and a two-axis chart showing which rivals occupy the market position your client wants to claim.
Turn scored competitor profiles into a report for strategic decisions. Organize market maps, comparison tables, unoccupied positions, threats, and prioritized recommendations so leaders can decide whom to compete with and how to differentiate.
Measure and optimize claude-flow v3 startup, search, attention processing, and memory use. Compare benchmarks against performance targets, track changes over time, and identify regressions to guide further optimization.
Measure Storybook startup from launching the process to displaying the first story. Separate server readiness from browser rendering, summarize repeated runs, and compare versions or feature flags to investigate slower startup.
Measure code performance and run benchmarks to investigate slow operations, regressions, or memory pressure. Establish a baseline, compare results under matching conditions, report variability, and rerun correctness tests after optimization.
Measure and optimize claude-flow v3 startup, search, attention processing, and memory use. Compare benchmarks against performance targets, track changes over time, and identify regressions to guide further optimization.
Add support for a programming language to codegraph, from connecting its parser and implementing code extraction to writing tests. Evaluate extraction quality and compare agent effort with and without codegraph across three real codebases.
Work with Waypoint models to analyze microbiome samples, the communities of microbes in a sample. Convert taxonomic abundance tables, generate numerical sample representations, configure further training for prediction tasks, and evaluate models on the Compass benchmark.
Solve optimization problems in Python with one or several competing goals. Define constraints, compare search algorithms, visualize trade-offs, and assess candidate solutions before choosing an option that fits your priorities.
Create consistent records for evaluating AI agents, including task success, invalid tool calls, token use, and actual costs. Produce CSV tables, database definitions, and JSON validation schemas to support reproducible comparisons and checks for performance declines.
Compare prompt revisions on a fixed set of test cases to identify regressions, meaning behavior that gets worse after a change. Record model settings, case-level evidence, missing results, costs, and evaluation limits for a reviewable decision.
Turns supplied AI agent evaluation results into reports that distinguish autonomous success, human-assisted success, failure, timeout, and invalid runs. Makes metric denominators, retries, timing, costs, and comparison conditions explicit to support evidence-based decisions.
Choose and track startup metrics suited to the business model and funding stage. Calculate customer acquisition cost, customer lifetime value, retention, and cash usage, then organize trends and targets for operational reviews and investor reporting.
Evaluate LLM applications using automated scores, human reviews, and model-based judging. Build repeatable checks to compare models or prompts, assess answer quality, and detect declines in performance before deployment.
Add a CUDA/C++ GPU operation to sgl-kernel using AOT, meaning compilation before use. Follow the implementation through PyTorch registration, Python API exposure, SGLang integration, correctness tests, and benchmarks that account for cache reuse.
Measure denoising latency in SGLang diffusion models and identify slow operations with profiler traces. Compare performance and output quality on the same GPUs, check existing optimizations, and gather evidence before starting low-level kernel work.
Set up and run kube-bench to check Kubernetes configuration against CIS security benchmarks. Select the appropriate benchmark and cluster components, then export JSON or JUnit results for automated pipelines and troubleshoot skipped checks or incorrect detection.
Design tests that assess an AI agent's capabilities and reliability. Covers repeated runs to measure variation, checks for required behavior, and adversarial testing to expose failures that ordinary successful examples may miss.
Compare compensation with market benchmarks by role, level, and location. Analyze salary band placement, flag outliers and retention risks, and model equity grants, meaning share-based compensation, to support hiring offers and compensation reviews.
Analyze social media post data to calculate engagement, click rates, and estimated returns on advertising spend. Compare results with supplied platform benchmarks, identify strong and weak posts, and generate recommendations for improving campaign performance.
Delegate research, coding, and testing from Claude Code to opencode workers. Run independent tasks in parallel with separate Git working directories, combine findings, track token use, and compare solo execution with coordinated workers.
Plan and troubleshoot large language and vision model training and inference. This guide uses hardware measurements to identify bottlenecks, address memory shortages and unstable training, and improve checkpointing, recovery, and performance across multiple GPUs and machines.