Claude Code Skills · page 63

Create publication-quality plots and visualizations using matplotlib and seaborn. Works with ANY LLM provider (GPT, Gemini, Claude, etc.).

Computer science bibliography via DBLP API. Use when: user asks about CS publications, author publication lists, or venue (conference/journal) metadata. NOT for: non-CS publications or citation counts.

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Execute autonomous multi-step deep research on any topic. Use when the user asks for comprehensive research, literature reviews, competitive analysis, topic deep-dives, or wants to understand a complex subject from multiple angles. Triggers on "deep research", "research on", "investigate", "literature review", "comprehensive analysis", "what do we know about", "summarize research on".

Discord ops via the message tool (channel=discord).

Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files). Triggers include: any mention of 'Word doc', 'word document', '.docx', or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a 'report', 'memo', 'letter', 'template', or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.

Orchestrates a full drug discovery workflow from target identification through lead optimization. Use when searching for drug candidates against a biological target, evaluating compound libraries, or optimizing hits for drug-likeness. NOT for pure protein structure analysis or single-compound lookups.

End-to-end drug discovery platform combining ChEMBL compounds, DrugBank, targets, and FDA labels. Natural language powered by Valyu.

Supports drug discovery workflows including target identification, virtual screening, ADMET prediction, lead optimization, pharmacokinetics modeling, and drug repurposing analyses; trigger when users discuss drug targets, compound libraries, medicinal chemistry, or pharmaceutical development.

Economic analysis including econometrics, causal inference, time series economics, game theory, welfare analysis, and economic modeling. Use when user works with economic data, regression analysis, instrumental variables, difference-in-differences, RDD, panel data, or economic theory. Triggers on "econometrics", "regression", "causal inference", "instrumental variable", "difference-in-differences", "panel data", "game theory", "supply demand", "GDP", "inflation", "economic model".

Supports education research including pedagogical method evaluation, learning analytics, assessment design, curriculum development analysis, and educational technology evaluation; trigger when users discuss teaching effectiveness, learning outcomes, educational interventions, or student performance data.

Control Eight Sleep pods (status, temperature, alarms, schedules).

Analyzes energy systems including renewable energy resource assessment, power grid modeling, battery storage optimization, energy efficiency evaluation, and techno-economic analysis of energy technologies; trigger when users discuss solar, wind, grid integration, energy storage, or power system design.

Query Ensembl genome database REST API for 250+ species. Gene lookups, sequence retrieval, variant analysis, comparative genomics, orthologs, VEP predictions, for genomic research.

Analyzes environmental and climate data including temperature trends, pollution monitoring, ecological modeling, carbon footprint assessment, and biodiversity metrics; trigger when users discuss climate change, ecosystems, pollutants, or sustainability assessments.

Performs epidemiological analyses including disease modeling (SIR/SEIR), outbreak investigation, risk factor identification, incidence/prevalence estimation, and causal inference from observational data; trigger when users discuss disease spread, public health data, or population-level health patterns.

Design scientific experiments including sample size calculation, randomization, control groups, blinding, and study protocols. Covers RCTs, quasi-experiments, factorial designs, A/B tests, survey design, and observational studies. Use when user asks to design an experiment, calculate sample size, plan a study, set up controls, or create a research protocol. Triggers on "design experiment", "sample size", "power analysis", "study design", "control group", "randomization", "A/B test", "factorial design", "survey design".

Design rigorous scientific experiments with power analysis and controls. Use when: user needs to plan an experiment, calculate sample sizes, or set up controls. NOT for: running experiments or analyzing collected data.

Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.

Verify scientific claims, political statements, and environmental assertions against evidence

Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.

Operate and maintain the local MCP server for this repository. Use for MCP tool updates, policy-guard changes, host configuration, and MCP runtime troubleshooting.

Run release-readiness checks for this repository. Use when validating docs, scripts, verification coverage, and operational safety before merge or release.

Understand this repository quickly before making changes. Use for architecture discovery, ownership mapping, command selection, and initial implementation planning.

Debug production-like issues in this repository with disciplined evidence gathering. Use when fixing failing workflows, regressions, flaky behavior, or data inconsistencies across hooks, API, DB, websocket, and UI.

