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概要と使いどころ
Organizes, queries, validates, and converts Brain Imaging Data Structure (BIDS) datasets. Supports organizing neuroscience and biomedical data (MRI, EEG, MEG, iEEG, PET, microscopy, NIRS, motion capture, EMG, MR spectroscopy, behavioral), querying BIDS layouts, validating compliance, converting DICOM to BIDS, writing metadata sidecars, or creating BIDS derivatives.
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。
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for JWT, JWS, and JWE validation paths, header parsing, key selection, claim acceptance, audience and issuer checks, role derivation, and token-to-identity confusion bugs. Use when the user asks to inspect JWT headers or claims, key lookup, `kid` handling, `alg` confusion, audience or issuer validation, role claims, or explain how a token becomes accepted identity or privilege. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
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。
Python error handling patterns including input validation, exception hierarchies, and partial failure handling. Use when implementing validation logic, designing exception strategies, handling batch processing failures, or building robust APIs.
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。
Coordinate parallel code reviews across multiple quality dimensions with finding deduplication, severity calibration, and consolidated reporting. Use this skill when organizing multi-reviewer code reviews, calibrating finding severity, or consolidating review results.
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。
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
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。
Experimental (preview) Pester skill for migrating classic Should -Be (v5) assertion syntax to the new Should-* (v6) assertions (note the hyphen, no space), e.g. `Should -Be` -> `Should-Be`, `Should -Not -Be` -> `Should-NotBe`. Tracks Pester 6, which is still a release candidate, so this guidance may change; verified against Pester 6.0.0-rc2. Use when converting Pester v5 assertions to Pester v6 Should-* operators, modernizing a Pester test suite, or when a user asks to migrate, convert, or rewrite `Should -...` calls in .Tests.ps1 / PowerShell files.
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。
CommunityToolkit.Mvvm (the MVVM Toolkit) core: source generators ([ObservableProperty], [RelayCommand], [NotifyPropertyChangedFor], [NotifyCanExecuteChangedFor], [NotifyDataErrorInfo]), base classes (ObservableObject / ObservableValidator / ObservableRecipient), commands (RelayCommand / AsyncRelayCommand), and validation. Companion skills: mvvm-toolkit-messenger for pub/sub, mvvm-toolkit-di for Microsoft.Extensions.DependencyInjection wiring. Works across WPF, WinUI 3, MAUI, Uno, and Avalonia.
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。
Review the implementation source code of MCP (Model Context Protocol) servers, clients, and tool handlers against a security baseline — authentication, sessions, rate limiting, input-schema validation, official-SDK usage, RCE vectors, and the OWASP MCP Top 10 — producing a report with file/line evidence. Use this skill when: - Reviewing an MCP server implementation for security before release - Checking a server against the baseline controls (MCP-01 to MCP-05) and the OWASP MCP Top 10 - Auditing tools for RCE vectors (command/code injection, unsafe deserialization, path traversal, SSTI, dependency hijacking, SSRF) - Verifying auth, session, rate-limiting, and input-validation controls on a network-exposed server - Reviewing MCP client code that handles untrusted server responses and session IDs - Requests like "review this MCP server for security" or "is my MCP server implementation secure?"
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。
Load when an agent needs to manage, install, update, remove, or validate OpenViking skills via the `ov skills` CLI. Trigger on explicit user requests about skill management, when the user mentions `ov skills`, `install skill`, `update skill`, `delete skill`, `validate skill`, or when an agent needs to discover what skills are available on the OpenViking server.
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。
Periodic architecture survey — walks the module graph and reports ranked deepening candidates (shallow modules, hypothetical seams, logic behind the wrong seam). Survey, not rescue: it finds candidates and hands them to the captain; it never refactors on its own.
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 should be used when the user asks to "create a hook", "add a PreToolUse/PostToolUse/Stop hook", "validate tool use", "implement prompt-based hooks", "use ${CLAUDE_PLUGIN_ROOT}", "set up event-driven automation", "block dangerous commands", or mentions hook events (PreToolUse, PostToolUse, Stop, SubagentStop, SessionStart, SessionEnd, UserPromptSubmit, PreCompact, Notification). Provides comprehensive guidance for creating and implementing Claude Code plugin hooks with focus on advanced prompt-based hooks API.
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。
Workflow for upgrading/integrating cache-dit in SGLang diffusion (multimodal_gen): DBCache, DMD calibrator, TaylorSeer, SVDQuant DQ; porting upstream PRs and resolving conflicts against the per-request knob system; adding new cache knobs; building the sglang generate CLI test matrix; precision validation (PSNR / log evidence); troubleshooting environment issues (wheel ABI, svdq extension, flashinfer conflicts). Use when upgrading or integrating cache-dit in sglang diffusion, porting cache-dit PRs with conflicts, adding cache knobs, running the sglang generate CLI test matrix, or validating precision (PSNR) for DBCache/DMD/SVDQuant(DQ) paths.
