Neural web search via Exa AI. Search people, companies, news, research, code. Supports deep search, domain filters, date ranges.
Comprehensive research skill using Exa AI tools for web search and code context retrieval. Use when conducting research on technologies, finding code examples, discovering latest…
Semantic search, similar content discovery, and structured research using Exa API. Use when you need semantic/embeddings-based search, finding similar content, or searching by…
Use when searching for concepts, ideas, or similar content without exact keywords; when user asks "find similar to...", needs semantic discovery, research across perspectives, or…
Executes Exa AI search queries via unified Python CLI. Use when searching the web for current information, finding code examples, researching APIs, SDKs, or retrieving programming…
Free AI search via Exa MCP. Web search for news/info, code search for docs/examples from GitHub/StackOverflow, company research for business intel. No API key needed.
Generate comprehensive, dense, exam-ready cheat sheets in DOCX or LaTeX/PDF format from lecture slides, textbook chapters, course PDFs, or notes.
Analyze messy and unstructured Excel files to identify data quality issues, detect format inconsistencies, find missing values, and generate comprehensive analysis reports.
Walk through 15 CFR 734.2(b) to determine if a release of technology or source code to a foreign national is a deemed export. Covers nationality, technology vs.
Walk through 15 CFR 734.2(b) to determine if a release of technology or source code to a foreign national is a deemed export. Covers nationality, technology vs.
Execute systematic feature development using EPIC methodology (Explore, Research, Plan, Validate, Implement, Review, Iterate).
Transforms research findings into executive-ready briefings. Automatically activated when user mentions 'executive', 'briefing', 'C-suite', 'board', 'leadership', or…
Build executive presence through intentional personal brand development using Gartner research methodology
Manage experiments: create records, log results, compare runs, and collect data for paper writing
Rules and workflow for exp007 RL walking experiments. Applies when working on droid_env_unitree.py, droid_train_unitree_v*.py, exp007 reports, or any biped_walking RL…
Rules and workflow for exp008 RL walking experiments with narrow torso model. Applies when working on droid_env_unitree.py, droid_train_narrow_v*.py, exp008 reports, or any…
Perpetual disease expansion - research, validate, integrate new diseases into DxEngine
Use when CS or AE needs expansion ideas based on reviewed evidence, fit, and customer value without wishful thinking or pressure tactics.
A/B test design, hypothesis documentation, sample size calculation, feature flag implementation, and statistical significance analysis.
Given a research goal, use existing skills, training reports, and references to propose a small, concrete experiment plan.
Use when analyzing experiment results, interpreting CSVs from data/raw/ or data/processed/, writing results sections, calculating any of the 12 thesis metrics, running statistical…
Analyze GRPO training runs for learning dynamics and pipeline performance. Use when diagnosing training issues, reviewing Elo progression, checking throughput, or updating…
Analyze completed experiments and craft executive-ready summaries with insights and recommendations.
Use when the task involves A/B testing, experiment design, statistical comparison, or causal claims from controlled or quasi-controlled comparisons.
Analyze completed growth experiment results, validate hypotheses, generate insights, and suggest follow-up experiments.
Workflow 1.5: Bridge between idea discovery and auto review. Reads EXPERIMENT_PLAN.md, implements experiment code, deploys to GPU, collects initial results.
Prepare an experiment brief outlining hypothesis, design, success metrics, and operational plan.
Recommends using Hydra or YAML for experiment configuration to ensure clarity and reproducibility.
Design and run lean experiments — hypothesis, cheapest test, success criteria, decision. Use when you need to validate before you build.
Generates a rigorous experiment design given a hypothesis. Use when asked to design experiments, plan experiments, create an experimental setup, or figure out how to test a…
Design, evaluate, and document experiments, A/B tests, product tests, growth tests, validation tests, hypotheses, primary metrics, guardrails, sample considerations, decision…
Use when planning product experiments, writing testable hypotheses, estimating sample size, prioritizing tests, or interpreting A/B outcomes with practical statistical rigor.
Compare experimental method details between two Zotero PDF papers, identify protocol differences (ratios, dosages, timing, conditions), search supporting literature to explain why…
Otonom deney dongusu. Kod degisikligi yap, olc, karsilastir, kabul et veya geri al. Metrik bazli karar verme ile performans, boyut veya kalite optimizasyonu.
Finalize an experiment session into clean, reviewable branches. Use when asked to "finalize experiment", "clean up experiments", or "prepare experiment for review".
Generic experiment workflow guard: sync configured upstream remote and base branch, stop immediately on merge conflicts, prefer containerized verification when…
Author pre/post-iteration hooks for an experiment session. Use when the user asks to add research fetching, Slack/webhook notifications, persistent learnings, auto-tagging,…
Log ML experiments with hyperparameters, metrics, and plots; human interprets results and plans next experiments
Apply the scientific method to any CEO initiative — define a metric, make ONE change per cycle, measure impact, keep or discard, repeat.
Use when before initiating raw file conversion or feature extraction, when you have a heterogeneous collection of raw LC-MS files (.raw or .mzML) and sample information scattered…
STEDII framework for selecting trustworthy experiment metrics. Ensures metric validity and reliability.
[Experiment Type] Skill. Usage: (1) [Scenario 1], (2) [Scenario 2], (3) [Scenario 3]. Tested on [Verification Model/Environment].
Ontwerp een experiment om je aanname zo snel en makkelijk mogelijk te valideren. Twee routes: nieuw product of bestaand product.
Turn a refined research proposal or method idea into a detailed, claim-driven experiment roadmap. Use after `research-refine`, or when the user asks for a detailed experiment…
Analyse a finished A/B test and write an honest results readout with real statistics. Use when asked to read out an A/B test, analyse experiment results, check if a result is…
Interpret A/B test results in plain language and get a ship/rollback/extend recommendation with a stakeholder summary.
Use when about to declare verdict='supported' on any experiment in a quant-research project (mandatory co-gate with bug_review; both must pass).
Create a new experiment workspace directory: initialize git, write an AGENTS.md goal doc, and create references/ with index.md + notes/links markdown plus GitHub repos cloned…
Use when designing experiments to test whether a Claude Code skill is effective, or when planning how to validate a new or improved skill
Manages ML experiment tracking with MLflow, Weights & Biases, or SpecWeave's built-in tracking. Activates for "track experiments", "MLflow", "wandb", "experiment logging",…
Audit ML experiment tracking infrastructure for reproducibility gaps, parameter logging completeness, metric capture, artifact management, and pipeline orchestration.
Verify that all hyperparameters, metrics, and data references are properly logged.
Planning or executing thesis experiments. Covers the lifecycle from ideation through polishing, tracking table, SPEC.md format, stage structure.
Triggers LUCI try jobs to generate code coverage data. Supports Mode 1 (standard git cl try for source CLs) and Mode 2 (led for custom builds).
Executes the phased, multi-skill code coverage debugging playbook for triaging underreported code coverage in the Gerrit Commit Queue (CQ).
Statistical analysis and reporting for experimental datasets; use when you need to interpret experimental results, test significance (t-tests/ANOVA), or generate reproducible…
Best practices for designing reproducible ML experiments. Use when planning ablations, baselines, or controlled experiments.
A/B testing, randomization, sample size calculation, confounding control, and causal inference for data science.
生物实验设计、样本量和统计功效 skill。用于 omics study design、批次设计、paired/blocking、replicate、power analysis、multiple testing、randomization、covariate、time course 和统计风险评估。
Experimental design principles for scientific inquiry. Covers variable identification and control, control groups (positive and negative), randomization, blinding, sample size and…