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"Analyze and mitigate the bullwhip effect where demand variability amplifies upstream in supply chains. Use this skill when the user needs to diagnose order variability amplification, quantify the bullwhip ratio, or implement dampening strategies — even if they say 'why are our orders so volatile', 'supply chain variability', or 'demand amplification problem'.".

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Generates specific, implementable fixes for every finding in findings.json. Produces code patches (unified diff), configuration changes, dependency updates, and IaC fixes — not generic advice but actual before/after code. Uses the reproduction command from each finding as the verification step: "run this after the fix — it should now fail." Stores remediation data in findings.json so the dashboard shows a Fix button and /gh-export includes the remediation in GitHub issues. Chains from /pentester, /codebase, or any scan skill after findings are produced. Chains into /gh-export for export with remediation included.

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。

Post-exploitation workflow. Covers privilege escalation (Linux SUID/sudo/kernel, Windows UAC/service/token), persistence assessment, local enumeration, credential harvesting, and pivot preparation. Structured workflows for Linux and Windows targets using impacket, netexec, john, linpeas/winpeas, and standard Kali tools. Includes kernel exploit reference tables, GTFOBins exploitation chains, Potato attack selection, Docker/container escapes, DLL hijacking, SSH key harvesting, credential recovery from memory, and Windows token manipulation. Chains from /pentester or /credential-audit when access is obtained.

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。

Exploit PHP object serialization vulnerabilities (Insecure Deserialization). This skill details how to identify unserialize() injection points and construct malicious serialized objects using Property Oriented Programming (POP) chains to achieve 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。

Systematic fuzzing across two dimensions: (1) content discovery — hidden directories, files, backup/source leaks, HTTP verb enumeration, 401/403 bypass via path manipulation; (2) input validation — auth/token stripping, type confusion, boundary value analysis, HTTP parameter pollution, header fuzzing, cookie fuzzing, mass assignment discovery, and entropy/predictability analysis of any generated value. Works on any domain. Chains from /web-exploit or /pentester; chains into /business-logic when boundary violations, predictable IDs, or mass assignment are confirmed.

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。

Simulate supply chain and adversarial machine learning attacks by injecting poisoned data or targeted backdoors into training and fine-tuning datasets. Use this skill when assessing the integrity controls of MLOps pipelines or evaluating the resilience of AI models against highly targeted, stealthy manipulation intended to alter model behavior on specific triggers.

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。

Supply-chain testing via package-manager dependency confusion: when internal package names resolve to attacker-controlled public registries, leading to malicious install and script execution. Use for npm/pip/gem/Maven/Composer/Docker manifest review and authorized red-team supply-chain exercises.

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 hunting GitHub Actions cache poisoning vulnerabilities where an attacker can inject malicious content into the CI/CD cache and have it restored by a privileged downstream workflow. Trigger on: "cache poisoning", "actions/cache", "actions/setup-node", "node_modules cache", "GitHub Actions cache", "pnpm cache", "LRU eviction", "10GB limit", "Cacheract", "poisoned cache", "workflow cache attack", supply chain via CI cache, "ng-renovate", "cache stuffing", scheduled workflow cache restore, shared cache key, "hashFiles package.json", cross-workflow cache, PR workflow release workflow same key, "npm install prefer-offline", Cacheract, Gato-X, supply chain npm token.

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。

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.

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。

Application-level business logic security testing for any domain. Takes an understanding-first approach: map the intended workflows before probing them. Covers: value/quantity logic abuse (negative, zero, overflow, rounding on any numeric field), workflow and state machine bypass (skipping required steps, forcing illegal state transitions, reusing one-time tokens), trust boundary violations (BOLA horizontal/vertical, BFLA, cross-tenant access, negative ownership attacks), idempotency and replay attacks (duplicate submissions, double-spend, same-reference reuse), multi-step flow integrity (checkout, registration, approval, verification), quota and rate limit bypass, time/date manipulation, and authorization code / reference number predictability. Domain-agnostic — applies to SaaS, e-commerce, banking, gaming, social platforms, APIs, or any multi-user application with stateful workflows. Chains from /pentester; chains into /param-fuzz when boundary violations or mass assignment are confirmed.

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。

Test AI agent systems for tool abuse, unauthorized actions, privilege escalation through tool chaining, and safety bypass via agentic workflows. Use this skill when assessing autonomous AI agents that use tool-calling (function calling, plugins, actions) to interact with external systems. Covers multi-step attack chains, implicit trust exploitation, and capability boundary testing for AI agents.

