Search: eval · SkillMD

dromlakhani

Jes Pa Avs Indication

Determines when adrenal venous sampling (AVS) is indicated for functional subtyping of primary aldosteronism (PA) when surgical treatment is feasible and desired by the patient. Triggered when a clinician confirms PA diagnosis and asks 'Do I need AVS for subtyping?' or evaluates surgical candidacy.

10

michaelschecht

Sports Betting

Sports betting fundamentals including spreads, moneylines, totals, parlays, teasers, props, and futures. Use when analyzing sports betting markets, converting odds formats, evaluating bet types, or building a sports betting strategy. Also trigger for 'point spread', 'moneyline', 'over under', 'parlay', 'prop bet', 'teaser', 'sports odds', or 'sportsbook'.

0

google

Google Cloud Waf Security

Evaluates Google Cloud workloads against the Well-Architected Framework security pillar, identifies security requirements, and provides actionable recommendations for IAM, network security, data protection, and operational security.

14.4k

nvidia

Tao Run Deft Aoi

Automates the full DEFT AOI improvement loop for NVIDIA TAO VisualChangeNet / ChangeNet PCB inspection models, including baseline evaluation, RCA, synthetic defect generation, data mining, retraining, and deployment gating until KPI targets are met.

2.2k · bundle

orchestra-research

Fine Tuning Openvla Oft

Fine-tunes and evaluates OpenVLA-OFT and OpenVLA-OFT+ policies for robot action generation with continuous action heads, LoRA adaptation, and FiLM conditioning on LIBERO simulation and ALOHA real-world setups.

10.4k · bundle

mhassan0000

Search First

Guides the agent to research existing tools, libraries, and patterns before writing custom code, using parallel searches across package registries, MCP servers, skills, and GitHub, then evaluating and deciding to adopt, extend, or build.

1

brycewang-stanford

Ase Experiments

Use when designing or auditing the evaluation of an ASE (IEEE/ACM Automated Software Engineering) paper, covering real subject systems, fair runnable tool baselines, task-matched effectiveness metrics, ablations that isolate a learned component, oracle and correctness validation, contamination-aware LLM handling, and provenance for mining.

1k

bobmatnyc

Software Patterns

Compare tradeoffs and recommend architectural patterns — dependency injection, service-oriented architecture, repository, domain events, circuit breaker, and anti-corruption layer. Use when choosing between design patterns, planning microservices boundaries, evaluating system design alternatives, or asking 'which pattern should I use' for a specific coupling or resilience problem.

71 · bundle

thatrebeccarae

Cro Auditor

Conversion Rate Optimization auditing for landing pages, signup flows, checkout funnels, forms, and CTAs. Identifies friction points, runs heuristic evaluations, produces prioritized recommendations using ICE/PIE frameworks. Use when the user asks about conversion optimization, funnel analysis, landing page audits, form optimization, CTA testing, or checkout improvement.

105 · bundle

projectious-work

Research With Confidence

Investigates a question systematically before answering — verifying claims through sources, distinguishing known facts from inferences, and calibrating expressed confidence to actual certainty. Use when asked to research a topic, verify a claim, evaluate a technology, or answer a question where accuracy matters more than speed.

0 · bundle

intelli-verse-x

Ivx Cf Person Ml

ML / research person pack for Content Factory. Use when the user says person ml, @person-ml, ML person, research scientist person, or LLM researcher person. Auto-loads ml-research-engineer, llm-researcher, ai-research-scientist plus experiment-tracking, evaluation, cf-llm-model-usage.

0 · bundle

qhjqhj00

Apessrc

Evaluates the faithfulness of abstractive summaries by verifying if factual claims (masked as cloze questions) in the reference summary can be correctly answered using only the generated summary, compared against a gold-standard answer derived from the source context.

