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概要と使いどころ
Uses symbolic execution (angr) to solve constraint-based malware problems — finding inputs that reach a target branch, recovering keys/passwords from a check routine, and brute-forcing opaque predicates — by generating a ready-to-edit angr harness. Activates for requests to use symbolic execution, solve a malware unlock check with angr, or recover an input that reaches a code path.
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。
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。
Use Chanjing text-to-digital-person APIs for AI portraits, talking videos, optional LoRA training, polling, and explicit downloads when requested.
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。
Before designing, training, or auditing ANY model that replicates human-annotated labels, audit the annotation protocol's INPUT — the exact document/evidence the human labelers consulted — and give the model that same input. Use when: (1) designing a classifier/LLM extractor whose target is a hand-coded label set, (2) a label-replication model shows low recall concentrated in a label subset and the diagnosis on offer is "the label's information is not in the features", (3) reviewers propose construct splits (e.g. "designation vs record-evident"), adjudication sittings, or per-domain stop rules to explain residual disagreement with gold, (4) validating an extraction pipeline against labels transcribed from a source document. Symptom of the underlying failure: elaborate theory accumulates to explain why gold is "partially unpredictable" when the model was simply never shown the document the annotators read.
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。
Configure the project's game engine and version. Pins the engine in CLAUDE.md, detects knowledge gaps, and populates engine reference docs via WebSearch when the version is beyond the LLM's training 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。
Unity procedural generation design-to-code translation. Noise-based terrain/placement, tile & grid systems, dungeon/room generation, seed & reproducibility, content budget & constraint systems, runtime vs baked generation. DESIGN INTENT format: INTENT/WRONG/RIGHT/SCAFFOLD/DESIGN HOOK. 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 a task needs the judgment of an Electronics Engineer working on RF, analog, and mixed-signal hardware — closing an RF link budget for a wireless telemetry radio, sizing an op-amp or instrumentation-amplifier front end against a gain-bandwidth constraint, designing an anti-alias or IF filter to a cutoff and Q, pre-checking a design against FCC/IEC EMC radiated-emissions limits before formal compliance testing, or picking between a superheterodyne and direct-conversion receiver architecture.
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 education administrator in a non-traditional setting — adult/continuing education, vocational or trade school, corporate/organizational training department, or other education programs outside standard K-12 or degree-granting postsecondary institutions. Covers the O*NET "Education Administrators, All Other" catch-all.
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/Industrial Designer — designing the form, ergonomics, and manufacturability of a physical product, choosing between manufacturing processes at a given volume, selecting materials/finishes under a cost or durability constraint, or planning a prototyping sequence before tooling commitment.
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 cement mason / concrete finisher — timing float and trowel work against bleed water and set, laying out and cutting control joints on a slab, choosing air-entrainment and mix parameters for freeze-thaw exposure, adjusting a finishing schedule for hot- or cold-weather concreting, or diagnosing a slab defect (blistering, scaling, random cracking) after the fact.
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 bicycle repairer — diagnosing drivetrain skip and quoting chain-vs-cassette replacement, torquing carbon components to spec, bleeding hydraulic disc brakes, truing a wheel to tension (not just by eye), or deciding whether a safety-critical fastener issue needs to be declined-in-writing rather than patched.
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 bailiff or court officer — deciding whether an in-custody defendant appears in visible or concealed restraints, running courtroom entry screening and staffing it for expected volume, handling a disruptive defendant or spectator under the judge's authority, moving a jury note between jury room and bench without an ex parte violation, or planning custody/security for a jury sequestration.
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 Biomass Power Plant Manager — running a biomass-fired power generation facility, managing fuel supply logistics and quality variability, evaluating combustion/efficiency problems, or balancing generation output against fuel cost and emissions constraints.
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 Aircraft Cargo Handling Supervisor — verifying a built ULD against its type's certified max gross weight before it's spotted for loading, running a hazmat item through the segregation table against its actual compartment neighbors, reconciling an actual cargo build against the dispatcher's load plan and deciding whether it clears a last-minute-change threshold or needs a reissued loadsheet, calling a ULD's lock/net restraint good or rejecting it, or running ramp FOD and ground-equipment clearance discipline around an aircraft during turn.
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 Agricultural Equipment Operator — calibrating planter seed population and singulation, diagnosing combine header or separator loss, sequencing planting or harvest across fields under a weather-constrained window, sizing machinery capacity in acres/hour against a season's acreage, or evaluating PTO/grain-bin entrapment safety on a job site.
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 Animal Control Worker — responding to a dog-bite or animal-attack call, determining rabies-exposure quarantine protocol, evaluating a dangerous-dog classification referral, planning a cruelty/neglect investigation and removal, or selecting a capture/restraint method for a stray or wildlife-in-structure call.
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 Aerospace Engineering and Operations Technologist/Technician — verifying an installed fastener's preload against a drawing's torque callout via the T=K·D·F relationship, reducing strain-gauge data from a structural proof-load test into stress and checking it against an FEA-predicted margin, selecting and executing an NDI method against a defect type and material, reducing a wind-tunnel test point's dynamic-pressure and Reynolds-number data, or ballooning and dispositioning an AS9102 first article inspection. Distinct from an aerospace-engineer (owns the design, structural analysis, and certification basis) — this role builds, instruments, tests, and inspects the hardware the engineer specified, closing the loop with measurement rather than deriving a new 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。
Use when a task needs the judgment of an Amusement and Recreation Attendant — running ride dispatch and restraint checks, calling a weather or mechanical hold, managing queue throughput and wait-time complaints, operating a game booth or coin-operated attraction, or handling an on-ride evacuation.
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 Architecture Teacher, Postsecondary — running studio desk crits and final jury reviews, grading design work on process and constraint-response rather than rendering polish, separating a personal design-style opinion from an objective functional or code flaw during critique, and checking whether a studio brief's precedent set is stale relative to current construction and code practice.
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 archivist — appraising whether a donated or transferred collection has enduring value, deciding a processing level for a backlog, writing a finding aid, triaging preservation under budget constraints, or resolving a records-restriction question.
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 when the user wants to generate, normalize, verify, deduplicate, or export training datasets for Codex, Antigravity, or Claude Code from topics, URLs, reference material, web research, or existing JSONL/CSV files. Supports SFT and DPO workflows, custom export schemas, and deterministic local pipeline 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。
Query and answer questions about Volcengine API specifications. Use when the user asks about API parameters, error codes, request methods, enum values, required fields, response structures, pagination, parameter dependencies, service capability lists, API availability, or API comparisons, even if they do not explicitly say "API". Typical intent includes checking what an action supports, which fields are required, what values a parameter accepts, why an API returns a specific error, how to pass nested or body parameters, how pagination works, what an API response contains, whether a batch operation exists, what services or versions expose an operation, or how two APIs differ. Use API Explorer data as the authoritative source and preserve constraints, examples, and caveats found in the spec. Answer in Chinese or English. When the user needs runnable SDK code, language-specific examples, or SDK configuration, hand off to volcengine-sdk-generator. When they need CLI operations, hand off to volcengine-cli.
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。
Language-agnostic writing style guidelines covering structure, tone, rhetorical constraints, banned phrases, and review checks. Use when writing any kind of content in any language. Including blog posts, notes, technical articles, technical writing, chitchat, social media posts, etc., even when you are just sending a text message. Also use when reviewing or editing existing content for tone and style compliance. For Traditional Chinese (zh-TW) content, apply `chinese-content-writing-guideline` alongside 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。