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概要と使いどころ
Configure CI/CD pipeline for automated Figma-to-code generation with Anima. Use when automating design-to-code in GitHub Actions, setting up PR-based component generation, or integrating Anima into design handoff workflows. Trigger with: "anima CI", "anima GitHub Actions", "anima automated generation".
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。
Establish a deterministic local EVM development loop with pinned forks, synthetic fixtures, and bounded Alchemy usage. Use when adding local chain tests or repairing flaky fork-based development. Trigger with "Alchemy local dev", "Hardhat fork with Alchemy", or "pin a mainnet fork".
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。
Execute proactive auto-loading: automatically detects and loads agents.md files. Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
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。
Execute this skill should be used when the user asks about "SPAWN REQUEST format", "agent reports", "agent coordination", "parallel agents", "report format", "agent communication", or needs to understand how agents coordinate within the sprint system. Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
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。
Execute software engineer specializing in creating production-ready ADK agents with best practices, code structure, testing, and deployment automation. Use when asked to "build ADK agent", "create agent code", or "engineer ADK application". Trigger with relevant phrases based on skill purpose.
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 this skill automates the adaptation of pre-trained machine learning models using transfer learning techniques. it is triggered when the user requests assistance with fine-tuning a model, adapting a pre-trained model to a new dataset, or performing... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
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。
Organization-grade identity-fabric mapping: tenant/federation fingerprinting and the pre-auth user-ENUMERATION oracle methodology — enumeration and fingerprint only, never credential submission. Covers domain-to-tenant resolution (Microsoft getuserrealm.srf Managed/Federated namespace check, Entra OIDC metadata tenant-GUID extraction, Autodiscover v2), keyless Microsoft tenant-federation mapping (GetFederationInformation SOAP -> sibling-domain discovery, discover-only ROE, FEDERATED_WITH provenance edge held out of attack-path pivoting), Okta org-slug derivation + OIDC fingerprint + governed custom-domain enumeration, ADFS passive/active fingerprint + version inference, Google Workspace MX-correlated detection, generic OIDC (Auth0/Keycloak/Ping Identity/OneLogin/Duo) discovery, SAML metadata (5 paths), Azure AD Seamless-SSO Negotiate-challenge detection, Microsoft Defender for Identity (MDI) sensor-API presence check, the user-enumeration oracle methodology for Microsoft GetCredentialType (IfExistsResult semantics: exists / doesn't-exist / exists-in-federated-tenant / throttled) and Okta /api/v1/authn (errorCode differential), Medium-detectability discipline with a hard 20-candidate-per-tenant cap and admin/role interest-based ranking, and name x confirmed-email-pattern login-candidate synthesis that FAILS CLOSED with zero output when no org pattern is confirmed. Grounded directly in a production ASM implementation's sso_idp.py, tenant_recon.py, and core/email_patterns.py modules. Deepens — does not duplicate — offensive-osint skill's Identity Fabric endpoint reference with the tenant-federation MAP, the oracle WORKFLOW, and the candidate-SYNTHESIS methodology that reference lacks. Use when fingerprinting an organization's identity provider, mapping its tenant/federation boundary, running an authorized pre-auth user-enumeration pass, or synthesizing login candidates from harvested names to feed that oracle — never for password spray, credential submission, or auth bypass.
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。
Turns one-shot external recon into a continuous monitoring program. Covers the scheduled re-scan-and-diff loop (baseline snapshot -> interval sleep -> re-scan -> asset/finding delta -> threshold-gated webhook alert), the scan-to-scan diff engine (new/removed/changed assets by a tracked-attribute table, new/resolved findings by a stable cross-scan fingerprint), adversary CTI / chatter monitoring across six public feeds (ransomwatch, ransomware.live, HackerNews Algolia search, Reddit security-subreddit RSS, GitHub Gist code-search, public Telegram channel scraping) with a source-kind-aware severity engine (leak-site/forum/telegram/paste tiers, CRITICAL through INFO), literal/glob/regex watchlist pattern matching, full-corpus capture with retroactive rescan on new watchlist entries, infrastructure-tracking-over-time discipline (certificate-transparency, passive-DNS, port/service, and typosquat re-enumeration cadence, and what a genuine 'perimeter drift' event looks like in the diff output), a five-state finding-lifecycle state machine (open/triaged/risk_accepted/resolved/false_positive) with per-severity SLA and fingerprint-based cross-scan dedup and auto-resolve/reopen rules, the alert-fatigue trap where a lifecycle-unaware rule re-fires on an already-accepted finding, a durable retry/backoff alert-outbox pattern ('queued is not delivered'), and copy-paste bash-cron plus PowerShell-Scheduled-Task recipes for a re-scan+diff loop with the Slack-compatible webhook payload shape. Passive OSINT and analysis only -- no new active-intrusion technique. Use when setting up ongoing monitoring for a retainer or MSSP engagement, tuning alert thresholds to avoid fatigue, triaging a finding's lifecycle status, investigating adversary chatter about a brand, building a 'what changed on the perimeter since last week' report, or deciding whether a persisting finding should re-alert.
