Results for “bench”

Gather keyword metrics, related terms, questions, full SERP benchmark, top-page extractions, and deep web research for a target keyword, then emit a beat spec the outline must satisfy. Triggered by /research <keyword> or as the first content stage of /blog-pipeline.

Guides users through defining goals, metrics, and scope, then runs an autonomous loop of code changes, testing, measuring, and keeping or discarding results for any programming task with a measurable outcome.

Measures inference latency of binarized, 8-bit, and 32-bit convolutional layers on edge devices to evaluate the efficiency and speedup of the Larq Compute Engine framework compared to standard implementations.

Audit AWS S3 bucket permissions to identify publicly accessible buckets, overly permissive ACLs, misconfigured bucket policies, and missing encryption settings using AWS CLI, Prowler, and IAM Access Analyzer.

Continuously monitor multi-cloud environments for misconfigurations, compliance violations, and security risks using Prowler, ScoutSuite, AWS Security Hub, Azure Defender, and GCP Security Command Center.

Audit Google Cloud Platform environments for security misconfigurations using Forseti, Security Command Center, and gcloud CLI to evaluate IAM policies, firewall rules, storage permissions, and CIS compliance.

Guides founders through the fundraising lifecycle from pre-seed to Series B, covering stage benchmarks, an 8-week execution timeline, and preparation checklists for pitch decks, financial models, and data rooms.

Evaluates speech language models on beyond-semantic speech attributes such as dialect comprehension, multi-turn context memory, emotion perception, age-aware response generation, and non-verbal cue handling, reporting accuracy and judge-based scores.

Provides a formal evaluation framework for Claude Code sessions, implementing eval-driven development (EDD) principles to define pass/fail criteria, measure reliability with pass@k metrics, and create regression test suites.

Evaluates the trade-off between file size reduction and image fidelity when encoding radio astronomy data using JPEG2000, benchmarking both lossless and lossy compression modes to determine the compression ratio at which visual artifacts first appear.

Deploy AWS Config rules for continuous compliance monitoring, including managed and custom rules aligned to CIS and PCI DSS frameworks, automatic remediation with SSM Automation, and multi-account compliance aggregation.

Use this skill when the user asks about trucking rate negotiation — cost-plus pricing, market rate benchmarking via DAT/Truckstop, fuel surcharge math, how to negotiate with brokers/shippers, when to walk away, contract pricing vs spot rates. Reference DAT Rate Analytics + freight market reports.

Generates and runs load tests for API endpoints using k6, Locust, or Artillery. Detects the API framework, creates realistic user behavior flows, executes ramp-up, sustained, spike, and stress profiles, and produces a bottleneck analysis with optimization recommendations.

End-to-end performance audit composite — baseline the slow metric, profile to find the real hotspot, implement the smallest targeted fix, re-benchmark to confirm improvement, capture in ADR. Use when users report slowness, CI shows runtime regression, or before a release. Never optimize without profiling first.

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

Multi-objective optimization with pymoo — NSGA-II, NSGA-III, MOEA/D, Pareto-front computation, constraint handling, and standard benchmarks (ZDT, DTLZ). Use when solving multi-objective or constrained optimization problems, computing Pareto-optimal trade-offs, or tackling engineering design problems with competing objectives. Part of the AlterLab Academic Skills suite.

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

Use this skill when creating a new Claude skill from scratch, editing or improving an existing skill, or measuring skill performance with evals and benchmarks. Invoke whenever the user says things like 'make a skill for X', 'turn this workflow into a skill', 'test my skill', 'improve my skill', 'run evals', 'benchmark this', or 'optimize my skill description'. Also use proactively when the conversation has produced a repeatable workflow that would benefit from being captured as a skill. Covers the full lifecycle: capture intent, draft SKILL.md, run evals, review with user, iterate, optimize description, package. NOT for general coding help, debugging runtime errors, building MCP servers, writing Claude hooks, or creating plugins - use domain-specific skills for those.

Run fb.ai's full SEO site audit — discover the real competitors, benchmark the brand's site against them, and report the page-level and sitewide issues with concrete fixes. The audit is the most expensive single fb.ai action (0.75 quota), so it is always priced and confirmed first. Run on demand or monthly.

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in...

