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概要と使いどころ
Use for Interpreter app settings and account usage. Prefer the direct workstation tools for reading settings, changing settings, and checking remaining Interpreter balance; use the Interpreter CLI only as a fallback.
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。
Section-by-section media planning and generation. Image generation (GPT Image 2 primary, built-in fallback), logo/icon generation (Ideogram v3 → favicon set), video generation (Veo 3.1), social preview images (OG 1200x630 + AI search optimization), stock photo curation (Pexels, Pixabay), critique/remix loops (max 3 rounds), asset compression pipeline, and media performance budgets.
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。
Error boundary patterns, fallback UI, reset/retry, react-error-boundary library, React 19 createRoot error hooks
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。
LiteLLM proxy server setup, TypeScript client patterns via OpenAI SDK, model routing, fallbacks, load balancing, spend tracking, virtual keys, and production deployment
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。
Tired of juggling 8 API keys? This skill gives you one-command access to Midjourney, Flux, Ideogram, and more, with zero setup. Use when you want to generate any image without worrying about API keys.
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。
Tired of juggling multiple audio APIs? This skill gives you one-command access to TTS, music generation, sound effects, and voice cloning. Use when you want to generate any audio without managing multiple API keys.
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 hunting Pwn Request vulnerabilities where pull_request_target workflows checkout attacker-controlled PR code and execute it in a privileged context with access to repository secrets. Trigger on: "pwn request", "pull_request_target", "checkout PR head", "npm install in CI", "lifecycle scripts in CI", "preinstall script", "postinstall script", "package.json scripts CI", "npm ci ignore-scripts false", "actions/checkout ref pull request head sha", privileged workflow running PR code, "Gato-X", supply chain via PR lifecycle scripts.
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 authentication and biometric bypass vulnerabilities in mobile apps (Android/iOS). Trigger on: BiometricPrompt, LocalAuthentication, LAContext, evaluatePolicy, CryptoObject, Android Keystore, Secure Enclave, kSecAccessControlBiometryCurrentSet, userAuthenticationValidityDurationSeconds, confirmCredentials, biometric fallback, PIN bypass, passive authentication, enrolled biometrics detection, Frida hook auth, jailbreak bypass, TouchID, FaceID, fingerprint. Covers MASVS-AUTH-1/2/3.
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。
Exploit an application's absolute trust in its underlying LLM (Overreliance). Use this skill to induce critical "hallucinations" (confident falsehoods) that cause downstream logical systems or automated agents tracking the LLM's output to make destructive actions or grant unauthorized access.
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 production-grade Claude Code CLI skills with fallback architecture, TypeScript implementation, and creativity directives. Based on Critical Thinking Bug Bounty Podcast Episode 166 — "Building Claude Skills as a Bug Bounty Hunter."
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。
Prevent common AI report pitfalls — bug blending, inflated threat models, and generic language. Train Claude with your best past reports for concise, technical submissions. Based on Critical Thinking Bug Bounty Podcast Episode 166.
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 bounded, read-only semantic advisory sidecar passes for candidate mapping, issue triage, upstream diffs, evidence verification triage, and meta audit hints. Enforces monthly spend limits with explicit fallback to worker-luna.
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。
Finding exoplanets — transit and radial-velocity methods, light-curve vetting, and false-positive rejection.
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。
Burn timed, styled captions into a finished video on the local machine, without re-rendering the source. This is the manual fallback to the API's captioning endpoint, POST /v1/captions, which stays the default path: come here when the style the user wants is outside the API's presets, when the wording has to be hand-corrected before it is burned into the pixels, or when the source was rendered with audio disabled and the API refuses it. Transcribes the audio with Whisper, groups words into reading phrases, renders the captions in a real browser via HyperFrames, and composites them over the source with ffmpeg using a real alpha channel. Use when the user has an MP4 with speech and asks to add captions, subtitles, burned-in text, TikTok-style captions or word-by-word text to it, and the API path has been ruled out. Runs out of band, so it costs no credits and works on any video file whoever generated it. Not for generating video (use the video skills) and not for a separate .srt/.vtt sidecar file.
