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Cloud IAM red-team attack chain across AWS, Azure, GCP — focused on EXTERNAL exploitation paths and post-credential-discovery privilege analysis. Covers IAM enumeration (aws iam, az role, gcloud iam), STS/AssumeRole chaining, Azure Managed Identity abuse (via SSRF/leak), GCP service account JSON abuse, IMDSv1/v2 attacks via SSRF, K8s ServiceAccount token privilege analysis once held (token discovery / cluster exposure is owned by hunt-k8s), role-trust-policy confused-deputy, cross-account assume-role enumeration, IAM privilege escalation patterns (24+ AWS, 8+ Azure, 6+ GCP), and AWS Cognito Identity Pool unauthenticated-role attack chain (GetId → GetCredentialsForIdentity → IAM role abuse). Built for the case where recon yields a credential (key, JSON, token) and you need to know what it grants and how to escalate. Use when an AWS key / Azure secret / GCP service account JSON / K8s SA token surfaces from a code repo, JS bundle, APK, breach corpus, or SSRF chain.

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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).

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 MFA / 2FA bypass — 7 distinct patterns. (1) MFA not enforced on sensitive endpoints (password change, email change accept without MFA challenge), (2) MFA-step skip via direct navigation to post-login URL, (3) MFA-token replay (same code accepted twice), (4) brute-force the 6-digit OTP without rate limit (10^6 attempts at server speed), (5) race condition on OTP validation, (6) recovery-code dump via /api/me, (7) backup factor downgrade (SMS factor with no rate limit). Plus the chain: cookie theft + password oracle + no step-up = ATO without MFA challenge. Detection: trace auth flow in Burp, find every state transition, check if MFA is middleware-gated vs per-endpoint, check OTP entropy and rate limit on OTP-validate. Validate: attacker session reaching post-MFA state. Use when hunting auth bypass, MFA flows, chaining primitives toward ATO.

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。

Hunting skill for csrf vulnerabilities. Built from 15 public bug bounty reports including modern variants — SameSite=Lax sibling-subdomain bypass (Argo CD CVE-2024-22424), GraphQL mutations-via-GET (GitLab $3,370), framework-wide CSRF middleware disabled (Stripe Dashboard $5,000), path-traversal CSRF-token bypass (GitHub Enterprise CVE-2022-23732 $10k), Origin-omission bypass (TikTok $2,500), OAuth-state null-byte (Streamlabs), WebSocket CSRF / CSWSH (Coda), default-SameSite email-change → ATO (YoYo Games $400), social-account-link CSRF (HackerOne), JSON-CSRF via text/plain on email-change (TikTok $500). Use when hunting modern CSRF — heavy emphasis on chain-to-ATO 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。

Hunting skill for race condition vulnerabilities. Built from 12 public bug bounty reports including modern HTTP/2 single-packet attack cases (James Kettle DEF CON 2023 "Smashing the State Machine"; RyotaK / Flatt Security 10,000-request first-sequence-sync expansion 2024). Covers coupon double-redemption, gift-card double-spend, MFA-OTP-validate race, account-create race, faucet/crypto token double-mint, email-activation race, vote/upvote inflation, password-reset token race, rate-limit bypass via concurrent requests. Use when hunting race conditions, TOCTOU bugs, MFA-bypass-via-timing.

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。

Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.

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。

Write a `.cs` script file (create or overwrite) with the provided C# code. Validates syntax via Roslyn before write — invalid code is rejected with error details and the file is left untouched. Refreshes the AssetDatabase and delivers the final result via `requestId` after Unity finishes the triggered compilation. Use 'script-read' to inspect existing content first.