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。
Turn a topic or finished script into a complete, publish-ready explainer video — led by an AI presenter from an authorized adult presenter image, or performed with no presenter in one of twelve explainer styles (nine visual styles and three narrative ones). Not for promos, ads, or footage montages. Use for new presenter or styled explainer videos and for continuing, revising, captioning, lip-sync repairing, or re-exporting an existing job. Use it whenever the user asks for 数字人口播 / 数字人讲解, for a 讲解视频 / 动画讲解 / 科普视频, asks which styles or templates are available (有哪些风格 / 看看风格 / 风格模板), or names one of the styles: 留白, 信号/科技感, 手帐/手绘, 立体书/纸艺, 波普漫画, 一镜到底/3D, 图纸与注脚, 黑板报, 黏土小城, 推演/动态图解, 案卷/调查复盘, 小剧场/生活场景. Keep model and provider selection capability-based and record the actual choices per job.
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。
Testing strategies including contract testing, snapshot testing, mutation testing, property-based testing, and test organization
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。
Claude-native deep research using DAG-based query planning, parallel subagent execution, and gap-driven iteration. No external API needed.
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。
Suggest better variable, function, and class names based on context and conventions.
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。
Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases. Credits: Original skill by @blader - https://github.com/blader/humanizer
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。
Lead workflow for developing vgpu features and Blender assets with the repository's subharness specialists. Use for agent-based research, API design, implementation, shaders, materials, simulations, or ImageGen-guided asset iteration, and when the human mentions the agent flow or subharness team.
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。
Run any question, idea, or decision through a council of 5 AI advisors who independently analyze it, peer-review each other anonymously, and synthesize a final verdict. Based on Karpathy's LLM Council methodology. MANDATORY TRIGGERS: 'council this', 'run the council', 'war room this', 'pressure-test this', 'stress-test this', 'debate this'. STRONG TRIGGERS (use when combined with a real decision or tradeoff): 'should I X or Y', 'which option', 'what would you do', 'is this the right move', 'validate this', 'get multiple perspectives', 'I can't decide', 'I'm torn between'. Do NOT trigger on simple yes/no questions, factual lookups, or casual 'should I' without a meaningful tradeoff (e.g. 'should I use markdown' is not a council question). DO trigger when the user presents a genuine decision with stakes, multiple options, and context that suggests they want it pressure-tested from multiple angles.
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。
macOS security bypass playbook. Use when targeting macOS endpoints and need to bypass TCC, Gatekeeper, SIP, sandbox, code signing, or entitlement-based protections during authorized red team or pentest engagements.
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。
Lattice-based cryptanalysis playbook. Use when attacking RSA via Coppersmith small roots, recovering DSA/ECDSA nonces from bias, solving knapsack problems, or applying LLL/BKZ reduction to cryptographic constructions.
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。
AV/EDR evasion playbook for Windows. Use when bypassing AMSI, ETW, .NET assembly detection, shellcode execution, process injection, API hooking, and signature-based detection on Windows endpoints.
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。
CORS misconfiguration testing playbook. Use when analyzing cross-origin trust, credentialed browser reads, origin reflection, preflight policy bugs, and browser-based access to authenticated 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。
AD Certificate Services attack playbook. Use when targeting misconfigured AD CS for privilege escalation via ESC1-ESC13 template abuse, NTLM relay to enrollment, CA officer abuse, and certificate-based persistence.
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。
Entry P1 category router for injection testing. Use when routing between XSS, SQLi, SSRF, XXE, SSTI, command injection, and NoSQL injection workflows based on how attacker-controlled input is consumed.
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。
Hash attack playbook. Use when exploiting length extension, MD5/SHA1 collisions, HMAC timing leaks, birthday attacks, or hash-based proof of work in CTF and authorized testing scenarios.
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 quiz and assessment funnels that generate qualified leads at 30-50% conversion. Use when the user mentions "quiz funnel", "scorecard", "lead magnet", "score-based segmentation", or "lead qualification". Also trigger when designing self-assessment tools, building calculators or graders for marketing, or creating personalized result pages that drive conversions. Covers concept hooks, question design, dynamic results by tier, and automated follow-up sequences. For landing page conversion, see cro-methodology. For full marketing plans, see one-page-marketing.
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。
Create award-winning, immersive web experiences at the level of Awwwards-featured agencies. Use when the user mentions "Awwwards quality", "make my site stunning", "scroll animations", "parallax storytelling", "cinematic web design", "portfolio site", or "brand experience". Also trigger when elevating a standard landing page into a memorable digital experience. Covers dramatic typography, purposeful motion, scroll-based composition, and performance-optimized animation. For foundational UI, see refactoring-ui. For type selection, see web-typography.
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。