multi-agent-brainstorming
Simulates structured peer review using multiple specialized agents. Validates designs and surfaces hidden assumptions and potential failure modes before implementation.
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Find your next superpower If you are an agent, refer to our llms.txt for full access.Simulates structured peer review using multiple specialized agents. Validates designs and surfaces hidden assumptions and potential failure modes before implementation.
Sets up Qwen authentication, including QWEN_API_KEY and endpoints. Helps resolve 401 errors and missing credentials reported by other skills.
Lists and triggers DAGs, retries runs, reads logs, and diagnoses failures or import errors. Checks connections, variables, pools, APIs, and health, and routes specialized authoring, testing, deployment, and migration tasks to related skills.
Crucix combines 27 OSINT data sources in a self-hosted personal intelligence dashboard. It includes LLM analysis, Telegram and Discord bots, and real-time alerts.
Accesses company information from Brønnøysundregistrene, the Norwegian Business Registry. Supports searching and retrieving data across approximately 1.2 million registered companies.
CoPaw is a personal AI assistant framework supporting DingTalk, Feishu, QQ, Discord, and other chat channels. It provides extensible skills, local or cloud deployment, and cron scheduling.
Applies threat modeling, attack surface analysis, defense in depth, and zero trust with CIA, STRIDE, and MITRE ATT&CK frameworks. Evaluates threats, controls, residual risk, incident response, security posture, and compliance.
Assesses whether an LLM evaluation system is trustworthy and surfaces gaps in its methods and metrics. Supports reviewing inherited systems or assessing where evaluation infrastructure is absent, rather than building a new evaluator.
Uses skeleton-first drafting, market-leader research, and an explicit open-questions gate to develop feature specifications. Breaks implementation into phases and steps and provides severity-ranked architectural reviews.
Guides embedding model changes, vector migration, and re-embedding in Qdrant without downtime. Covers dimension upgrades, provider changes, simultaneous models, and model A/B testing.
Learns the user's communication style, audiences, and data sources through questions and examples of existing updates. Uses that information to configure roundup briefings for the user's workflow.
Guides selection between Tigris-native SDKs and AWS S3-compatible SDKs. Covers language-specific choices, CLI preference, and cases where AWS SDKs are the only option.
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