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。
Uses YARA to classify and triage samples at scale: applying rule sets, reading matches and string offsets, tuning for false positives, and organizing rules for malware family identification. Activates for requests to scan files with YARA, apply YARA rules, or classify samples by signature.
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。
Turns reverse-engineering findings into durable YARA detections: selecting stable code constructs and constants over volatile strings, extracting opcode/byte patterns with wildcards, and validating rules for low false positives. Activates for requests to write a YARA rule from reversed code, create a detection signature, or convert RE findings into hunting 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。
Writes and validates Suricata/Snort IDS rules for network-based malware detection, covering rule anatomy, content/pcre matching, flow and threshold options, and common mistakes that cause false positives or no matches. Activates for requests to write a Suricata rule, create Snort signatures, or build network detection rules from IOCs or C2 patterns.
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。
Pulp in the browser — the WAM v2 and WebCLAP adapters, the wasm runtime, the Skia/WebGL2 browser window host, and the WebGPU (emdawnwebgpu) GPU-audio lane. Covers what does and does not compile to wasm, the worklet-thread constraints (no std::thread, no fetch, no navigator.gpu), the non-realtime tick, memory growth, how to PROVE a GPU lane actually ran rather than silently falling back to CPU, and the silent-failure traps that make a wasm build "work" while producing no audio or no pixels.
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。
VST3 format adapter for Pulp — SingleComponentEffect wiring, bus arrangement negotiation, parameter / MIDI event routing, state round-trip, and the pitfalls discovered while wiring the adapter against Steinberg's SDK.
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。
Enable a Skia + Dawn GPU build of Pulp (MacGpuWindowHost, Skia Graphite). Covers the prebuilt skia-builder binaries, the headers-only fresh-worktree trap, reusing another checkout's cached libs via SKIA_DIR, FindSkia layouts, verifying PULP_HAS_SKIA / MacGpuWindowHost, and the raster-fallback + GPU-wedge gotchas. Use whenever GPU rendering "doesn't work" or a build silently came up CPU-only.
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。
Audio Unit v3 (AUAudioUnit) format adapter for Pulp — render-block wiring, parameter tree bridging, MIDI / sysex via AURenderEvent, sidechain pulls, state persistence, iOS extension surface, and the pitfalls discovered while wiring the adapter.
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。
CLAP format adapter for Pulp — how Processor bridges to clap_plugin_t, how parameters / modulation / sidechain / MPE / UMP / sysex flow, and the pitfalls discovered while wiring the adapter.
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。
Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why, AI policy for the marketing team, AI governance, AI acceptable use policy, which AI tools are we allowed to use, can I paste customer data into ChatGPT, shadow AI, AI use register, human review of AI output, when can we stop reviewing every AI draft, can the agent send on its own, the AI made a mistake now what, pull back AI autonomy, AI evals, our AI vendor changed the model, AI output went out wrong, prompt injection, can our AI read prospect replies safely, lethal trifecta, the AI leaked internal notes, turn off auto-generated ad assets, auto-applied recommendations, AI literacy training, EU AI Act Article 4, duplicate records in the CRM, dedupe before import, which value wins in a merge, CRM cleanup.
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。
pyfixest demeaner_backend="cupy64" (including its CPU fallback when cupy is absent) is NOT numerically identical to the default numba backend and does NOT drop fully-absorbed/collinear regressors the same way. Use when: (1) adding demeaner_backend="cupy64" to existing pf.feols/fepois calls changes the printed coefficient table, (2) a regression report suddenly gains rows with absurd estimates (e.g. coef 435.8, SE 7106) for controls absorbed by the fixed effects, (3) diffing outputs before/after a backend change, or (4) anything parses a pyfixest text report by line position.
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。