Operate and maintain the local MCP server for this project. Use when creating MCP host config, troubleshooting tool connectivity, modifying tool domains, or adjusting safety policy flags.

Implement a feature safely end-to-end in this repository. Use when adding or changing functionality across backend, frontend, or MCP with required verification and documentation updates.

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Audit a component or page for accessibility issues and fix them

Audit screens for UX issues using Nielsen's heuristics and modern mobile UX best practices

Generate a new UI component following the StyleSeed design conventions

Generate UX microcopy (button labels, error messages, empty states, toasts) following a casual-but-polite voice and tone

Add appropriate user feedback states (loading, success, error, empty) to a component or page

Design user flows and navigation structure following proven UX patterns

Quick automated lint — detects common design system violations in seconds

Apply a named StyleSeed motion to a component — either one of the 5 personality seeds (Spring/Silk/Snap/Float/Pulse × entrance/exit/hover/press/layout) or a distinctive keyword move from the motion library (toggle-flip, toggle-curtain, reveal-blur, pop-in, shimmer, …). Translates vibe words into framer-motion code from one source of truth.

Scaffold a new mobile page/screen using the StyleSeed layout patterns

Generate a composed UI pattern (card layout, list, form section, grid, etc.) using design system primitives

Review UI code for design system compliance, accessibility, and best practices

Interactive setup wizard — guides you step-by-step to configure the design system for your project

View, add, or modify design tokens in the StyleSeed design system

Update StyleSeed engine in your project — analyzes what's outdated and updates safely

Score a UI file's design quality 0-100 against StyleSeed's design language — per-category breakdown, the worst offenders, and a prioritized fix list. A quantified version of /ss-review.

Reviews UI/frontend code and tells you exactly why it "looks AI-generated" — then how to fix it. Use it when a React/Tailwind/HTML interface looks off, generic, or unfinished, when you want a design score before shipping, or when asked to make UI look more professional, polished, or "designed, not generated." Self-contained; based on the open-source StyleSeed design engine.

Verify and debug changes to CORAL itself — smallest reproduce loop per area (grader / daemon / CLI / hooks / manager / workspace / hub / template / config / web), where to look when something breaks (hung graders, agent restart loops, stalled agents, missing heartbeat actions, corrupted shared state, broken worktree symlinks, grader import errors, wrong-task resume), how to inspect a live or finished run under `.coral/public/`, and the canonical lint/test commands. Use when editing code under `coral/` or chasing a CORAL bug, NOT when adding a new task or extending the framework.

Add a new component to the CORAL framework itself — a new agent runtime under `coral/agent/builtin/` (claude_code/codex/cursor_agent style), a new CLI command in `coral/cli/`, a new bundled skill or subagent template under `coral/template/skills/` or `coral/template/agents/`, a new hook in `coral/hooks/`, a new field in `coral/config.py`, or a framework-level extension to the grader stack under `coral/grader/`. NOT for writing a per-task grader or adding an example task — use `coral-new-task` for that. NOT for debugging existing code — use `coral-debug`.

End-to-end recipe for adding a new task under `examples/` — the three pieces that have to line up (`task.yaml`, `seed/`, and `grader/`), what to put in each, the `TaskGrader` API surface, the `coral validate` → smoke-test loop, and the common mistakes (repo_path pointing at the wrong dir, score direction backwards, hidden answer keys leaking into seed/, grader writing to codebase_path which the daemon force-removes, private-vs-public confusion, missing `run()` signature). Use whenever the user wants to add a new CORAL task or port an existing benchmark into CORAL.

Research the problem domain before coding. Web search for techniques, save raw sources, write structured findings, update the index.

Organize the shared notes directory when it becomes hard to navigate. Restructure within research/ and experiments/, deduplicate, update index.md.

Autonomously create, test, and optimize skills by detecting reusable patterns in your own work. Use when you notice repeated tool sequences, recurring code patterns across attempts, or insights that should be captured as a packaged skill. Also use to benchmark and iterate on existing skills.

The fast path from zero to a running CORAL experiment — what CORAL is and when to reach for it, installing the `coral` CLI, registering a runtime with `coral setup`, and the `.coral_workspace/` convention for pointing CORAL at code you already have and want optimized. Use this whenever the user asks "what is coral", "should I use coral for this", wants to install or get coral set up, hits a "command not found" for coral or doesn't have it installed yet, or says "use coral to optimize / speed up / improve this code" and you need the end-to-end onboarding from install to a launched run. Hands off to `setting-up-coral` (runtime bindings), `creating-a-coral-task` (grader authoring), and `running-coral-experiments` (operating a run) for depth.