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Use this skill when the user is doing hands-on DOCA Telemetry Exporter programming on a host where DOCA is installed — defining a doca_telemetry_exporter_schema and event types, creating sources, picking a publish surface (typed events / opaque events / the metrics counter-gauge-histogram API / OTLP logs / NetFlow), walking the schema-then-source lifecycle, or debugging DOCA_ERROR_* failures from the exporter API. Trigger even when the user does not explicitly mention "DOCA Telemetry Exporter" or "doca_telemetry_exporter_*" — typical implicit phrasings include "publishing counters from my DOCA app", "BAD_STATE when I report an event", "consumer/DTS sees nothing but my report succeeded", "how do I export NetFlow/IPFIX records", or "should I link the exporter or the telemetry service". Refuse and route elsewhere for the receiving DOCA Telemetry Service (DTS), plain stdout logging via doca_log, or real-time event subscription back into the app via doca-comch — 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 GPUNetIO programming — wiring a CUDA kernel on an NVIDIA GPU to a doca-eth queue via doca_gpu_eth_rxq / doca_gpu_eth_txq, standing up the per-CUDA-device doca_gpu context, designing the persistent CUDA kernel that drains the GPU-visible queue, running the dual capability check (DOCA cap-query plus cudaGetDeviceProperties), registering cudaMalloc pools via doca_buf_arr_create_*, or debugging DOCA_ERROR_* returns from the GPUNetIO API. Trigger even when the user does not explicitly mention "DOCA GPUNetIO" or "persistent kernel" — typical implicit phrasings include "CUDA kernel reading packets directly from the NIC", "GPU-initiated networking on BlueField", "DOCA_ERROR_DRIVER on doca_gpu_create", "nvidia_peermem not loaded", "kernel-per-packet is too slow", or "which GPU supports GPU-side packet I/O". Refuse and route elsewhere for general CUDA programming, DOCA Ethernet queue bring-up, DOCA DPA, or DOCA install — 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 for BlueField-3 (BF3) day-1 platform bring-up via the classic RShim/BFB path: pushing a BlueField bundle (BFB) to the DPU over RShim with bfb-install from the host, the host-to-DPU TMFIFO management channel (tmfifo_net0, the 192.168.100.x convention), RShim daemon state and console-over-rshim, DPU mode selection (DPU/embedded-function vs separated-host/NIC mode) via mlxconfig, post-BFB recovery, a six-state BlueField-state classifier, and verifying the install (cat /etc/mlnx-release plus version checks). Trigger even when the user does not say "BF3" — typical phrasings include {push a BFB to my BlueField-3}, {bfb-install exited 0 but the DPU never came back}, {ping 192.168.100.2 works but ssh fails}, or {is DOCA on the host or the Arm side?}. BFB reflash, mlxconfig set, mode changes, and firmware burns are destructive: require explicit target-bound confirmation and load doca-hardware-safety. App launch, container deploy, env install, and the BF4 BMC-Redfish path route elsewhere.

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。

WARNING: guides potentially IRREVERSIBLE BlueField-4 hardware operations (PLDM firmware burns, ISO reflashes, power cycles, BMC factory resets) that can brick firmware, corrupt boot media, or cause outages — a maintenance window and rollback plan are required, and every mutating step is governed by doca-hardware-safety, loaded alongside. Use this skill for BlueField-4 (BF4) day-1 platform bring-up from the BMC: installing the BlueField/DOCA bundle ISO onto the DPU (Grace, the Arm complex) over UEFI HTTP Boot, PXE, or Redfish Virtual Media; the PLDM firmware-update flow (BMC, NIC firmware, SBIOS, ERoT) via the Redfish UpdateService and pldmtool; and a Grace Ubuntu image with optional cloud-init. Trigger on BlueField-4/BF4 bring-up phrasings even without "BF4": {bring up my new BlueField-4}, {the BlueField ISO will not boot over HTTP from the BMC}, {attach BF4 virtual media via Redfish}, {BF4 firmware Task stuck at Running}. BF3 bring-up, application launch, and library APIs 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 building, deploying, evaluating, debugging, or measuring latency for the DeepStream SOP Inference Microservice — a GPU-accelerated FastAPI service that detects whether operators perform assembly-line steps in order via event boundary detection (GEBD) plus VLM classification. Trigger even if the user does not name it: verify operator step sequence, detect missing or out-of-order SOP steps, score factory/work-cell video for procedure compliance, run VLM-based SOP checking on industrial cameras, or call /v1/chat/completions with a file, RTSP, or Basler camera. Also trigger for its internals: SOPVideoProcessor, DeepStream GEBD model (e.g. DDM) via Triton CAPI, nvds_custom_postprocess, Cosmos Reason 1/2 vLLM, SSE streaming, Kafka NvProto/JSON output, Basler/Pylon camera + emulation, Docker compose, chunk-level latency. Do NOT trigger for generic DeepStream pipelines, object detection/tracking, NIM imports, or video summarization.

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。

Golang semantic code intelligence via `gopls`, the official Go language server — go-to-definition, find references, call/implementation hierarchy, workspace symbol search, package API discovery, diagnostics, safe rename, refactors (extract/inline/fill/rewrite code actions), formatting, and generated tests. Reaches an agent via gopls's own MCP server (`go_*` tools), Claude Code's native `LSP` tool, or the `gopls` CLI. Use when navigating or refactoring Go code — jumping to a definition, finding call sites before a rename, understanding a file's or package's dependencies, running diagnostics after an edit, or extracting/inlining/renaming. Not for the published ecosystem — packages not in your `go.mod`, versions, licenses, importers — → See `samber/cc-skills-golang@golang-pkg-go-dev` skill (`godig`). Not for a whole-tree vulnerability audit → See `samber/cc-skills-golang@golang-security` skill (`govulncheck`).

