950 件 ・ 関連度順
概要と使いどころ
Full penetration test using MCP tools — recon, scanning, exploitation, and reporting. Tailored for OpenCode (BYO LLM). Supports network/web targets and local codebases. Chains into analyze-cve, threat-modeling, and remediate skills automatically.
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White-box source code security review structured around OWASP ASVS 5.0 (427 verification requirements across 16 chapters). Reads and understands application source code to build a security-aware knowledge base that enriches all downstream skills. Covers: tech stack identification, route/endpoint mapping, authentication and authorization architecture, dangerous function patterns, source-to-sink data flow tracing, IaC review, dependency analysis, ASVS compliance mapping, and LLM integration security (prompt injection, tool abuse, output handling, RAG poisoning, MCP server patterns). When LLM/AI framework usage is detected, automatically reviews OWASP LLM Top 10 patterns from source code and chains into /ai-redteam with white-box context for live endpoint testing. Chains into /pentester, /threat-modeling, /web-exploit, /api-security, /cloud-security, /analyze-cve, /credential-audit, and /ai-redteam — providing white-box context that transforms black-box testing into targeted, informed assessment.
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Prevent common AI report pitfalls — bug blending, inflated threat models, and generic language. Train Claude with your best past reports for concise, technical submissions. Based on Critical Thinking Bug Bounty Podcast Episode 166.
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Curate, review, or repair ai-gene-review ModuleReview YAML documents under modules/, including pathway/module boundary setting, parts and variant modeling, annoton-level molecular functions, representative UniProt/PTN grounding, module deep-research provenance, validation, rendering, and project batch updates.
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Use when introducing, reviewing, or renaming a top-level type, table, or domain concept; or choosing where state lives (in-memory vs persistent)
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Use when creating, configuring, reviewing, or debugging TypeScript project standards across frontend apps, libraries, SDKs, CLIs, monorepo packages, tsconfig, strictness, module/moduleResolution, path aliases, project references, declaration files, package exports, public API types, DTOs, advanced generics, discriminated unions, type guards, type narrowing, or type-level regressions. Prefer framework project skills for React/Vue/Next/Nuxt component architecture; Chinese triggers include TypeScript project specification, TS project specification, TypeScript type safety, type modeling, generics, discriminative unions, type narrowing, tsconfig, declaration 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。
Full-funnel paid advertising operations for B2B SaaS. Use this skill for PPC strategy, Google Ads optimization, LinkedIn Ads, social media advertising, programmatic buying, creative strategy, attribution modeling, budget allocation, budget pacing and planned-vs-delivered reconciliation, audience overlap and campaign self-competition audits, suppression lists, ROAS improvement, media spend optimization, and the media no auction sells — newsletter and podcast sponsorships, community and industry-publication placements, paid review-site listings on G2 and Capterra, and pay-per-lead content syndication. It also covers buying on the AI answer surface — ads inside Google AI Overviews and AI Mode that existing Search, Shopping and Performance Max campaigns already serve into with no opt-out, no placement targeting and no segmented reporting, and ChatGPT Ads and Microsoft Copilot as separate opt-in platforms with their own plan-tier reach limits, no negative keywords, and a landing-page crawler gate. It also owns the conversion signal the bidding algorithms learn from — CRM pipeline stages (SQL, opportunity, closed-won) sent back to Google Ads, LinkedIn, Meta and Microsoft Ads through offline conversion import, enhanced conversions for leads, the Google Data Manager API and the LinkedIn Conversions API, with click-ID capture, stage values, dedupe, EEA consent signals and retractions. It also gates connected TV (CTV) and streaming video for B2B — whether a reach medium belongs in the plan, how a vendor ties a TV household to a target account (member data, IP-to-account graph or contextual), the server-side ad insertion (SSAI) fraud questions, Programmatic Guaranteed deals, and a holdout lift test because CTV has no click. It also runs person-led paid social — LinkedIn Thought Leader Ads and Meta partnership ads that put spend behind an employee's or creator's post — on a paid-usage term, an in-post disclosure check and an end-date register, because the platform approval carries no term. Also triggers on: CTV, connected TV, OTT, streaming TV ads, should we run CTV, LinkedIn CTV, B2B CTV, account-based CTV, SSAI fraud, thought leader ads, boost an employee's post, sponsor a creator's post, partnership ads, whitelisting, creator usage rights, PPC, Google Ads, LinkedIn Ads, social ads, programmatic, ad creative, attribution, media budget, paid campaigns, ROAS, CPA, ad spend, newsletter sponsorship, podcast sponsorship, community sponsorship, sponsor a newsletter, content syndication, pay-per-lead, cost per lead vendor, media kit, rate card, insertion order, direct buy, publisher partnership, make-good, G2 paid listing, Capterra ads, review site advertising, sponsorship ROI, budget pacing, we underspent our budget, are our campaigns competing with each other, audience overlap, self-competition, campaign cannibalization, suppression list, exclusion list, ad frequency across channels, how many ad accounts do we have, ads in AI Overviews, AI Mode ads, ChatGPT Ads, OpenAI Ads Manager, Copilot ads, conversational ads, should we advertise on ChatGPT, can we buy our way into the AI answer, our ads are showing in AI Overviews and we cannot report on it, context hints, OAI-AdsBot, our ad landing page fails review, robots.txt is blocking our ads, our paid leads are junk, Google Ads is optimizing for the wrong leads, offline conversion import, OCI, enhanced conversions for leads, send CRM stages back to Google Ads, upload SQLs to LinkedIn, LinkedIn Conversions API, LinkedIn CAPI, Meta conversion leads, CRM to ad platform, gclid, li_fat_id, msclkid, value-based bidding, optimize for pipeline not form fills, low match rate, conversion lag.
