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Trigger when: (1) User wants to create an educational/explainer video, (2) User has a vague concept they want visualized, (3) User mentions "3b1b style" or "explain like 3Blue1Brown", (4) User wants to plan a Manim video or animation sequence, (5) User asks to "compose" or "plan" a math/science visualization. Transforms vague video ideas into detailed scene-by-scene plans (scenes.md). Conducts research, asks clarifying questions about audience/scope/focus, and outputs comprehensive scene specifications ready for implementation with ManimCE or ManimGL. Use this BEFORE writing any Manim code. This skill plans the video; use manimce-best-practices or manimgl-best-practices for implementation.

日本語の概要は準備中です。原文の説明を表示しています。

Translate NI-DAQmx C function signatures into correct calldaqlib MATLAB calls. Use when the user mentions calldaqlib, any DAQmx* C function name (DAQmxCfgSampClkTiming, DAQmxExportSignal, DAQmxSetStartTrigRetriggerable, DAQmxGetSampClkRate, etc.), or needs NI-DAQmx functionality not exposed by the daq object. Covers: projection rules (taskHandle implicit, output pointers become return values, buffer-size args, enum-as-string), multi-task semantics, start() clobber, device-level empty-daq workaround, dictionary returns, string getter placeholder, array getter placeholder. Trigger contexts: low-level DAQ, escape hatch, retriggerable acquisition, shared timebase, export signal, connect terminals, external sample clock, trigger configuration, NI driver, C function translation.

日本語の概要は準備中です。原文の説明を表示しています。

Use this skill when conditioning, loading, preparing, or labeling signal data for analysis or ML training. Covers: cleaning a single signal (fill gaps, remove drift, deoutlier, denoise, resample/align a time base) BEFORE analysis; building a `signalDatastore` pipeline; creating a `labeledSignalSet` for Signal Labeler; deriving labels (filename, folder, in-file, ROI, time-frequency ROI); stratified train/val/test splits; framing long signals; parallel processing; and shaping datastore output for `trainnet`. Triggers include "clean up this signal", "remove drift / detrend", "fill gaps", "remove spikes / outliers", "denoise", "resample to a uniform rate", "align channels", "labels from filenames", "stratified split", "prepare for Signal Labeler", and function names like `fillgaps`, `fillmissing`, `detrend`, `filloutliers`, `smoothdata`, `resample`, `synchronize`, `signalDatastore`, `labeledSignalSet`, `filenames2labels`, `folders2labels`, `splitlabels`, `framesig`, `framelbl`, `createDatastores`.

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Configure, simulate, debug, and analyze radarDataGenerator within radarScenario. Use for: interactively building radar detection scenarios from datasheets or performance requirements; diagnosing missed detections and configuration errors; interpreting sensor spherical, body, and scenario-frame outputs; deriving ReferenceRange from hardware specs via link budget; scan mode configuration (mechanical, electronic/AESA, hybrid); and validating simulation results against analytical predictions.

日本語の概要は準備中です。原文の説明を表示しています。

Analyze AMS waveform data using Mixed-Signal Blockset utilities: phase noise measurement, clock jitter, anti-aliased resampling, timing measurements, lock time, INL/DNL, ADC/DAC calibration, HSpice import. Use when analyzing time-domain voltage from PLL/VCO/clock simulations, measuring phase noise from variable-step solver output, computing jitter, or resampling non-uniform data.

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Interpret and explain a trained tabular machine-learning model (classification or regression) in MATLAB. Find which predictors, features, or columns matter most; explain why the model made a specific prediction, including diagnosing predictions it got wrong; show how a predictor affects the output; and compare how the model behaves across cohorts or subgroups. Uses model-agnostic techniques and model-native measures, and works on custom models (such as a dlnetwork) through a prediction function handle. Use for model interpretability, explainability, and feature-importance questions on tabular data, not for training, tuning, feature selection, deploying models, or models trained on image, text, signal, or other non-tabular data.

日本語の概要は準備中です。原文の説明を表示しています。

Identify a linear dynamic model from input-output or time-series data using MATLAB System Identification Toolbox. Use when estimating transfer function, state-space, ARX, ARMAX, BJ, OE polynomial or process models from measurement data.

日本語の概要は準備中です。原文の説明を表示しています。

AWS EC2 virtual machine management — instances, security groups, key pairs, AMIs, EBS volumes, Auto Scaling Groups, Spot Instances, Session Manager, placement groups, and instance lifecycle automation. Trigger on ANY of these, even when EC2 isn't named explicitly: - Launching or provisioning: "spin up a server", "create a VM", "new instance", "run-instances", mention of instance types (t3, m5, c5, r6, g5, p4d, t4g, c7g, etc.) - SSH / connectivity problems: "connection refused", "connection timed out", "permission denied publickey", "can't connect to my instance", "SSH not working" - Instance management: resize, stop, start, terminate, reboot, change instance type - Cost optimization: stop dev instances overnight, save money on EC2, spot vs on-demand, reserved instances - Auto Scaling: ASG, launch template, mixed instances policy, scale to zero, scheduled scaling - Spot Instances: spot fleet, spot interruption, capacity-optimized, price-capacity-optimized - AMIs and backups: create image, custom AMI, EBS snapshot, DLM lifecycle policy, copy AMI - Monitoring: EC2 CPU utilization, CloudWatch metrics for instance, instance status checks, console output - Access methods: Session Manager, EC2 Instance Connect, bastion host, port forwarding - Security: IMDSv2, instance metadata, IAM role on instance, security group rules - User data and bootstrap scripts, cloud-init

日本語の概要は準備中です。原文の説明を表示しています。

Defend AI systems against prompt injection and indirect prompt attacks using input controls, tool permissions, output validation, and isolation boundaries.