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。

Apply the Law of Proximity — spatial closeness groups elements more strongly than any other cue. Use when spacing alone must carry grouping. For grouping via containers use `law-of-common-region`; via shared appearance use `law-of-similarity`.

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 THIS agent needs to pay for x402-protected content at runtime: hitting a paywall mid-task, settling it via AgentCore Payments, and applying operator-defined spend limits. Covers payment setup, policy, session budgets, and troubleshooting. Triggers on: "my agent hit a 402 while calling an API", "a tool call returned 402 Payment Required", "my agent needs to pay for x402-protected content", "let the agent pay for content, capped at $5 per session", "set a spend limit for the agent", "ProcessPayment failed", or "why did my agent refuse to pay". Not for BUILDING payment capability for end users, including wallets and framework middleware; use agents-build and references/payments.md. For non-paid APIs via Gateway use agents-connect. For inbound auth use agents-harden. For project scaffolding use agents-get-started.

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。

Tests Amazon MWAA workflow execution end-to-end: trigger a run and monitor it to completion for Provisioned (Python DAG, via Airflow REST API) and Serverless (YAML workflow, via StartWorkflowRun). Verifies the artifact is deployed and parse-ready, triggers with confirmation, polls to terminal state, and on failure delegates diagnosis to debugging-mwaa-workflow and artifact/redeploy fixes to authoring-mwaa-workflow, then retests up to a capped number of attempts. Triggers on: test my DAG, test my workflow, run my DAG, trigger a test run, does my DAG work, smoke-test the pipeline, verify my workflow runs, execute my DAG to check it. Not applicable to writing or deploying a new workflow (handled by authoring-mwaa-workflow), or for diagnosing why a run failed or root-causing an error (handled by debugging-mwaa-workflow).

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。

Prototype pollution testing for JavaScript stacks. Use when user input is merged into objects (query parsers, JSON bodies, deep assign), when configuring libraries via untrusted keys, or when hunting RCE gadgets via polluted Object.prototype in Node or the browser.

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。

AI image generation and editing for blog content powered by Gemini via MCP. Claude acts as Creative Director - interpreting intent, selecting domain expertise, constructing optimized 6-component prompts (Subject + Action + Context + Composition + Lighting + Style), and orchestrating Gemini for blog-quality results. Generates hero images, inline illustrations, social preview cards, and OG images. Edits existing blog images. Supports 6 blog-optimized domain modes (Editorial, Product, Landscape, UI/Web, Infographic, Abstract). Works standalone via /blog image or internally from blog-write and blog-rewrite workflows. Falls back gracefully when MCP is not configured. Use when user says "blog image", "generate hero image", "blog illustration", "social card", "generate blog image", "edit blog image", "image generate", "blog cover image", "inline image", "OG image".

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。

Weaviate vector database setup with GraphQL queries and hybrid search

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。

GitHub CLI skill for interacting with GitHub via the gh command line tool. Use when Bub needs to (1) Create, view, or manage GitHub repositories, (2) Work with issues and pull requests, (3) Create and manage releases, (4) Run and monitor GitHub Actions workflows, (5) Create and manage gists, (6) Install and manage agent skills, or (7) Perform any GitHub operations via command line.

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。

Set up and send technical status notifications through WeCom (Enterprise WeChat) webhooks. Use this skill whenever the user needs to send notifications, alerts, backup completion reports, or status updates via WeCom; when they mention 企业微信, 企微机器人, webhook, or alerting; or when a message needs to be clear, unambiguous, and technically precise rather than vague or condescending.

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。

Two scopes: (1) drives Kimi.app via computer-use for Work/Agent data-plugin queries (天眼查、同花顺 iFinD、SEC、IMF etc.) and Chat's built-in 深度研究 report; (2) kimi-cu macOS desktop-automation playbook — AX set_value / keyboard / click channel selection, foregrounding, AX-tree recovery, form & dialog recipes. Use for "用 Kimi 查" / "操作 Kimi 客户端" / "Kimi 插件" / "Kimi 深度研究" / driving any macOS desktop app (Electron/webview/native) via kimi-cu. Not for kimi.com browser automation (use kimi-webbridge) or direct iFinD API 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。

Write a human changelog from the REAL commit history — read the actual commit range via the GitHub connector, not a template. Use when asked to write the changelog for this release, what changed since the last tag, draft release notes from my commits, or summarise this range for users in Cowork. Reads commits/PRs between two refs via the GitHub connector, groups them into user-facing changes (features / fixes / breaking), translates commit-speak into human benefit, and produces a changelog artifact ready for the release.

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。

Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.

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。