Run KADATH (Kernel for Agentic Darwinian Adaptation, Tooling, and Heredity), a Docker-based evolutionary kernel that turns a goal into a locked, Architect-authored benchmark, then evolves a population of smolagents-based coding agents across epochs: each agent runs in an isolated container, gets graded against frozen evidence, and the population is culled, mutated, and reproduced generation over generation until it converges on the best-performing agent framework for that goal. Use when the user wants to propose/approve/run a KADATH evolutionary run, check a run's status or live dashboard, pause/resume/continue a run, export the winning agent population, or understand its Architect/Grader/Tweaker/Birther pipeline, evidence-freezing, or genome lineage/memory model. Triggers on: "kadath", "kadath.sh", "evolve an agent", "Darwinian agent evolution", "agent population fitness benchmark", "smolagents evolutionary run", "kadath dashboard", "genome lineage", "epoch champions".

Build and improve AI or probabilistic software through evaluation-driven development. Use for LLM applications, agents, prompts, RAG, tool use, classifiers, model migrations, quality regressions, golden datasets, LLM-as-judge rubrics, benchmarks, or requests to add evals and measurable release gates. Pair with TDD for deterministic code; do not use as the primary guide for ordinary unit testing without model behavior.

Autonomously optimize any Claude Code skill by running it repeatedly, scoring outputs against binary evals, mutating the prompt, and keeping improvements. Based on Karpathy's autoresearch methodology. Use when: optimize this skill, improve this skill, run autoresearch on, make this skill better, self-improve skill, benchmark skill, eval my skill, run evals on. Outputs: an improved SKILL.md, a results log, and a changelog of every mutation tried.

Optimize MATLAB design files for GPU Coder to generate faster CUDA code. Iteratively profiles, rewrites, and benchmarks until performance targets are met or diagnostics are resolved. Use when asked to: optimize for GPU Coder, improve GPU codegen performance, profile generated GPU/CUDA code, profile GPU MEX, fix gpuPerformanceAnalyzer diagnostics, speed up GPU MEX, reduce GPU memory transfers, improve kernel parallelism, rewrite MATLAB for CUDA, or run gpuPerformanceAnalyzer.

Optimizes SQL queries, designs database schemas, and troubleshoots performance issues. Use when a user asks why their query is slow, needs help writing complex joins or aggregations, mentions database performance issues, or wants to design or migrate a schema. Invoke for complex queries, window functions, CTEs, indexing strategies, query plan analysis, covering index creation, recursive queries, EXPLAIN/ANALYZE interpretation, before/after query benchmarking, or migrating queries between database dialects (PostgreSQL, MySQL, SQL Server, Oracle).

Use the HyperFrames CLI development loop: init, add, catalog, capture, lint, check, snapshot, compare, grade-compare, preview, play, present, beats, keyframes, single or batch render, publish, cloud, cloudrun, feedback, lambda, doctor, browser, info, upgrade, skills, compositions, docs, benchmark, telemetry, transcribe, auth, tts, and remove-background. Also use when diagnosing build or render failures. validate, inspect, and layout are deprecated aliases; use check. Covers local, HeyGen-hosted cloud, AWS Lambda, and Google Cloud Run rendering.

WordPress performance optimization: performance, slow query, N+1 query, Query Monitor, autoload audit, autoload options, object cache, Redis, wp_cache, transient, cache invalidation, Core Web Vitals, LCP, CLS, INP, FID, profiling, Xdebug, microtime, pre_get_posts, database query optimization, TTFB, page load, Vite bundle, lazy loading, critical CSS — benchmark before/after, never optimize blind. Invoke for: slow page load, N+1 query problem, caching strategy, transient API, dequeue unused scripts, performance profiling, Core Web Vitals improvement.

Builds, simulates, and runs quantum circuits with Cirq, Google Quantum AI's framework for NISQ hardware, noise-aware low-level circuit design, and noise characterization. Use when targeting Google Quantum AI processors (Sycamore/Weber), designing noise-aware NISQ circuits, or running characterization experiments (randomized benchmarking, XEB). For IBM Quantum hardware and Qiskit Runtime prefer alterlab-qiskit; for gradient-trained quantum ML and hybrid quantum-classical models prefer alterlab-pennylane; for open-system Lindblad/master-equation dynamics prefer alterlab-qutip. Part of the AlterLab Academic Skills suite.

Comprehensive blog writing skill that handles technical blog posts, personal voice writing, brain dump transformation, and category-aware AEO-optimized content. Use when: (1) writing, editing, or proofreading a blog article or post, (2) transforming unstructured brain dumps into polished posts, (3) writing in specific personal voices (Jarad, Nick Nisi), (4) creating category-aware technology/company/product posts, (5) building tutorials, deep dives, postmortems, benchmarks, or architecture posts, (6) writing engineering blogs, dev blogs, programming blogs, coding tutorials, or documentation posts. Triggers: blog post, blog writing, technical blog, dev tutorial, brain dump, article, content writing, developer article, engineering blog, programming blog, coding tutorial, documentation post, technical writing, blog editing, proofreading, developer content