日本語の概要は準備中です。原文の説明を表示しています。

Run structured AI red team exercises for jailbreak resistance, data exfiltration risk, harmful output controls, and agent tool abuse resilience.

日本語の概要は準備中です。原文の説明を表示しています。

Harden AI/LLM deployments against prompt injection, data exfiltration, model theft, and supply chain attacks. Covers input validation, output filtering, access control, model API security, and compliance controls for production AI systems.

日本語の概要は準備中です。原文の説明を表示しています。

Secure LLM-powered applications with input validation, output controls, tenant isolation, and abuse prevention.

日本語の概要は準備中です。原文の説明を表示しています。

Evaluate 4 execution strategies (sequential, parallel-subagents, workflow, agent-team) for a phase or fan-out task. Outputs 7-signal score table, agent count math, cost guards, and strategy recommendation.

日本語の概要は準備中です。原文の説明を表示しています。

Use when exploring the ai-agent-skills catalog to find, compare, and evaluate skills before installing. Always use --fields to limit output size and --dry-run before committing to an install.

日本語の概要は準備中です。原文の説明を表示しています。

CloudBase declarative deployment from a cloudbaserc config (声明式部署, 配置式部署, cloudbaserc 部署) through the deployBuild / deployPlan / deployApply MCP tools. Use when deploying database, functions, app, hosting, or gateway resources described in cloudbaserc.json/yaml as a single desired-state config, when a user wants to build static hosting artifacts locally first (deployBuild), or wants a dry-run plan before applying (deployPlan), or when handling multi-environment deploys via mode / envOverrides. Covers build-plan-apply flow (deployBuild local build → deployPlan dry-run → deployApply confirm=true), hosting build-output neutralization, envId resolution priority, only/skip filtering, concurrency, and continueOnError. Prefer deployBuild (when hosting declares a buildCommand) and deployPlan before deployApply; do not confuse with per-resource tcb CLI deploy or single-function deploy.

日本語の概要は準備中です。原文の説明を表示しています。

Use this skill when converting Markdown to Word DOCX or DOCX back to Markdown with Pandoc, especially when the output should use the bundled Chinese Word reference templates, heading numbering variants, list indentation variants, SCI paper templates, and Lua filters for HTML tags, image captions, font color, and inline code styles.

日本語の概要は準備中です。原文の説明を表示しています。

Review a business plan from the perspective of the solo founder who has to actually execute it. Asks the brutal operational questions: Can I build this with my time budget? Can I keep it running? Will my cashflow survive? Outputs a verdict — SHIPPABLE NOW / SHIPPABLE WITH DESCOPE / NEEDS HIRE / WRONG STACK. Use when a plan looks great on paper and you want to know whether a solo operator can actually execute it. Triggered by: 'operator review', 'can I actually do this solo', 'execution reality check', '操盘视角', '我能不能干得动'.

日本語の概要は準備中です。原文の説明を表示しています。

Review a business plan from the perspective of a hostile devil's advocate. Asks: what kills this in month 6? What's everyone politely not mentioning? Outputs a verdict — EXISTENTIAL RISK / SOLVABLE RISKS / LOW-RISK / WRONG QUESTION. Use after the investor/customer/operator reviews are done, as the final gauntlet before committing real time and money. Triggered by: 'skeptic review', 'devil's advocate', 'what would kill this', 'red team this', '泼冷水', '杀手在哪'.

日本語の概要は準備中です。原文の説明を表示しています。

Weekly business retrospective. Reads all snapshots, learnings, skill-usage telemetry, and (if provided) revenue data from the past week. Outputs: what got decided, what shipped, what's stalled, skill activity, suggested next focus. Surfaces unused skills as activation prompts. Use at end of week or end of sprint. Triggered by: 'weekly retro', 'business retro', 'what did we ship', 'how's the week going', '周复盘', '业务回顾', '本周复盘'.

日本語の概要は準備中です。原文の説明を表示しています。

Review a business plan or product strategy through the eyes of a smart-money seed/Series-A investor. Asks the brutal questions: Is this fundable? What's the moat? What's the exit story? Outputs a verdict with one of four modes — SEED VIABLE / LATER ROUND ONLY / BOOTSTRAP-ONLY / UNFUNDABLE. Use when the user has a business plan and wants to know if it would survive an investor pitch. Triggered by: 'investor review', 'is this fundable', 'VC perspective', '投资人视角'.

日本語の概要は準備中です。原文の説明を表示しています。

Review a business plan from the perspective of the named target customer. Asks: would they actually pay this price for this solution today? Outputs a verdict with one of four modes — PAY NOW / PAY WITH FRICTION / WOULDN'T PAY / WRONG ICP. Use when the user has a defined ICP and pricing and wants to stress-test whether the value prop survives contact with reality. Triggered by: 'customer review', 'would they pay', 'pricing reality check', '客户视角', '会付费吗'.

日本語の概要は準備中です。原文の説明を表示しています。

Define, validate, run, resume, and audit repo-local multi-step automations with current `asc workflow` and `.asc/workflow.json`, including step outputs and safe release/TestFlight workflows.

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Guidance for using asc cli in this repo (flags, output formats, pagination, auth, and discovery). Use when asked to run or design asc commands or interact with App Store Connect via the CLI.

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Discover when you are most active and most productive with Claude Code by bucketing sessions and events into hour-of-day and day-of-week bins from their timestamps, then flagging peak versus low-output windows. Uses the session list, per-session events, and analytics daily trends. Use when planning a schedule or deciding when to do deep work versus lighter tasks.

日本語の概要は準備中です。原文の説明を表示しています。