「tia」の検索結果

Use when building public-equity-investing initiating coverage reports. Do not use for trade pitches, memos, earnings notes, models, or tearsheets.

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Run or plan bulk RNA-seq differential-expression analysis from count matrices with replicate, design formula, contrast, batch, normalization, QC plot, and result-table checks.

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。

This skill installs mixpanel_headless, pandas, numpy, matplotlib, seaborn, networkx, anytree, scipy (and pyarrow on Python 3.11+), then verifies Mixpanel credentials. It should be invoked when setting up a new environment for Mixpanel data analysis, when dependencies are missing, or when configuring service account or OAuth credentials for the first time.

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。

Builds a numbered, categorized concept catalogue from the talk summary and timeline, scoring each concept HIGH / MEDIUM / LOW for talk potential with optional repo enrichment. Use when you need a structured inventory of concepts before choosing a talk angle, or when assessing which ideas have the strongest presentation potential.

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。

Guides a user through collecting the credentials needed to deploy their own copy of Open Harness, deploying this repo on Vercel, and completing first-run setup. Use for requests about deploying, self-hosting, configuring credentials, or getting started with a fork of this app.

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Post-access cloud exploitation for AWS, GCP, and Azure — what to do AFTER you obtain credentials or reach a metadata endpoint. Covers IAM enumeration and privilege escalation (iam:PassRole, CreatePolicyVersion, AssumeRole chains, GCP service-account impersonation, Azure managed-identity abuse), IMDSv1/v2 metadata credential theft, STS token abuse, S3/GCS/Blob bucket takeover and object exfil, Lambda/Cloud Functions env-var secrets, cross-account and cross-service pivoting, and turning leaked keys into demonstrated impact (read PII, assume admin, exfil data). Assumes authorized scope only. Use when you have AWS/GCP/Azure keys, an SSRF hitting 169.254.169.254 / metadata.google.internal, a leaked service-account JSON, or a token, and need to escalate and prove impact. For finding buckets/origins first, use cloud-recon; for the SSRF entry point, see web2-vuln-classes. 中文触发词：云渗透、AWS提权、IAM枚举、元数据凭证、云安全审计

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。

Password spray methodology for bug bounty — when to do it vs web-vuln hunting, the wordlist-gen + breach-check + osint-employees + spray pipeline, mode selection (http-form / oauth / o365 / okta), rate-limit + lockout tactics, BBP legal guardrails, success detection, and the spray → authenticated /hunt chain pattern. Use when assessing whether credential attack is worth running on a target, picking the right mode, or recovering from common pitfalls.

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。

Microsoft 365 / Entra ID red-team attack chain — current 2026 reality. AADSTS code reference, user enumeration vectors (with hardening status), Smart Lockout math, Conditional Access bypass options, ROPC + SAML SSO browser flow, Burp/Playwright templates. Built from authorized red-team work where ROPC spray surfaced pre-existing lockouts and CA-blocked credentials, plus real-time external attacker activity correlation. Use for any M365/Entra credential attack, password spray, user enumeration, CA-bypass exploration, or active-attacker-detection scenario.

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。

Hunt CORS Misconfiguration — origin-reflection with credentials, null-origin trust, subdomain-regex bypass (unanchored vs unescaped-dot vs prefix-only), pre-flight (OPTIONS) gating bypass, postMessage origin checks. High only when an attacker-controlled origin can perform a CREDENTIALED cross-origin read of sensitive data and you have proven it in a browser. Use when testing API endpoints, SPAs, or any app emitting Access-Control-* headers.

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Hunt NTLM/Negotiate information disclosure on internet-reachable IIS/SharePoint/Exchange. Anonymous NTLM Type-2 challenge capture leaks NetBIOS domain, internal DNS forest, computer name, AD timestamp via AV_PAIRS structure. Default Windows-installer hostnames (WIN-XXXXXXXXXXX pattern) signal lazy provisioning. Use when target advertises `WWW-Authenticate: NTLM` or `Negotiate` headers anonymously.

