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概要と使いどころ
"Implement Elo rating system to rank items or players from pairwise comparison outcomes. Use this skill when the user needs to rank items from head-to-head matchups, build a competitive rating system, or evaluate relative quality from comparison data — even if they say 'player rating', 'ranking from comparisons', or 'competitive scoring system'.".
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 CTR prediction models for estimating ad click-through rates from features. Use this skill when the user needs to predict click probability, build an ad ranking model, or evaluate ad creative performance — even if they say 'predict click rate', 'ad relevance scoring', or 'which ad will get more clicks'.".
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。
Zero-ceremony atomic task execution. Use for tiny fixes or enhancements (minutes, not hours) that don't warrant SDLC ceremony. Flags: --discuss (disambiguate first), --full (delegate to /pre-commit after execution), --issue N (link commit to GitHub issue).
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。
Phase 0 validation gate — problem framing, competitive analysis, feasibility, and a go/no-go checkpoint before committing to a feature. Use when evaluating whether an idea is worth building, before /story or /design. Trigger on "is this worth building", "validate this idea", "discovery", "should we build".
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。
Front-door router voor het werken aan een regelrecht-dossier. Gebruik dit aan het begin van een dossier, of bij twijfel over waar te beginnen — het triageert de situatie en routeert naar de juiste skill (regelrecht-stelselanalyse voor desk-review/corpus-completion, regelrecht-audit-products voor live expert-validatie) en de juiste volgorde. Bevat het routeringsprincipe: feitelijke defecten routeren naar de desk, oordeels-/praktijkvragen naar de workshop. Dossier-agnostisch.
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。
Performs critical code reviews using REVIEW.md guidelines. Evaluates code quality, architecture, testing, and domain-specific concerns (legal faithfulness, cross-law references, engine safety). Use after completing significant code changes or before merging.
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。
Describe what this skill does and the concrete situations in which an agent should use it.
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 exploring, continuing, or accepting a deep-learning or computer-science research idea. Converts claims into pilot-first experiment matrices covering diagnostics, failure analysis, authorized execution, evaluation, and paper-story viability.
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。
Optimizes Atlassian Forge apps to reduce platform consumption and avoid unnecessary costs using Atlassian's "Optimise Forge platform costs" guidance. Use when the user asks to optimize Forge app costs, reduce Forge invocations, lower GB-seconds, reduce storage or log usage, tune memory, replace polling, improve scheduled triggers, reduce KVS writes, move work to the frontend, use bridge APIs, batch API calls, add caching, or evaluate Forge Remote trade-offs. By default, perform an audit first and offer to make the recommended changes after presenting the audit. Only modify files immediately when the user explicitly asks the agent to implement or apply optimizations.
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。
Creates controlled synthetic slop for demos and evaluation fixtures.
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。
Generate controlled synthetic slop for demos and evaluation fixtures.
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 structured Requests for Proposal (RFP) or Requests for Quotation (RFQ) aligned with ISO 20400 sustainable procurement principles. Cover scope, technical requirements, evaluation methodology, and commercial terms. Use when sourcing vendors, services, or equipment.
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 strategic analysis using Porter's Five Forces, PESTEL, and SWOT frameworks. Assess competitive dynamics, macro environment, and internal capabilities. Synthesize into strategic implications with ranked options. Use when entering new markets, evaluating competitive position, or strategic planning.
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。
Evaluate and phase MCP adoption with a bias toward the smallest set of integrations that materially improve engineering work.
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 balanced, evidence-based performance reviews for employees. Covers achievements, development areas, and goal setting using the SBI (Situation-Behavior-Impact) feedback model. Produces reviews that are fair, legally defensible, and growth-oriented. Use during annual or semi-annual review cycles. Do not use for disciplinary actions, PIPs, or compensation calculations.
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 structured legal due diligence for M&A, joint ventures, or investment transactions. Cover all 9 workstreams — Corporate, Contracts, IP, Litigation, Regulatory, Employment, Tax, Environmental, and Data Privacy. Produce a risk matrix with deal-breaker flags and closing conditions. Use when evaluating a target or counterparty.
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。
Prompt engineering and task-specification skill for improving prompts, agent instructions, system messages, evaluation prompts, coding-agent tasks, multi-step workflows, and LLM output reliability. Use when rewriting vague prompts, adding constraints, reducing ambiguity, improving safety, creating reusable prompt templates, or converting goals into actionable agent instructions.
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。
Server-Side Template Injection (SSTI) occurs when user input is embedded directly into a template engine (Jinja2, Twig, Freemarker, Pebble, Velocity, Smarty, Mako) and evaluated, enabling remote code execution. Detect via math expressions `{{7*7}}` returning `49`, or `${7*7}`, `<%= 7*7 %>`. Leads to full RCE via template sandbox escape, Python `__class__.__mro__` traversal, and Java reflection chains. Tools: tplmap, Burp Suite.
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。
Identify and exploit Server-Side Template Injection (SSTI) vulnerabilities within web applications. Use this skill when testing web applications that render dynamic content using template engines like Jinja2 (Python), Twig (PHP), Freemarker (Java), or Pug (Node.js). Covers identification via mathematical evaluation, context mapping, escaping sandboxes, and achieving Remote Code Execution (RCE).
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。
SSTI playbook. Use when template expressions, server-side rendering, preview features, or templating engines may evaluate attacker-controlled 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。
Detects authentication and biometric bypass vulnerabilities in mobile apps (Android/iOS). Trigger on: BiometricPrompt, LocalAuthentication, LAContext, evaluatePolicy, CryptoObject, Android Keystore, Secure Enclave, kSecAccessControlBiometryCurrentSet, userAuthenticationValidityDurationSeconds, confirmCredentials, biometric fallback, PIN bypass, passive authentication, enrolled biometrics detection, Frida hook auth, jailbreak bypass, TouchID, FaceID, fingerprint. Covers MASVS-AUTH-1/2/3.
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 sensitive training data (PII, API keys, intellectual property, or code) directly from a deployed Large Language Model (LLM). This AI Red Teaming skill focuses on forcing models to regurgitate memorized, unredacted data from their massive internet-scraped datasets through repetition attacks, prefix continuation, and context window manipulation.
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。
Identify Open Redirect vulnerabilities where applications route users to arbitrary external URLs based on unvalidated input. Prove maximum impact by chaining Open Redirects with OAuth token theft, SSRF, or Phishing, graduating this typically low-risk finding into a high-severity 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。
Systematically bypass LLM safety filters and content moderation systems using advanced jailbreaking techniques. Use this skill when testing AI systems for safety alignment robustness, evaluating content moderation effectiveness, or conducting authorized AI red team assessments. Covers role-play attacks, few-shot manipulation, encoding bypass, multi-modal exploitation, and automated jailbreak discovery methods.
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。