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Audits GitHub Actions workflows that run AI coding agents. Traces how attacker-controlled content reaches prompts, checks permissions and sandbox settings, and reports evidence, severity, and fixes for prompt injection and related configuration risks.
Tests whether an AI agent accepts unverified plugins, tools, models, or updates. It checks risks introduced through external dependencies, including unsafe installation sources and missing version controls, and reports confirmed findings with conversation evidence.
Organize AI agent security findings using the ten OWASP ASI risk categories. Map issues such as leaked credentials or unauthorized tool calls to category IDs, and assign severity levels with suggested remediation timeframes.
Manage a crypto wallet through the onchainos CLI, a command-line tool. Check balances, send or swap tokens, bridge assets between blockchains, manage limit orders, track transactions, and inspect token approvals and security risks.
Add per-request API payments with x402, a protocol that uses HTTP 402 to request payment. Inspect charges, prepare wallet authorizations, assemble payment headers, and call payment processing services, or generate payment requirements for an API you operate.
Build blockchain applications with ChainGPT APIs and SDKs. The skill guides chatbot integration, NFT image creation and minting, smart contract generation and auditing, and crypto news feeds, with starter templates and usage cost estimates.
Write and review GitHub Actions workflows for syntax and structural correctness. Check quoting around expressions and special characters, then validate with actionlint, a workflow checker, to catch errors that ordinary YAML parsing can miss.
Create GitHub Copilot coding tasks from plain-language instructions using the GitHub CLI extension. Tasks become issues that trigger code changes and pull requests for review, with commands to list tasks, inspect details, and update instructions.
Guide a bug bounty investigation from scope checks and asset discovery to vulnerability testing, impact validation, and report writing. Covers web applications, source code, and AI features, with an emphasis on reproducible evidence of real harm.
Audit a deployed AI agent through its product interface to test tool misuse, cross-user memory leaks, and unauthorized actions. Focuses on observable actions and reproducible evidence, including whether required human confirmation can be bypassed.
Guide bug bounty work from target research and vulnerability hunting through validation and reporting. Compare authenticated users, investigate related weaknesses, and organize reproducible evidence of practical impact before preparing a submission.
Manage code changes across multiple AI agents while recording operations and their outcomes. Use previous task histories to find similar attempts, inspect successful patterns, and obtain suggested steps for implementation, code review, deployment, or merging changes.
Organize implementation work across research, planning, validation, implementation, and review agents. Each stage passes files to the next, while implementation starts with failing tests and the main conversation coordinates the workflow.
Conversational AI-first interface with minimal controls, clear outcomes, and delegated task flows for agentic workflows.
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Migrate workloads from Google Cloud Platform to AWS — plus AI and agentic workloads from any provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, migrate App Engine to Elastic Beanstalk, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform, app code, or billing exports, then clarify, design, estimate costs, generate artifacts, and collect feedback. Clarify must finish before Design, Estimate, or Generate. Do not use for: Azure migrations (see azure-to-aws), on-premises migrations, AWS-to-GCP reverse migration, general AWS architecture advice without migration intent, or GCP-to-GCP refactoring.
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OWASP Agentic Security Top 10 knowledge base for identifying, assessing, and remediating AI agent system security risks.
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Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.
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Use this skill when the user asks to check Simulink model compliance against a standard (MISRA, MAB, JMAAB, ISO, DO, IEC, EN, CERT C/CWE, AUTOSAR, Simulink Code Inspector (SLCI)), wants to run Model Advisor checks, or needs a compliance report with fix suggestions. For JMAAB/MAB, supplement deterministic checks with agentic review of uncheckable guidelines.
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Audit and improve an entire agentic-development setup, including skills, context, memory, tools, models, permissions, hooks, workflow habits, and secrets hygiene. Invoke for setup reviews, workflow grading, Goblin Mode, or questions about what to improve. Supports shallow, deep, ultra, and yolo modes. Use domain-specific skills for a single model, report, or tenant.
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Friendly, jargon-free onboarding for people new to coding agents and Power BI agentic development. Invoke for setup, prerequisites, tool installation, terminal guidance, safety questions, or explanations of models, context, prompts, tools, and environments. Use setup mode for concise installation help, explain mode for guided learning, and improve-my-agent-setup for an existing setup audit.
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Pick the right LLM for LEGAL RESEARCH & ANALYSIS — issue-spotting, rule application, case/statute analysis, memos, and multi-step agentic research. Vendor-neutral routing grounded in mid-2026 benchmarks (Vals AI LegalBench across 124 models; Harvey Legal Agent Benchmark for agentic work). Asks up to 4 quick questions (cost, speed, accuracy/stakes, privacy/jurisdiction/language), then recommends a primary model + fallback + what to avoid + what a human must verify. Use when someone asks "which model for legal research / case analysis / a memo", "best AI for legal reasoning", "route this research task", or is starting legal analysis without a fixed model.
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Determines whether a specific AI-agent action, output, recommendation, or proposed commitment remains admissible for execution or institutional reliance under current authority, delegated scope, evidence, facts, policy, risk, escalation, and revocation conditions. Use this Skill before an enterprise or regulated AI agent executes, updates records, triggers workflows, communicates externally, or before an institution relies on an agentic output in a way that creates consequence.
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Agentic eyes for Godot work: cross-platform screenshot CLI (asset/region/window/screen) to budgeted WebP, TEMP editor viewport bridge under .gdskills/, and Taste Receptor Atlas v2 (~237 micro-receptors × 0–2: hierarchy, contrast, typography×41, affordance, space, color, icon, composition, light/FX, motion-tells, anti-slop, originality, identity). Use when agents must see UI, assets, or the editor — not for in-game screenshot Autoloads. Keywords: agent vision, screenshot, WebP, visual QA, taste receptors, typography, UI review, asset sheet, editor viewport, anti-slop.
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Score a repository's agentic legibility from repo-visible evidence only. Use when Codex needs to audit how easy a codebase is for coding agents to discover, bootstrap, validate, and navigate, especially for harness-engineering reviews, developer-experience audits, repo cleanup, or before/after comparisons after improving docs, tooling, or architectural constraints.
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