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Research

Scientific computing and academic tools.

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scientific-computing

72

claygent

Use Clay's AI research agent (Claygent) for web research, custom data extraction, and information that does not exist in standard databases. Use when the user asks about Claygent, AI research in Clay, web browsing for data, custom data points, real-time company research, or scraping with AI. Triggers on "Claygent", "AI research", "Clay AI agent", "web research in Clay", "browse web", "research agent", "custom data points", "scrape with AI". Do NOT use for standard enrichments available via providers, Clayscript formulas, or general AI text generation without web research.

sachacoldiq

research

computational-chemistry

71

causal-inference

Production-grade Bayesian causal inference with PyMC, CausalPy, and DoWhy. Enforces DAG-first thinking, mandatory user checkpoints for assumptions, design-specific refutation, and defensible reporting with causal language guardrails. Trigger on: causal inference, causal effect estimation, treatment effects, counterfactuals, difference-in-differences (DiD), synthetic control, regression discontinuity (RDD), interrupted time series (ITS), instrumental variables (IV), propensity scores, DAGs, causal graphs, confounders, backdoor criterion, do-calculus, interventional distributions, pm.do(), pm.observe(), CausalPy, DoWhy, mediation analysis, refutation, sensitivity analysis, parallel trends, placebo tests, or any question of the form "does X cause Y" or "what is the effect of X on Y."

Learning-Bayesian-Statistics

research

lab-tools

71

name

A test skill

Roger-luo

research

academic

70

review-synthesis

Aggregate multi-reviewer feedback into consolidated, actionable recommendations. Use when relevant to the task.

jmagly

research

scientific-computing

70

data-analysis

Data analysis and statistical computation. Use when user needs "数据分析/统计/计算指标/数据洞察". Supports general analysis, financial data (stocks, returns), business data (sales, users), and scientific research. Uses pandas/numpy/scikit-learn for processing. Automatically activates data-base for data acquisition.

JinFanZheng

research

academic

70

ai-pattern-detection

Detects AI-generated writing patterns and suggests authentic alternatives. Auto-applies when reviewing content, editing documents, generating text, or when user mentions writing quality, AI detection, authenticity, or natural voice.

jmagly

research

academic

70

ai-pattern-detection

Detects AI-generated writing patterns and suggests authentic alternatives. Auto-applies when reviewing content, editing documents, generating text, or when user mentions writing quality, AI detection, authenticity, or natural voice. Use when relevant to the task.

jmagly

research

academic

70

doc-scraper

Scrape documentation websites into organized reference files. Use when converting docs sites to searchable references or building Claude skills.

jmagly

research

academic

70

doc-scraper

Scrape documentation websites into organized reference files. Use when converting docs sites to searchable references or building Claude skills.

jmagly

research

computational-chemistry

70

gate-evaluation

Validate phase gate criteria with multi-agent review and generate pass/fail reports. Use when relevant to the task.

jmagly

research

astronomy-physics

70

crs-sanity-check

Validate coordinate systems, units, and reprojection assumptions before analysis.

GeoRetina

research

astronomy-physics

70

data-source-intake

Standard intake checklist for evaluating geospatial data sources before use.

GeoRetina

research

astronomy-physics

70

geospatial-triage

Rapidly scope a geospatial request, validate data assumptions, and propose an execution plan.

GeoRetina

research

computational-chemistry

70

aptos-move-prover

Expert on Move Prover formal verification - specification language (MSL), preconditions, postconditions, invariants, aborts_if, ensures, requires, modifies, emits, global invariants, schema patterns, quantifiers, helper functions, pragma directives, verification strategies, and debugging proofs. Triggers on keywords move prover, formal verification, spec, invariant, ensures, requires, aborts_if, precondition, postcondition, quantifier, schema, pragma

raintree-technology

research

lab-tools

70

observability

Add observability to a codebase — logging, metrics, tracing, error handling, health checks. Teaches observability by instrumenting the student's actual project. Use when someone says /observability or asks about monitoring/debugging.

