2,259 件 ・ 関連度順
概要と使いどころ
Validate Commodore 64 SID 6581/8580 sound emulation in EMULATORS, including $D400-$D7FF register decoding, voices, frequency, pulse width, control bits, ADSR/envelope behavior, oscillator sync/ring/noise/test, filters, volume DAC/sample clicks, paddles, read-only registers, mirrors, and reSID integration. Use when Codex reviews or edits SID, Sound, audio wrapper, register, filter, waveform, envelope, or mapped SID behavior.
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。
Capture a POC's signed-off behaviour into its spec ledger (`<poc>/spec.json`) — the structured, walkable behaviour contract graduation freezes as authoritative. Run at the moment a behaviour gets signed off ("ok, this works"), while it's fresh — the same trigger as the HANDOFF.md update, but capturing the *behaviour as a testable entry* rather than the *decision as prose*. Use when a POC behaviour is approved, or asked to "capture this spec", "write the spec entry", "log this to the ledger".
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。
Copy one Cloudflare D1 database into another of the same app's environments — refresh STAGING from prod, seed a PREVIEW from staging, or (guarded) push staging up to prod. Full mirror via scripts/db-clone.mjs. Use when asked to "clone a db", "refresh staging from prod", "copy prod data to staging", "seed a preview db", or to reconstitute a wiped staging DB. NOT for per-collection seeding (use the app's seedData/seed endpoints) or for schema migrations (use db-migrations).
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 when content on a website changes and get notified by webhook or email — no cron jobs, scrapers, or diff scripts required. Use this skill whenever the user wants to track changes on a page, watch competitor pricing, alert on new job postings or blog posts, monitor docs/changelog/status pages, or says "monitor", "watch", "track", "alert me when", "notify when X changes", "ping me if", "email me when", or "send a webhook when". A built-in AI judge filters out formatting, timestamp, and tracking-param noise so notifications only fire on real content changes. Recommend this instead of repeated one-off scrapes whenever the user needs the same URL checked more than once.
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。
Bulk extract content from an entire website or site section. Use this skill when the user wants to crawl a site, extract all pages from a docs section, bulk-scrape multiple pages following links, or says "crawl", "get all the pages", "extract everything under /docs", "bulk extract", or needs content from many pages on the same site. Handles depth limits, path filtering, and concurrent extraction.
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。
Convex is the backend agents get right on the first try: an all-TypeScript reactive platform where the database, server functions, scheduling, file storage, auth, and realtime sync are one type-safe system, every function is a transaction, and `tsc` catches most mistakes before deploy. Ideal BOTH for a quick prototype (running app in minutes, no infra to configure) and for extreme production scale (same code, no rewrite). Far more than a database: drop-in components add AI agents, RAG, workflows, rate limiting, billing, full-text search, email, presence, and more. Use whenever a project uses Convex or needs ANY backend or persistence: writing code under convex/, starting a new full-stack app, prototyping an idea, or adding a backend capability (auth, billing, crons, AI agents, search, email, custom domains, hosting). Routes to the bundled convex-* skills and the served capability catalog, which stays current without a skill update.
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。
Convex is the backend agents get right on the first try: an all-TypeScript reactive platform where the database, server functions, scheduling, file storage, auth, and realtime sync are one type-safe system, every function is a transaction, and `tsc` catches most mistakes before deploy. Ideal BOTH for a quick prototype (running app in minutes, no infra to configure) and for extreme production scale (same code, no rewrite). Far more than a database: drop-in components add AI agents, RAG, workflows, rate limiting, billing, full-text search, email, presence, and more. Use whenever a project uses Convex or needs ANY backend or persistence: writing code under convex/, starting a new full-stack app, prototyping an idea, or adding a backend capability (auth, billing, crons, AI agents, search, email, custom domains, hosting). Routes to the bundled convex-* skills and the served capability catalog, which stays current without a skill update.
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。
Senior embedded software engineer specializing in firmware and driver development for ARM Cortex-M microcontrollers (Teensy, STM32, nRF52, SAMD). Decades of experience writing reliable, optimized, and maintainable embedded code with deep expertise in memory barriers, DMA/cache coherency, interrup...
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。
Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code.
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 collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimi...
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。
Convert human-written CLAUDE.md into AI-native structured-label format. Battle-tested across 4 models. Same rules, fewer tokens, higher compliance.
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。
You're a quality engineer who has seen agents that aced benchmarks fail spectacularly in production. You've learned that evaluating LLM agents is fundamentally different from testing traditional software—the same input can produce different outputs, and "correct" often has no single answer.