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。
Earnings estimate revisions, guidance analysis, and post-earnings drift (PEAD) — track analyst consensus changes, earnings surprise patterns, and management guidance shifts for US/HK equities.
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。
Install and run Stratus Red Team to detonate granular, MITRE ATT&CK-mapped AWS, Azure, GCP, and Kubernetes attack techniques through their warmup-detonate-revert-cleanup lifecycle, then verify the telemetry they generate. Use to validate that cloud detections (CloudTrail, GuardDuty, Microsoft Sentinel, GCP SCC, Falco) actually fire, or to run a repeatable purple-team exercise generating realistic attacker telemetry.
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 prompt injection using regex signature matching, heuristic scoring for structural anomalies, and DeBERTa-based transformer classification, flagging direct injections (system-prompt overrides, role-play escapes) and indirect injections (encoded payloads, obfuscation) per OWASP LLM Top 10 (LLM01:2025). Use for input validation layers in chatbots/agents/RAG pipelines, or for retrospectively classifying injection attempts in logs or incident investigations.
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。
Conduct comprehensive GDPR compliance assessments by evaluating data processing activities against EU Regulation 2016/679, including Article 30 records of processing, lawful basis validation, data subject rights implementation, Data Protection Impact Assessments (DPIAs) under Article 35, breach notification procedures, international transfer safeguards (SCCs, adequacy decisions), and technical/organizational measures under Article 32. Use when processing personal data of EU residents, preparing for supervisory authority audits, implementing privacy-by-design for new systems, scoping compliance gaps for M&A due diligence, assessing third-party processors, or responding to data subject access requests at scale. Incorporates 2026 guidance from ICO, EDPB, and post-Data (Use and Access) Act 2025 UK-GDPR considerations. Do not use for implementing specific Article 32 controls — use implementing-gdpr-data-protection-controls; or for DSAR automation — use implementing-gdpr-data-subject-access-request.
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。
WorkorAI talent marketplace skill: candidate job search and employer hiring with white-box match explanations via the WorkorAI MCP server (https://workorai.com/mcp). Use when the user asks to find a job, apply to jobs, respond to employer invitations, or when an employer wants to post jobs, search and evaluate candidates, invite them, and review applicants.
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。
Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library
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。
Add clidash — a zero-dependency, read-only web dashboard that derives its tabs and tables at runtime from any CLI that lists resources as JSON. Ships pre-wired for NanoClaw's ncl CLI (agent groups, sessions, channels, users, roles), plus message-activity charts, a log tail, and a read-only file viewer for group skills/CLAUDE.md/profiles.
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。
Skill validation framework PLUS daily test-suite health and regression intelligence. Validates skill conformance (frontmatter, manifest coverage, resolver coverage). Runs the project test suite in tiered phases (unit / evals / integration / system health), classifies failures, and produces a regression-aware report.
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。
Ghostwrite content in a specific person's voice from a VALIDATED voice profile — tweets, replies, short posts, launch copy, recruiting blurbs, emails. Loads the subject's voice profile (people/<slug>-voice) plus first-party context from the brain, drafts 2-3 options in-register, then runs a hard voice-fidelity self-check before showing anything. Includes the profile BUILDER: if no validated profile exists, drafting hard-stops and this skill walks the corpus-to-fingerprint build instead. Never auto-posts.
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。
Token-hygiene audit of the always-loaded context stack — CLAUDE.md, AGENTS.md, auto-memory MEMORY.md, and the bootstrap-rendered identity files (SOUL.md, USER.md, ACCESS_POLICY.md, HEARTBEAT.md) or their harness equivalents. Finds redundancy, contradictions, stale content, compression candidates, and skill-extraction candidates; produces a ranked action list sorted by token savings with a risk class per finding. REPORT-ONLY: this skill never edits any audited file. Recommendations for bootstrap-rendered files target the interview answer bank / templates, never the rendered output. Judging routes through `gbrain eval cross-modal` (single cheap model by default; full multi-model panel is explicit opt-in).
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。
Prepare a local Python SDK release candidate in a dedicated worktree. Use only when explicitly invoked with a version.
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 or adapt a bounded computer-use loop where Cua Driver observes and acts, TypeSafe Jev selects only from application-owned candidate IDs, and the caller validates and verifies every action. Use for the jev-use recipe or similar Jev integrations; do not use it to add model logic or credentials to Cua Driver.
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。