AI Agent Skills

use before circulating any scientific text, human- or LLM-drafted — a clarity and integrity linter: hype vocabulary, empty sentence structures, agentless prose, number d…

Use when building AI agents with agentfootprint — LLMCall, Agent, skills, RAG, memory, control flow, Swarm concepts, mock/anthropic/openai/ollama providers, tools, recor…

Abuse an LLM agent's tools/functions — coerce it to call tools with attacker-chosen args for SSRF, RCE, data exfil, or privilege abuse. Load when the target is an agent…

Bypass an LLM's safety/guardrails to make it produce restricted output or ignore its policy. Load when testing an AI product's content controls, "jailbreak", "guardrail…

Unbounded-consumption / denial-of-wallet attacks on LLM apps — force runaway tokens, cost, or latency. Load when testing an LLM product's limits/billing, on "LLM DoS", c…

Extract or steal an ML/LLM model's parameters, training data, or system prompt via query access — model stealing, membership inference, training-data extraction. Load wh…

Poison a RAG/knowledge-base pipeline so retrieved content hijacks the model (indirect prompt injection at scale) or exfiltrates data. Load when the app does retrieval ov…

Attack the ML/LLM supply chain — poisoned models, datasets, plugins, and unsafe model deserialization. Load when an app loads third-party models/weights (HuggingFace, .p…

Generate comprehensive Chinese technical tutorial documents for specific vLLM models (e.g., Qwen3-VL, DeepSeek-V3, Llama 4, InternVL3, etc.). Produces deep-dive model wa…

Guides rapidly triaging a sudden cost or latency spike affecting one specific agent workflow — scoping it, correlating it against recent changes, and applying a fast, sa…

Guides building LLM applications with LangChain's chain/agent abstractions and, for stateful multi-step agents, LangGraph's graph-based orchestration (nodes, edges, cycl…

Guides reducing token cost and response latency of LLM-based agents without degrading quality. Use when a user asks to "reduce our LLM API bill," "make the agent respond…

Guides deciding when and how to split a task across multiple cooperating LLM agents (supervisor/worker, pipeline, or debate patterns) instead of one agent with many tool…

Guides structuring system prompts, managing context window budget, and organizing what an LLM agent sees at each turn. Use when a user asks to "write a system prompt," "…

Guides designing retrieval-augmented generation (RAG) pipelines: document chunking, embedding, indexing, retrieval, and grounding LLM output in retrieved content. Use wh…

Shared payment and setup skill for fetcher.sh, a pay-per-call web-data API covering Twitter/X, TikTok, Instagram, YouTube, Reddit, Google Search, Google Maps, G