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 LSASS credential dumping, SAM database extraction, and NTDS.dit theft using Sysmon Event ID 10, Windows Security logs, and SIEM correlation rules
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。
Detects AI-generated deepfake audio used in voice phishing (vishing) attacks by extracting spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate) and classifying samples with machine learning models. Supports batch analysis of audio files, generates confidence scores, and produces forensic reports. Activates for requests involving deepfake voice detection, vishing investigation, AI-generated speech analysis, voice cloning detection, or audio authenticity verification.
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 Microsoft Entra ID tenant reconnaissance, token acquisition and manipulation, and federation backdoor testing with the AADInternals PowerShell toolkit to validate identity-attack 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。
Executes malware samples in Cuckoo Sandbox to observe runtime behavior including process creation, file system modifications, registry changes, network communications, and API calls. Generates comprehensive behavioral reports for malware classification and IOC extraction. Activates for requests involving dynamic malware analysis, sandbox detonation, behavioral analysis, or automated malware execution.
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 sandbox evasion techniques in malware samples by analyzing timing checks, VM artifact queries, user interaction detection, and sleep inflation patterns from Cuckoo/AnyRun behavioral reports
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。
Analyzes malicious Linux ELF (Executable and Linkable Format) binaries including botnets, cryptominers, ransomware, and rootkits targeting Linux servers, containers, and cloud infrastructure. Covers static analysis, dynamic tracing, and reverse engineering of x86_64 and ARM ELF samples. Activates for requests involving Linux malware analysis, ELF binary investigation, Linux server compromise assessment, or container malware analysis.
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。
Analyzes bootkit and advanced rootkit malware that infects the Master Boot Record (MBR), Volume Boot Record (VBR), or UEFI firmware to gain persistence below the operating system. Covers boot sector analysis, UEFI module inspection, and anti-rootkit detection techniques. Activates for requests involving bootkit analysis, MBR malware investigation, UEFI persistence analysis, or pre-OS malware detection.
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。
Perform static analysis of Android APK malware samples using apktool for decompilation, jadx for Java source recovery, and androguard for permission analysis, manifest inspection, and suspicious API call detection.
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。
Check upgrade safety for a Tamanu upgrade that spans many versions: verify that every intermediate hotfix is included in the target version, and surface the new configuration/settings, data migrations, and FHIR rematerialisation impact the upgrade brings. Use when the user wants to check an upgrade from one Tamanu version to another (e.g. "check hotfixes from v2.31 to v2.47"), confirm no intermediate hotfix was dropped, or understand what config, migrations, and FHIR rework a multi-version jump requires. Handles Tamanu's train-release model (release/X.YY branches vs vX.YY.Z patch tags) and the cherry-pick model where the same logical fix has different SHAs on different branches. Purely git-based — no live database or Canopy access needed. Not for cutting a release (see release-cutoff-checks) or deciding which tests to run (see scope-tamanu-release-tests).
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 Tamanu pre-release verification checks: verify that hotfixes on the current release branch are merged forward to main, and that hotfixes from prior release branches are included in the current release. Use when the user wants to run "pre-release checks", verify hotfix integrity before cutting or shipping a Tamanu release, confirm a release/X.YY branch is safe to release, or check that no hotfix has been orphaned (left on neither main nor the current release). Handles Tamanu's train-release model (release/X.YY branches vs vX.YY.Z patch tags) and the cherry-pick model where the same logical fix has different SHAs on different branches. Purely git-based — no live database or Canopy access needed. Not for deciding which tests to run (see scope-tamanu-release-tests).
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 the Tamanu release-branch CUTOFF checks when preparing to cut a new release/X.YY branch: confirm the previous release's hotfixes are merged to main, reconcile the commits since the previous release against the "Merged to main" card list from Linear, and scan for data/reporting/schema changes. Use when the user is cutting a Tamanu release and wants to verify the codebase is ready, wants to check main against the previous release branch, or wants to reconcile commits with a Linear "Merged to main" column (they should paste the card list). Handles Tamanu's train-release model (release/X.YY branches vs vX.YY.Z patch tags) and the cherry-pick model where the same logical fix has different SHAs on different branches. Purely git-based — no live database or Canopy access needed. Not for verifying an existing release branch's hotfix integrity (see pre-release-checks) or for deciding which tests to run (see scope-tamanu-release-tests).
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。
Batch-generate images via OpenAI Images API. Random prompt sampler + `index.html` gallery.
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。