Author a new CORAL task — the three pieces that must line up (`task.yaml`, `seed/`, a packaged `grader/`), the `coral init` → `coral validate` → smoke-test loop, and how to pick a grader pattern (stdout float, test pass-rate, ratio-vs-baseline, multi-metric, or an LLM rubric judge). Use whenever the user wants to create a CORAL task, write or wire a grader, port a benchmark into CORAL, score open-ended outputs (reports/memos) with a judge, or debug a grader that crashes on the seed / ranks the leaderboard backwards / leaks the answer key. Deep references for the TaskGrader API, grader patterns, rubric judges, and the full task.yaml schema live alongside this skill.

Run and manage CORAL experiments from the operator side — launch agents with `coral start` (dotlist overrides, model/count, tmux vs local), monitor with `coral status` / `coral log` / `coral show` / the web dashboard, and drive the loop with `coral resume` (inject instructions, fork from an attempt), `coral heartbeat` (tune reflection cadence), and `coral stop`. Use whenever the user wants to start a CORAL run, check on agents, read scores/leaderboard, steer or resume a run, diagnose agents that keep restarting or fail every eval, scale to more agents or islands, or stop a run. Deep references for steering/heartbeat tuning and scaling/troubleshooting live alongside this skill.

One-time machine setup after installing the `coral` CLI — register local agent runtimes as named bindings with `coral setup` / `coral setup agent`, validate them with `coral agents doctor` (incl. a live hello-ping that catches expired auth and model typos), and reference them from a task via `agents.binding`. Use when the user is configuring which agent runtimes/models coral can use, hits a "runtime not found" / auth error when starting a run, or asks how to set up claude/codex/cursor for coral.

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Import recorded driving sensor data (GPS, camera, lidar, actor tracks, lanes) into scenariobuilder.* objects (GPSData, CameraData, LidarData, ActorTrackData, Trajectory, laneData) and run preprocessing — synchronize, offset correction, crop, normalizeTimestamps, convertTimestamps. Also: compute actor tracks from lidar when no annotations exist, attach camera/lidar mounting + intrinsics, export to MAT/workspace/timetable/script. Use for raw driving dataset files (KITTI, nuScenes, Waymo, Pandaset, ROS/ROS2 bags, .mat, .csv, .mp4) or driving/vehicle/sensor logs that need wrapping. drivingLogAnalyzer (DLA) is OPT-IN ONLY — invoke only on explicit user request ('DLA', 'open in DLA', 'inspect/explore/analyze the recording') or reported sensor problem (sync drift, timestamp mismatch, overlay misalignment). NEVER auto-launch DLA after wrapping (Rule 0). For 'build scenario / export to RoadRunner / drivingScenario / OpenSCENARIO / Unreal / simulate', hand off to matlab-scenario-builder.

Generate driving scenes, scenarios, road surfaces, and 3D content from already-wrapped scenariobuilder.* sensor data (GPS, camera, lidar, actor tracks) using Scenario Builder for Automated Driving Toolbox. Use to BUILD, EXPORT, or AUGMENT a virtual scenario/scene/map: ego or actor trajectories, trajectory smoothing, OpenCRG road-surface extraction, 3D asset generation, static-object placement, point-cloud georeferencing + elevation, lane-based ego localization, sensor-fusion tracking, scenario-event extraction (cut-ins, hard brakes, near-misses, ADAS disengagements), or export to RoadRunner, drivingScenario, OpenDRIVE, OpenCRG, OpenSCENARIO, or Unreal Engine. Also: log-to-scenario, scenario harvesting, accident/near-miss reconstruction, SOTIF (ISO 21448) and ISO 26262 scenario coverage, USGS-aerial-lidar scene augmentation, traffic-sign placement from camera+lidar logs. NOT for raw-data import or multi-sensor sync/crop/offset/timestamp normalization — route those to matlab-driving-data-importer.

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Build, modify, and diagram SimBiology models — API reference, helper functions, and layout patterns. Use when constructing or editing models programmatically or visually.

