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MMKV high-performance key-value storage for React Native - synchronous JSI-based reads/writes, encryption, typed hooks, multiple instances, listeners, persistence middleware adapters

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 whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.

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。

Tailor a resume to a job description with ATS keyword optimization, gap analysis, and rewritten bullets — zero fabrication. Use when the user wants /tailor for a single role, /batch for multiple JDs, career pivot reframing, or interview prep questions from a tailored application.

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。

Adds professional packaging and motion graphics to videos. Use when the user asks to add intros, outros, subtitles, transitions, watermarks, or lower thirds to a video. Supports multiple styles and custom options, no API key required.

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。

Multi-agent management workflow — task delegation, progress monitoring, quality verification with regression testing, feedback delivery, and cross-review orchestration. Use this skill when coordinating multiple agents on a shared task, monitoring delegated work, ensuring quality across agent outputs, or implementing a multi-phase plan (3+ phases or 10+ file changes).

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。

Enriches indicators with context from threat-intel sources: planning lookups against reputation, passive DNS, WHOIS, and sandbox/sample databases, scoring confidence, and avoiding operational-security mistakes that tip off adversaries. Activates for requests to enrich IOCs, add threat context, or plan indicator lookups safely.

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。

Pivots on indicators of compromise across multiple data sources by correlating a seed set of IOCs against logs to find co-occurring indicators, hosts, and timeframes that expand the investigation. Activates for requests to pivot on IOCs, correlate indicators across data sources, or expand an investigation from a seed indicator set.

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。

Morning briefing - curated task list from multiple sources

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。

Build charts with Chart.js: configuration, plugins, custom tooltips, real-time updates, and dashboard patterns. Use for standard charts in web apps.

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。

Manage fleets of local AI agents: lifecycle, monitoring, task assignment, and resource control. Use when running multiple CLI agents via an orchestrator like AI Maestro.

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。

Manage fleets of local AI agents — spawn, supervise, route tasks, monitor progress, and shut down workers cleanly. Use when coordinating multiple agents on parallel workstreams.

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。

Complete B2B SaaS marketing agency powered by 90 AI agents across 17 specialties. This is the entry point for ALL marketing requests. Routes to the right specialist team: content marketing, SEO, paid media, social media, email marketing, design, sales enablement (including technical presales — demos, proofs of concept, security questionnaires), product marketing, account-based marketing (ABM), partnerships and channel programs (co-marketing, marketplace GTM, channel conflict and deal registration, partner economics), growth, analytics, project management, or client operations. Use this skill when the request spans multiple disciplines or when unsure which specialist to invoke. Also triggers on: marketing help, marketing team, marketing agency, what can you do, marketing capabilities.

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。

Diagnose and fix slow pyfixest regression GRIDS (many feols/fepois calls run sequentially) that stay slow despite demeaner_backend="cupy64" and an idle GPU. Use when: (1) a script looping dozens of pf.feols models on a 100k+ row panel takes ~1 min/model, (2) process inspection shows ~1-1.5 cores busy and nvidia-smi shows ~0% GPU utilization with a resident cupy context, (3) planning any worker prompt that will run a model grid (robustness variants x FE structures x domains). Root cause: per-model CPU-side single-threaded fixed costs (formulaic model-matrix build, interaction construction, singleton detection, cluster vcov) dominate wall time; GPU demeaning is a small slice. Fix: shard the model grid across OS processes and/or use pyfixest multiple-estimation syntax; mandate this IN THE WORKER PROMPT.

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。

Build throwaway code to validate an uncertain approach before committing to a plan. Two branches — Logic (terminal state machine) or UI (multiple designs on one route with URL-param switcher). No persistence, no tests, single-command start. Use between analyze and plan when the approach itself is uncertain.

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。

Detect non-deterministic (flaky) tests by reading CI run logs or test result history. Aggregates pass rates per test, identifies intermittent failures, recommends quarantine or fix, and maintains a flaky test registry. Best run during Polish phase or after multiple CI runs.

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。

Audit the game for security vulnerabilities: save tampering, cheat vectors, network exploits, data exposure, and input validation gaps. Produces a prioritised security report with remediation guidance. Run before any public release or multiplayer launch.

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。

Unity New Input System correctness patterns. Catches common mistakes with action reading (triggered vs IsPressed vs WasPressedThisFrame), action map switching, rebinding persistence, InputValue lifetime, PassThrough vs Value, local multiplayer device assignment, and control scheme auto-switching. PATTERN format: WHEN/WRONG/RIGHT/GOTCHA. Based on Unity 6.3 LTS.

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 selecting or composing behavior-preserving refactoring techniques after structural evidence is known, especially when multiple candidate transformations, preconditions, or ordering choices must be compared.

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。

Apply source-aware mechanical-engineering judgment to research, analysis, coding, writing, teaching, research identity, and release work. Use for thermal-fluid systems, heat transfer, fluid mechanics, thermodynamics, HVAC, energy systems, turbomachinery, piping, multiphase flow, experiments, correlations, CFD, reduced-order models, AI/ML, uncertainty, engineering datasets, literature reviews, citations, manuscripts, reviewer revisions, Overleaf packages, figures, proposals, research software, reproducibility, public releases, engineering teaching materials, or a research-project logo and visual identity.

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 a task needs the judgment of an Elementary School Teacher — interpreting a running record or fluency check against grade-level benchmarks, deciding whether a struggling reader needs a Tier 2 intervention, running a self-contained classroom's daily schedule across multiple subjects, implementing (not writing) an IEP/504 accommodation with fidelity, or writing a parent-conference summary grounded in assessment 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。

Use when a task needs the judgment of a dental assistant — sequencing instrument transfer during a procedure, troubleshooting a retake-worthy radiograph before re-exposing the patient, tracing a failed sterilization-cycle indicator back to the affected instrument batches, or timing a chairside material mix against its working-time window across multiple prepped teeth.

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 a task needs the judgment of a Commercial and Industrial Electronics Repairer — diagnosing a VFD fault trip on a production line, tracing a PLC I/O fault back to the sensor, the wiring, or the ladder logic, selecting arc-flash PPE for live-panel diagnostic work, triaging multiple simultaneous down-lines by downtime-cost-per-hour, or deciding drive/board swap versus component-level repair.

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 a task needs the judgment of a Computer Numerically Controlled (CNC) Tool Programmer — calculating stepover from a scallop-height/surface-finish spec instead of picking one by feel, deciding whether a toolpath needs a dry run before cutting material on an unfamiliar post-processor/machine pairing, verifying datum consistency across multiple part setups, or diagnosing why cycle time jumped after a "minor" parameter change.

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 a task needs the judgment of a billing and posting clerk — generating an invoice against a rate/contract schedule, applying an incoming payment across multiple open invoices, processing a credit memo or adjustment, or investigating an aging-report discrepancy.

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。