3

brycewang-stanford

Nsdi Workflow

Use when planning an NSDI campaign across the spring and fall deadlines — sequencing abstract and paper gates, notification waits, one-shot revision windows, artifact evaluation, and the May symposium — so a networked-systems project always knows which of the two yearly gates it is really building toward.

1k

sinhoneyy

Polars

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

11

levalencia

Polars

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

3 · bundle

desesbraker

Polars

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

2

welitonevoc

Polars

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

1

fradser

Verification Before Completion

Use when about to claim work is complete, fixed, or passing, before committing, before reporting a task done, or before telling the evaluator the batch is ready. Requires running the verification command and reading its output in this turn before any success claim; evidence before assertions always.

580

diegojcn

Polars

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

1

dromlakhani

Endo Pa Screen Hypertension

Determines whether to screen a patient with hypertension for primary aldosteronism based solely on the presence of hypertension. Use when a clinician asks whether to screen a hypertensive patient for PA, triggered by phrases such as "should we screen for aldosteronism" or "evaluate for secondary hypertension".

10

inskillflow

Polars

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

1

iamanacarolinarezende

Polars

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

0

doriangallo

Polars

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

1

mmehdi0606

Polars

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

2

francostino

Polars

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

63

casemark

Recall Plan

Drafts FDA product recall plans compliant with 21 CFR Part 7, covering Recall Committee governance, health hazard evaluation, recall classification, notification protocols, effectiveness checks, product disposition, and termination procedures. Use when developing recall SOPs, updating recall procedures, drafting recall readiness plans, or preparing FDA-regulated product recall documentation.

34

arjumaan

Polars

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

1

claude-dev-suite

Bitcoin L2 Ark

Ark protocol: VTXOs (Virtual Transaction Outputs) batched off-chain with periodic on-chain settlement. ARKADE (Ark Labs) is the first mainnet implementation. ASP (Ark Service Provider) coordinates rounds; users keep unilateral exit. USE WHEN: building Ark integrations, evaluating Ark vs Lightning, designing batched payment flows.

28

26bb

Polars

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

0

theheavenlyd3mon

Pydanticai

Build type-safe AI agents and graph-based workflows with PydanticAI and PydanticGraph. Agent creation, function tools, capabilities, dependency injection, structured output, streaming, multi-agent patterns, testing, evals, and graph state machines. Use whenever you are building agents, tool-using LLM workflows, or graph-based state machines in Python.

28 · bundle

sickn33

Polars

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

45.1k

mukul975

Analyzing IOS App Security With Objection

Perform runtime iOS app security assessments using Objection and Frida to inspect keychain, filesystem, and memory, bypass client-side protections, and evaluate data storage, network, and authentication controls during authorized penetration tests.

24.6k · bundle

mukul975

Performing Post Quantum Cryptography Migration

Assesses organizational readiness for post-quantum cryptography migration per NIST FIPS 203/204/205 standards, performs cryptographic inventory scanning, evaluates hybrid TLS configurations, and validates CRYSTALS-Kyber and CRYSTALS-Dilithium readiness.

24.6k · bundle

tinh2

Hr Ops

Analyzes an HR operations system for headcount planning effectiveness, attrition pattern detection, compensation benchmarking accuracy, workforce analytics maturity, and onboarding process optimization. Evaluates HRIS architecture, pay equity compliance, predictive attrition models, and people analytics governance.

13

qhjqhj00

Dior

Quantifies how sensitive a language model benchmark's reliability and ranking stability are to specific design choices, such as the selection of scenarios, subscenarios, examples, and few-shot prompts. Use when the user has predictions and gold and needs to compute DIoR.

3

akillness

Autoresearch

Run Karpathy-style autonomous ML search on a real training repo: choose the right mode (setup, program.md, bounded loop, results interpretation, or constrained-hardware adaptation), preserve the immutable prepare.py / 300-second / val_bpb contract, and route prompt/skill eval work away to LangSmith, Promptfoo, Braintrust, or skill-autoresearch.

42 · bundle

Plugins

Results for “eval”