Fit SimBiology model parameters to data — fitproblem, population NLME, virtual patients, and NCA. Use when asked to fit, estimate, calibrate, or compute PK metrics.

Simulate SimBiology models — ODE, stochastic (SSA), scenarios, and sensitivity analysis. Use when asked to run, simulate, predict, explore what-if, or identify influential parameters.

Display images and annotations for image processing, computer vision, and visual inspection. Use when displaying images with imageshow, creating image viewers with viewer2d, adding Regions of Interest (ROI) or annotations, overlaying masks or segmentations, streaming video frames, or building apps with image display.

Display 3-D image volumes, medical image volumes, surface meshes, and annotations for 3-D image processing. Use when displaying 3-D images or isosurfaces with volshow, creating volume viewers with viewer3d, adding Regions of Interest (ROI) or annotations, overlaying masks or segmentations, streaming volumetric data, or building apps with volume display.

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Patterns for using blockedImage to process large images, harness parallel compute for image processing, and write custom adapters. Use when writing code that creates, processes, or visualizes blockedImage objects, when implementing images.blocked.Adapter subclasses, or when a user needs help with large image data. Always use this skill when working with TIFF,GeoTIFF, .svs, .ndpi, .czi or other WSI, satellite imagery or microscopy volume image formats.

Build MATLAB apps programmatically using uifigure, uigridlayout, UI components, callbacks, and uihtml for web integration. Use when creating GUIs, dashboards, interactive tools, apps with sliders/buttons/dropdowns, or embedding HTML/JavaScript components.

Create plain-text MATLAB Live Scripts (.m files) with rich text formatting, LaTeX equations, section breaks, and inline figures. Use when generating tutorials, analysis notebooks, reports, documentation, or educational content. Requires R2025a+.

Diagnose MATLAB errors and unexpected behavior. Breakpoints, workspace inspection, try-catch diagnostics, and common error patterns. Use when debugging functions, tracing errors, inspecting variables, or diagnosing runtime failures.

Deterministic workflow to download MATLAB Package Manager (mpm) and install MathWorks products from the OS command line with consistent, repeatable behavior. Use when installing MATLAB, Simulink, toolboxes, or support packages via command line, or setting up scripted installations for CI/CD, containers, or fleet provisioning.

Show all installed MATLAB products and support packages for a given MATLAB installation folder. Use when listing, checking, or verifying what products or support packages are in a MATLAB installation.

Review MATLAB code for quality, performance, maintainability, and adherence to MathWorks coding standards. Uses check_matlab_code and matlab_coding_guidelines. Use when reviewing code, checking style, finding code smells, assessing quality, or preparing code for handoff or publication.

Generate and run MATLAB unit tests using matlab.unittest and matlab.uitest. Parameterized tests, fixtures, mocking, coverage analysis, CI/CD with buildtool, app testing with gestures. Use when creating tests, writing test classes, running test suites, checking coverage, testing apps, or validating MATLAB code.

Analyze tabular data using MATLAB. Use when the task involves tables, timetables, or time-series data — including but not limited to exploring, filtering, sorting, cleaning, transforming, aggregating, smoothing, and answering questions about data. MATLAB provides extensive, easy-to-use built-in functions for these workflows with no additional products required.

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Analyze the effective toolbox file set to produce a Dependency Manifest — classify all transitive dependencies as included, product, add-on, or external-unresolved, then present resolution options with tradeoffs. Use after matlab-define-toolbox-api when the spec is approved.

Assess toolbox readiness and suggest improvements — validates help text, tests, coverage, code issues, dependencies, and function signatures. Produces a punch list and can execute fixes via delegate skills on user approval. Use before packaging or when asked to improve a toolbox.

Execute the build plan — introspect buildfile.m, run its dependency chain, and produce the .mltbx toolbox package. Mechanical execution with no human checkpoint. Works with any buildplan shape.

Generate a MATLAB buildfile.m with tasks for static analysis, testing, coverage reporting, and packaging. Use after matlab-create-project when the project structure is in place and you need repeatable build automation.

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Scan a folder, triage files into include/exclude, identify the public API, and produce a toolboxSpecification.m Interface Spec — all in one pass. Use when turning loose code into a toolbox.