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。

Hunt cloud / infrastructure misconfigurations. AWS: public S3 buckets (s3:GetObject anonymous), permissive bucket policies (PutObjectAcl public-write), exposed CloudFront origin, public Lambda function URL, public RDS snapshot, IAM credentials in JS bundles, AWS metadata accessible via SSRF. GCP: public GCS buckets, exposed Cloud Run services, leaked service account JSON. Azure: public blob containers, exposed Function App. (Kubernetes/Docker exposure is owned by hunt-k8s; CI/CD pipeline attacks by hunt-cicd; post-credential IAM escalation by cloud-iam-deep.) Detection: targeted dorking, certificate transparency, JS bundle secret extraction, port scan for known service ports. Validate: actual data read / write / RCE. Use when hunting cloud-native storage and compute misconfig (S3/GCS/Blob, IMDS-via-SSRF, serverless, public managed services).

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External SSL VPN / remote-access appliance attack matrix — Cisco ASA/AnyConnect, Fortinet FortiGate/FortiOS, Citrix NetScaler/ADC, Palo Alto GlobalProtect, Pulse Secure / Ivanti Connect Secure, SonicWall, F5 Big-IP. Covers version fingerprinting, CVE matrix (2018-2026), AAA backend identification, default credentials, configuration-disclosure paths, pre-auth RCE/SSRF/path-traversal exploits where applicable. Built from authorized-engagement Cisco ASA testing plus 2024-2026 enterprise VPN CVE landscape. Use whenever the target's perimeter exposes any SSL VPN appliance or remote-access gateway — these are the most common initial-access points in 2024-2026 actor TTPs.

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VS-Enhanced Research Question Refiner - Prevents Mode Collapse and derives differentiated research questions Enhanced VS 3-Phase process: Modal question avoidance, alternatives presentation, differentiated RQ recommendation Use when: refining research ideas, formulating research questions, clarifying scope Triggers: research question, 연구 질문, PICO, SPIDER, research idea

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Assesses a research paper outline (.qmd file) against structured criteria from Kosuke Imai's empirical research guide. Evaluates the abstract and section outline for: research question clarity, stated contributions, hypotheses and testable implications, data description, planned results and inferential approach, and robustness checks. Detects causal papers from the abstract and conditionally assesses identification strategy only when causal framing is present. Produces a structured markdown report with PASS/PARTIAL/FAIL assessments and actionable suggestions. Use when asked to assess, evaluate, review, or check a paper outline, abstract-and-outline, or research plan in a .qmd 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。

Instantiate a prefab into the currently active scene at an optional position/rotation/scale, parented under an optional scene GameObject path. Use 'assets-find' to locate the prefab asset first.

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。

Comprehensive guide for AntV L7 geospatial visualization library. Use when users need to: (1) Create interactive maps with WebGL rendering (2) Visualize geographic data (points, lines, polygons, heatmaps) (3) Build location-based data dashboards (4) Add map layers, interactions, or animations (5) Process and display GeoJSON, CSV, or other spatial data (6) Integrate maps with AMap (GaodeMap), Mapbox, Maplibre, or standalone L7 Map (7) Optimize performance for large-scale geographic datasets

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。

Design experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so the results will actually be interpretable. Use whenever someone is planning a study, asks how to assign subjects/samples to groups, mentions randomization, blocking, stratification, controls, factorial or fractional-factorial designs, design of experiments (DOE), screening many factors, response-surface optimization, crossover or repeated-measures or split-plot designs, cluster/group randomization, Latin squares, plate layouts, batch/run-order effects, replication vs. pseudoreplication, or sequential/adaptive/group-sequential designs. Trigger this even for informal phrasings like "how should I set up this experiment", "how do I avoid confounding", "what's the best way to test these 6 factors", or "assign these mice to conditions". For computing the sample size or power once the design is chosen, use statistical-power; for analyzing data already collected, use statistical-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。

GSC-driven content calendar. Pulls real Search Console data, finds the highest click-potential opportunities (striking-distance queries at positions 5-20, unanswered query intent, related-keyword expansions), and produces a dated, prioritized content calendar — ready to hand to /content-writer. Use when the user asks "plan my content", "what should I write next", "content calendar", "content plan", "content roadmap", "editorial calendar", "what topics will rank", "find quick-win SEO topics", "click-potential analysis", or "schedule my SEO content".