$fractal-nyc$

fractal-nyc

research

computational-chemistry

69

competitive-analysis-templates

Master competitive analysis templates including competitor deep-dives, battle cards, feature comparison matrices, and win/loss analysis. Use when analyzing competitors, creating battle cards for sales, positioning against alternatives, tracking competitive moves, or preparing for competitive threats. Covers competitive intelligence frameworks, positioning strategies, and templates from Crayon, Klue, and April Dunford.

slgoodrich

research

lab-tools

69

reality-testing

Reality is the ultimate judge. Use when need to ground insights in experience, test if it actually works, or validate through results not theory.

nikhilvallishayee

research

scientific-computing

69

symmetry-discovery-questionnaire

Use when ML engineers need to identify symmetries in their data but don't know where to start. Invoke when user mentions data symmetry, invariance discovery, what transformations matter, or needs help recognizing patterns their model should respect. Works collaboratively through domain analysis, transformation testing, and physical constraint identification.

lyndonkl

research

lab-tools

69

state-snapshot-instrumenter

Instrument programs (Python, C/C++, Java) to capture snapshots of key program states at runtime, including variables, memory, and call stacks. Use when you need to debug complex issues, reproduce test cases, prepare traces for formal verification, or analyze program execution. Supports manual instrumentation points, automatic function/method instrumentation, and conditional triggers. Outputs structured JSON snapshots for debugging, replay, and verification workflows.

ArabelaTso

research

lab-tools

69

reproduction-trace-instrumenter

Instruments programs to capture execution traces specifically for reproducing reported bugs, enabling consistent replay and diagnosis of failures. Use this skill when you need to reproduce a bug, capture execution traces for debugging, instrument code to record program behavior, generate replay scripts for bug reproduction, diagnose hard-to-reproduce failures, or perform deterministic replay of program execution. Triggers when users ask to instrument code for tracing, capture execution traces, reproduce bugs, generate replay scripts, or enable deterministic debugging.

ArabelaTso

research

lab-tools

69

replay-oriented-instrumentation

Instruments programs to record execution information for deterministic replay debugging. Use when debugging hard-to-reproduce bugs (race conditions, timing issues, intermittent failures, heisenbugs), reproducing production failures, or analyzing complex execution sequences. Records non-deterministic events (I/O, threading, randomness, time) to enable exact replay of program executions. Supports Python, JavaScript, Java, and C/C++ with both custom instrumentation and existing replay tools.

ArabelaTso

research

lab-tools

69

library-advisor

Recommend relevant Isabelle/HOL or Coq standard library theories, lemmas, and tactics based on proof goals. Use when: (1) Users need library lemmas for their proof, (2) Proof goals match standard library patterns, (3) Users ask what libraries to import, (4) Specific lemmas are needed for list/set/arithmetic operations, (5) Users are stuck and need to know what library support exists, or (6) Guidance on find_theorems/Search commands is needed. Supports both Isabelle/HOL and Coq standard libraries.

ArabelaTso

research

lab-tools

69

code-instrumentation-generator

Automatically instruments source code to collect runtime information such as function calls, branch decisions, variable values, and execution traces while preserving original program semantics. Use when users need to: (1) Add logging or tracing to code for debugging, (2) Collect runtime execution data for analysis, (3) Monitor function calls and control flow, (4) Track variable values during execution, (5) Generate execution traces for testing or profiling. Supports Python, Java, JavaScript, and C/C++ with configurable instrumentation levels.

ArabelaTso

research

scientific-computing

69

metamorphic-property-extractor

Automatically identify metamorphic properties (symmetry, linearity, additivity, input invariances) from programs or functions. Use when generating metamorphic tests, discovering program properties, validating transformations, or creating test oracles without explicit specifications. Analyzes control flow, data flow, and sample executions to output structured properties for metamorphic test generation and verification.

ArabelaTso

research

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