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Iterative manuscript improvement workflow where a reviewer agent critiques, an author agent revises, a verifier checks the revision, and the loop repeats until acceptance or a human-pause condition is reached. Use for virtual peer review, reviewer-author revision loops, rebuttal preparation, resubmission polishing, journal paper improvement, response-to-reviewers drafting, and deciding when additional data, experiments, theory, modeling, or author judgment is required before further revision.
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Use when a task needs the judgment of a Data Warehousing Specialist — defining the grain of a fact table before modeling it, choosing a slowly changing dimension (SCD) type for a dimension attribute, designing an incremental ETL/ELT load strategy against a full-refresh alternative, resolving a conformed-dimension conflict across source systems, or diagnosing why a BI report's numbers don't reconcile to the source system.
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Use when a task needs the judgment of a Compensation, Benefits, and Job Analysis Specialist — market-pricing a job against salary survey data, scoring a job through a point-factor or IPE job evaluation, running a pay-equity regression or cohort analysis, building or refreshing a salary structure and compa-ratio bands, classifying a job family's FLSA exemption status through the duties test, or modeling benefits plan cost (loss ratio, PEPM, self-funded vs. fully insured). Distinct from hr-people-manager (owns the comp/leveling judgment calls — whether to approve an exception, promote someone into a level), hr-specialist (owns individual-case leave/I-9/ADA/FLSA reclassification for one employee's drifted duties), and compensation-benefits-manager (owns pay philosophy, negotiation-range policy, and the department that runs this work). This role owns the mechanics: the survey match, the point score, the regression, the band math — the analysis those upstream policy and downstream judgment calls are made against.
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 a task needs the judgment of an Application Security Engineer — checking whether an endpoint verifies object-level authorization (not just authentication), running threat modeling at design time rather than deferring security review to the end, triaging a dependency vulnerability by actual reachability rather than CVSS score alone, combining SAST/DAST/SCA tooling to cover their distinct blind spots, or applying context-specific output encoding to untrusted input. Distinct from an information security analyst — this role works inside the SDLC to prevent vulnerabilities before they ship, not detect and respond to intrusions in production.
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Expert guidance for Domain-Driven Design architecture and implementation. Use when designing complex business systems, defining bounded contexts, structuring domain models, choosing between modular monolith vs microservices, implementing aggregates/entities/value objects, or when users mention "DDD", "domain-driven design", "bounded context", "aggregate", "domain model", "ubiquitous language", "event storming", "context mapping", "domain events", "anemic domain model", strategic design, tactical patterns, or domain modeling. Helps make architectural decisions, identify subdomains, design aggregates, and avoid common DDD pitfalls.
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Comprehensive security audit covering OWASP Top 10, secrets detection, supply chain security, threat modeling, and language-specific vulnerability patterns. Investigates actual code paths rather than grep-matching keywords. Generates a scored SECURITY_AUDIT.md with prioritized remediation. Use when assessing application security, preparing for a security review, or onboarding to a codebase with security concerns.
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。
fMRI data analysis — preprocessing, GLM modeling, and group-level inference for task and resting-state studies.
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。
Dose-response modeling — EC50 estimation, curve fitting, synergy analysis, and benchmark-dose 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。
Analyze text data with NLP — preprocessing, embeddings, topic modeling, sentiment, classification, and evaluation. Use when turning unstructured text into structured insight.
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。
Domain-Driven Design: bounded contexts, ubiquitous language, aggregates, and tactical patterns. Use when modeling complex domains or structuring domain logic.
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。
Manage ML datasets with the datasets ecosystem — loading, streaming, preprocessing, versioning, and sharing. Use when data handling (not modeling) is the bottleneck.
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Run the data science workflow end to end — framing, exploration, feature engineering, modeling, validation, and communicating results. Use when turning raw data into decisions or predictions.
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。
Relational schema design — normalization, keys, constraints, and evolution — use when modeling data or reviewing a schema.
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。
Infectious disease modeling with SIR/SEIR models — R0, fitting, forecasting, and intervention scenarios.
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Integrate headless CMSs: content modeling, APIs, previews, webhooks, and editorial workflows. Use when content teams need to publish without developers.
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。
Bulk RNA-seq differential expression — experimental design, count modeling with DESeq2/edgeR, and enrichment.
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 3D visuals with modeling, texturing, lighting, rendering, and optimization for web and product.
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。