Results for “arp-spoofing”

Run autonomous optimization loops that iteratively improve artifacts against evaluators using hypothesis tree refinement, without overfitting.

Scan Model Context Protocol servers and tool metadata for poisoning, SSRF, and unauthenticated exposure.

Produces an evidence-backed inventory of live automations (jobs, hooks, connectors, MCP servers, wrappers) and recommends keep/merge/cut/fix-next actions before rewriting anything.

Runs an autonomous optimization loop that iteratively improves an artifact against an objective and evaluator using Hypothesis Tree Refinement, with subagent executors in isolated git worktrees.

Creates temporary encrypted one-time handoffs between autonomous agents, supporting mission briefs, scoped secrets, artifacts, and receipts via CLI or API.

Agent skill for pseudocode - invoke with $agent-pseudocode

Route plain-language requests for Claude Code, Cursor, Copilot, MarketingClaw ACP, OpenCode, Gemini CLI, Qwen, Kiro, Kimi, iFlow, Factory Droid, Kilocode, or explicit ACP harness work into either MarketingClaw ACP runtime sessions or direct acpx-driven sessions ("telephone game" flow). For coding-agent thread requests, read this skill first, then use only `sessions_spawn` for thread creation. Codex chat binding defaults to the native Codex app-server plugin unless ACP is explicit or background spawn needs ACP.

Perform nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows using the umap-learn library.

Model interpretability and explainability with SHAP (SHapley Additive exPlanations) — feature importance and plots (waterfall, beeswarm, bar, scatter, force, heatmap). Use when explaining ML model predictions, computing feature importance, debugging models, analyzing bias or fairness, comparing models, or implementing explainable AI across tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model. Part of the AlterLab Academic Skills suite.

Evaluates speaker verification models by computing true positive rate at fixed false positive rate thresholds, probing embedding space separation of same-speaker versus different-speaker pairs.

Agent wallet, identity, and paid tools in one package. Register an agent, fund it via Stripe or USDC, then use the balance for web search, AI image generation, AI video generation, AI music creation, X/Twitter search, email send/receive, SMS and voice calls, contacts management, and 100+ LLM models. The funding and identity layer for autonomous agents that need to spend money, send messages, make phone calls, or call paid APIs.