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。

Post-setup credential manager. Shows current integration status (configured/missing/expired) and lets you update individual credentials without re-running the full setup wizard. Runs a smoke test after each 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。

Initialize uc-taskmanager for the current project. Creates works/ directory and configures Bash permissions in .claude/settings.local.json. Use when the user says "uctm init", "initialize uctm", "uctm 초기화", or "초기화".

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 when the user is measuring GPU-kernel-initiated RDMA WRITE latency through doca-gpunetio — building and running the `gpunetio_ib_write_lat` client + server pair under `doca/tools/gpunetio_ib_write_lat/`, checking GPU-NIC pairing, reading the half-iter / full-iter / CUDA-side usec columns, characterizing median / p99 / jitter for a real-time control loop, picking GPUNetIO vs GPI vs CPU-initiated `perftest`, or weighing the latency-vs-batching trade-off. Trigger even without 'GPUNetIO' or 'ib_write_lat': 'GPU kernel RDMA latency benchmark', 'how fast can a CUDA kernel post a WRITE', 'p99 RDMA latency on H100 + ConnectX', 'kernel-launched WR tail latency', or 'compare GPU-init vs CPU-init perftest'. Route elsewhere for bandwidth runs (doca-gpunetio-ib-write-bw), the GPI surface (doca-gpi), library debugging (doca-gpunetio), or DOCA install.

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 for hands-on DOCA GPI programming — wiring a GPU-Packet-Initiator context so a CUDA kernel drives RDMA queues directly from GPU memory without host CPU mediation. Covers picking GPI vs doca-gpunetio, the doca_gpi / domain / channel object model, the GPU-side handle handoff (doca_gpu_gpi_channel*), attaching GPU memory to a GPI domain, the domain and channel attribute objects, and debugging DOCA_ERROR_* from doca_gpi_* calls. Trigger even when the user does not explicitly mention "DOCA GPI" — implicit phrasings include "my CUDA kernel needs to post RDMA directly from GPU memory", "DOCA_ERROR_* from doca_gpi_gpu_channel_get", "how do I hand a GPU handle to my CUDA kernel", "how many channels can a GPI domain hold", or "GPU kernel driving RDMA without the host CPU on the path". Refuse and route elsewhere for the doca-gpunetio Send/Receive surface, the doca-rdma queue lifecycle, DPA-side initiation (doca-rdmi), or the CUDA programming model — those belong to other 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。

Use this skill when the user is doing hands-on DOCA SHA programming — offloading SHA-1, SHA-256, or SHA-512 hashing onto a BlueField DPU or ConnectX accelerator, picking between one-shot `doca_sha_task_hash` and incremental `doca_sha_task_partial_hash`, querying `doca_sha_cap_*` for algorithm support and min destination / max source buffer sizes, setting source / destination `doca_mmap` permissions, or decoding DOCA_ERROR_* returns from the SHA API. Trigger even when the user does not explicitly mention "DOCA SHA" or "doca_sha_task" — typical implicit phrasings include "hash a multi-GiB file on the DPU", "offload SHA-256 to the BlueField", "streaming hash over chunks", "partial hash returns BAD_STATE", "destination buffer too small for digest", or "is SHA-512 available on this card". Refuse and route elsewhere for general cryptographic-hash theory (collision resistance, SHA-3 selection), other DOCA crypto libraries (AES-GCM, Compress, DMA), or DOCA install / BFB bring-up — those belong to other 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。

Pretrain a fresh KERMT model from scratch on a user-provided corpus. Builds a new vocabulary from the corpus, instantiates the model architecture from defaults, and launches pretrain_ddp.py inside the kermt container (detached for long runs). Unlike kermt-continue-pretrain, no starting checkpoint is loaded — the model is randomly initialized.

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。