@sickn33
chaos is order yet undeciphered zSUVXjYGMTwqSCgQ9GNiji2sBHFvYjB9U836chbpump
319 skills1 repository 48.3K
Design and implement LLM applications, AI agents, and RAG systems that retrieve documents to support answers. Cover model selection, search quality, testing, monitoring, safety measures, and cost controls for applications intended for production use.
Inventory local skills within a requested scope, rank candidates against a task, and prepare a coordination plan showing their order and data flow. Project assignments help prioritize relevant skills without automatically launching agents or executing skills.
Organize supplied health records, including sleep, exercise, nutrition, and test results, to describe trends, relationships, and risk factors. Prepare general health information, summaries, and questions for a doctor, with clear limits on medical interpretation.
Define security controls for AI coding agents used by a team. Covers command and file permissions, secret scanning, isolated execution environments, code review gates, and audit trails, with configuration examples to adapt.
Configure permission checks for unattended Antigravity CLI runs. The setup combines blocking rules, automatic approval for scoped operations, AI classification for ambiguous calls, and single-use approval tokens to control tool access within a verified workspace.
Delegate a clearly scoped coding task to Google Antigravity CLI when explicitly requested. Provides a workflow for preparing instructions, reading the implementer’s report, reviewing changes, rerunning project checks, and committing verified work.
Generate realistic photo-style images with Google AI Studio for social posts and educational materials. Adjust aspect ratios, lighting, settings, and subtle imperfections to create a natural smartphone-photo look suited to each use.
Helps design focused AI products built on APIs such as OpenAI and Anthropic. Covers product strategy, prompts, model selection, user experience, usage tracking, and cost management for tools aimed at solving specific customer problems.
Hand a scoped coding task to Aider while the coordinating agent reviews changes, runs project checks, and commits the result. Covers task briefs, execution tracking, and follow-up edits, but the executable helper is not bundled.
Design tools that AI agents can understand and call, covering input schemas, clear descriptions, validation, and error handling. The guidance includes JSON Schema, a format for defining inputs, and MCP, a protocol for connecting agent tools.
Plan security controls for LLM applications and AI agents, covering malicious instructions, data leakage, model tampering, and API access. Includes guidance and code examples for filtering outputs, enforcing permissions, isolating networks, and recording audit events.
Plan AI application development by selecting relevant phases and companion skills. The workflow covers language model integration, retrieval from documents, agents, machine learning pipelines, monitoring, and security, with prompts and quality checklists.
Plan structured adversarial tests for AI applications to assess safety bypasses, data leaks, and unauthorized tool use. Organize test prompts, record findings, and prioritize fixes using reporting guidance and an adaptable testing template.
Create, update, or audit AGENTS.md instructions using evidence from repository files and CI workflows. Preserve maintainer policies while clarifying commands, validation steps, and directory-specific rules that guide coding agents.
Create and edit Agent QA tests, test suites, and hooks, then validate their structure and tool-generated IDs before running them. Use MCP, an agent tool connection, or the CLI within a configured workspace.
Set up recurring agent tasks using a supported scheduler, cron, or an authorized external timer. Choose a suitable cadence, preserve task state, prevent overlapping runs, define stop conditions, and verify that scheduled work runs as intended.
Coordinate multiple Claude Code workers through an existing Kanban board. This guide describes task dispatch, automated checks before AI review, per-task cost tracking, human intervention, and recovery after restarts; the runtime and integrations require separate installation.
Plan AI application work across six areas: prompt evaluation, context allocation, retrieval-based answer generation, agent security, evaluation pipelines, and product strategy. This overview explains the approaches; the individual workflow packages, test cases, and CI templates are not included.
Find autonomous AI agents, frameworks, and tools through AgentFolio. Compare their capabilities, intended users, integrations, and deployment options to shortlist existing products or gather examples and ideas before designing an agent of your own.
Review GitHub Actions workflows that run AI agents, tracing how external input reaches prompts and tools. Produce findings with evidence, severity, impact, and remediation guidance through static analysis, which examines configuration without running attacks.
Create logo concepts from a brand name, industry, or reference image using Beatra’s paid service. The skill guides verified package installation, account authorization, cost approval, and review of the generated artwork.
Develop incident response procedures for LLM outages, declining answer quality, spending spikes, and unsafe outputs. Use example monitoring alerts, severity levels, recovery playbooks, and postmortem guidance to organize detection, response, and follow-up work.
Guide a scoped coding task through specification, implementation, and review. Set acceptance criteria, verification checks, iteration limits, and approval gates, then correct identified gaps until the requirements pass or a stopping condition is reached.
Build AI agents that make phone calls and send or receive SMS through the AgentPhone API. Manage phone numbers, configure voice agents and event notifications, and inspect call transcripts and account usage.
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