3,810 件 ・ 関連度順
概要と使いどころ
Analyzes the PE Rich header and related compiler artifacts to fingerprint the build toolchain, cluster related samples, and detect inconsistencies that suggest tampering. Activates for requests to analyze a Rich header, fingerprint the compiler/linker, or cluster samples by build toolchain.
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 Rust-compiled malware by detecting the Rust toolchain signature, demangling Rust v0/legacy symbol names, and identifying crate dependencies from embedded paths. Activates for requests to analyze Rust malware, demangle Rust symbols, or identify a Rust binary build and its crates.
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 goal alignment + Pareto analysis + drift 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。
Search, suggest, add, and browse third-party audio packages. Handles "suggest packages", "add a package", "what packages are available", "search for pitch detection", and package browsing.
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。
Judge whether a change made things better with demonstrable proxy measures tied to the mechanism it targets, not wall-clock time. Load before choosing acceptance measures for any before/after or A/B claim — CI and fleet speed-ups, build speed, DSP/audio quality, render fidelity. Each proxy names its data source, detection floor and sample size, and every zero is paired with a control on the same instrument.
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。
Statistical analysis of extreme weather — EVT, return periods, thresholds, and detection/attribution of rare events.
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-number variant analysis — detection from sequencing/arrays, segmentation, and clinical interpretation.
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。
Strengthen defensive security operations — detection engineering, hardening, monitoring, and continuous defense improvement.
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。
Keep paperclip-docs in lockstep with the parent paperclipai/paperclip codebase. Detects code-surface changes (CLI, env vars, API routes, adapters, plugin SDK, schemas) and produces friendly-tutorial-voice doc updates. Two modes — `nightly` (tracks parent's default branch (currently `master`), targets the `nightly` branch, preview deploy) and `release` (on parent tag, merges nightly → main, tags, ships live). Trigger phrases: "sync docs", "update docs from paperclip", "/sync-docs", "check for paperclip changes", "is docs current".
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。
Three.js interaction - raycasting, controls, mouse/touch input, object selection. Use when handling user input, implementing click detection, adding camera controls, or creating interactive 3D experiences.
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 Lanhu HTML/CSS output into project frontend code for Vue, React, or the detected stack, with component reuse and design token mapping.
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。
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。
Brownfield onboarding — audits existing project artifacts for template format compliance (not just existence), classifies gaps by impact, and produces a numbered migration plan. Run this when joining an in-progress project or upgrading from an older template version. Distinct from /project-stage-detect (which checks what exists) — this checks whether what exists will actually work with the template's skills.
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。
Scan all GDDs against the entity registry to detect cross-document inconsistencies: same entity with different stats, same item with different values, same formula with different variables. Grep-first approach — reads registry then targets only conflicting GDD sections rather than full document reads.
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。
Validates completeness and consistency of the project architecture against all GDDs. Builds a traceability matrix mapping every GDD technical requirement to ADRs, identifies coverage gaps, detects cross-ADR conflicts, verifies engine compatibility consistency across all decisions, and produces a PASS/CONCERNS/FAIL verdict. The architecture equivalent of /design-review.
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 6 XR development guide. Use when building VR, AR, or MR experiences. Covers XR Interaction Toolkit, XR Plug-in Management, OpenXR, hand tracking, controllers, haptics, AR Foundation (plane detection, anchors, image tracking), and platform-specific setup (Meta Quest, Apple Vision Pro). Based on Unity 6.3 LTS documentation.
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 English Language and Literature Professor — backward-designing a first-year composition or literature syllabus against a shared outcomes rubric, deciding whether an AI-detection or plagiarism flag on a student essay warrants an integrity case, reading where a tenure case's monograph actually sits in a university press's pipeline, or handling a content complaint about an assigned literary text.
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 Construction and Building Inspector — deciding whether a phase of work passes a hold-point inspection (foundation, framing, rough-in, insulation, final) or gets red-tagged, writing a correction notice that will hold up on appeal, resolving a conflict between the approved plans and field conditions, applying a specific IRC/IBC section to a borderline condition (egress, smoke/CO detector placement, GFCI/AFCI locations), or deciding whether schedule pressure from a contractor changes the 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 Application Security Engineer — checking whether an endpoint verifies object-level authorization (not just authentication), running threat modeling at design time rather than deferring security review to the end, triaging a dependency vulnerability by actual reachability rather than CVSS score alone, combining SAST/DAST/SCA tooling to cover their distinct blind spots, or applying context-specific output encoding to untrusted input. Distinct from an information security analyst — this role works inside the SDLC to prevent vulnerabilities before they ship, not detect and respond to intrusions in production.
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。
Find and work with ENCODE single-cell genomics data including scRNA-seq and scATAC-seq. Use when the user asks about single-cell experiments, cell type resolution, clustering from ENCODE data, deconvolution of bulk signals using single-cell references, or comparing single-cell vs bulk profiles. Covers platform differences (10X Chromium, Smart-seq2, Drop-seq), quality limitations of single-cell data, multimodal integration (RNA+ATAC), and cross-study reproducibility concerns. Also use for cell type annotation, gene detection limits, dropout artifacts, and single-cell data structure in ENCODE.
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。