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AI/LLM red-team assessment using the OWASP LLM Top 10 (2025) + OWASP AI Testing Guide (AITG v1, Nov 2025) frameworks, plus OWASP MCP Top 10 runtime testing for agentic/MCP targets. Tests prompt injection, jailbreaks, system prompt leakage, sensitive data extraction, excessive agency, improper output handling, model extraction, content bias, evasion, membership inference, MCP token exposure, MCP command injection, and more. Uses four tools in combination: FuzzyAI (single-turn jailbreak fuzzing), PyRIT (multi-turn orchestrated attacks), Garak (probe-based vulnerability scanning), and promptfoo (plugin-based red-team evaluation). Each tool covers different OWASP categories; running them together gives systematic coverage. Includes a conditional MCP reconnaissance phase and a post-access AI infrastructure phase (chained from /post-exploit). Produces: OWASP LLM Top 10 + AITG + MCP coverage matrix, findings per category, architecture diagram of the AI system, PoCs for confirmed exploits. Chains into /gh-export for issue filing.

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 a complete Active Directory penetration test from initial enumeration to domain dominance. Use this skill for AD security assessments including LDAP enumeration, Kerberos attacks (Kerberoasting, AS-REP roasting), BloodHound attack path analysis, credential dumping with Mimikatz, lateral movement via PsExec/WMI/DCOM, DCSync for NTDS extraction, and Golden/Silver ticket forging. Covers the full kill chain from domain user to domain admin.

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Establishes safe practices for acquiring, storing, transferring, and disposing of malware samples: password-protected archives, neutralized extensions, hashing for identity, and chain-of-custody. Activates for requests about safely storing or sharing malware, sample handling hygiene, or defanging artifacts.

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。

Establishes a disciplined malware sample repository: content-addressed storage by hash, encrypted/password-protected archiving, consistent metadata records, and chain-of-custody tracking so samples are reproducible and safe to handle. Activates for requests to organize a malware repository, manage samples, or track sample metadata and provenance.

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。

OpenUI generative UI with LangChain/LangGraph backend. Supports ChatOpenAI and ChatAnthropic.

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。

Assess supply chain risk exposure and resilience posture. Analyzes supplier dependency mapping (Tier 1/2/3), geographic concentration risk, single-source vulnerability, disruption scenario modeling (pandemic, trade war, port closure), Monte Carlo simulation.

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Analyze defense supply chain systems — DFARS compliance assessment, CMMC cybersecurity readiness, sole-source and DMSMS risk identification, counterfeit parts prevention per SAE AS6171, ITAR export control verification, and supplier tier mapping.

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Analyze food supply chain systems for waste reduction opportunities including shelf life prediction models, FIFO and FEFO inventory rotation enforcement, demand forecasting accuracy and bias, donation logistics workflows, cold chain temperature monitoring, and sustainability reporting against EPA Food Recovery Hierarchy and UN SDG 12.3 targets.

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Audit a supply chain compliance system for ethical sourcing and labor rights. Triggers: building or reviewing supply chain compliance platforms, ESG reporting systems, or textile/garment sourcing tools.

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**WORKFLOW SKILL** — Persist analysis so it survives context compression and chains between tasks. USE FOR: any task whose decisions affect future work — features, refactors, architecture changes, integrations. Produces a lightweight Task Analysis Map that externalizes intent, decisions, and chain context. Use to document decisions, create continuity, prevent context loss, bridge tasks, externalize reasoning. DO NOT USE FOR: quick intent validation (use task-intent), deep structured analysis (use contextação).

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Use when designing, mapping, or troubleshooting the end-to-end quote-to-cash process in orgs running Salesforce CPQ (managed package) or Revenue Cloud — covering the full object chain from SBQQ__Quote__c through Contract to blng__Invoice__c, approval routing via Advanced Approvals (sbaa__), billing schedule generation, and order activation. Trigger keywords: CPQ quote, SBQQ, revenue cloud billing, Advanced Approvals, subscription, blng invoice, contract pivot. NOT for quote-to-cash on standard Sales Cloud objects — use admin/quote-to-cash-requirements. NOT for setting up SBAA approval rules and chains — use admin/cpq-approval-workflows.

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。

ผู้จัดการ supply chain — ABC analysis, EOQ, safety stock, demand forecast, JIT, supplier mgmt, 3PL selection

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Blockchain/Web3 Developer — smart contract (Solidity/Rust), ERC-20/721/1155, DeFi protocol, security audit เบื้องต้น, deploy testnet/mainnet

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。