codex memory curator
by servrox
Audit, prune, and secure your AI agent's long-term memory to prevent pollution and data leakage.
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THE AGENSI STORE
146 skills found
by servrox
Audit, prune, and secure your AI agent's long-term memory to prevent pollution and data leakage.
A structured recovery framework to stop agent loops, handle malformed output, and manage autonomous error escalation.
Drastically reduce RAG costs and latency while improving retrieval accuracy through advanced memory architecture.
An advanced FinOps engine to analyze AI usage, optimize token spend, and reduce LLM costs by up to 60%.
by Nex AI
Builds a Python FallbackProvider that chains Claude, Gemini, Qwen, and Ollama so your AI pipeline never fully stops.
Lint the function-calling tool definitions your agent exposes. Flags tools with no description, parameters missing a description or a type, overlapping or near-duplicate tools, too many tools for reliable selection, an unsafe tool exposed without a guard, required parameters missing from the schema, and free-form parameters that should be bounded with an enum. Cleaner tool schemas mean an agent that picks the right tool.
Builds a complete LLM evaluation framework — quality dimensions, a golden dataset, code-based and model-graded rubric graders, judge calibration, and CI regression rules. Use when the user says build LLM evals, create a golden dataset, or set up LLM-as-judge. Do not use when they want to debug one bad model output, not build a repeatable measurement system.
Paste any AI output. Get the production-ready prompt that made it.
Produces a diagnosed and rewritten prompt — component-level failure analysis, structure fixes, few-shot examples, and a regression case set. Use when the user says "fix my prompt", "why does this prompt keep failing", "improve my system prompt", "reduce hallucinations in this prompt", or "my prompt isn't following instructions". Do not use when the request is choosing which model to use, not fixing wording.
Cost-aware execution planning for AI agents — estimate cost-vs-value before expensive steps, propose cheaper paths (cache, summarize once, downshift models), and track spend against a session budget with a PROCEED / OPTIMIZE / DEFER verdict.
by Joeri Brons
Analyzes your agent conversation history to find and automate your most frequent recurring tasks.
by Nex AI
Production-ready Claude API integration with dual-model routing, token tracking, and resilient retry logic.
by GTDataworks
Convert loose prompt sets into structured, target-ready records with variables, contracts, and eval cases.
Hardens AI prompts and agent workflows against logic errors, tool-misuse, and prompt injection.
Find the LLM integration code that will not survive a provider being pulled or going down. Flags single-provider lock-in with no alternative, calls with no failover branch, missing timeouts, retries with no limit or backoff, no degraded-mode default, and hardcoded endpoints with no alternate. This is about the model going away, not the model declining.
Inventory every LLM model and provider your code depends on, the AI bill of materials, and flag the dependency risk. It lists each provider, model, and where it's used, then flags hardcoded model ids, single-provider dependency with no alternative, the same model referenced by different ids, model ids with no config or env indirection, and providers pinned in your manifests. Recognizes OpenAI, Anthropic, Google Gemini, and more from an editable list.
Produces a RAG system design, retrieval failure diagnosis, or institutional-memory schema — architecture decisions, hybrid search strategy, and a readiness checklist. Use when the user says "design our RAG system", "debug our retrieval accuracy", "fix RAG hallucinations", "choose a vector database", or "build an institutional memory schema". Do not use when the request is fine-tuning the base LLM itself.
Messy, insecure, unfixable — that's what AI builds without architecture. This file is the architecture: 10 years of senior judgement on rendering, caching, security and SEO, so your agent builds it right from day one.
Adversarial memory audit to remove PII, stale facts, and injected instructions from agent storage.
Produces a diagnosis of why a RAG system gives confident-but-wrong answers, or picks between vector search, knowledge graph, and structured/temporal retrieval. Use when the user says "why is our RAG hallucinating", "diagnose this failing query", "should we use a knowledge graph", "pick a retrieval architecture", or "design our memory layer". Do not use when the request is building a RAG system from zero.
by Al1as
A high-discipline decision-governance layer to prevent AI agents from acting on poor evidence or conflicting goals.
by SkillForge
Stop fragile agent chains with structured, versioned, and idempotent handoff contracts for multi-agent systems.
by Roy Yuen
Transform high-level goals into autonomous Plan-Build-Run-Learn iteration loops with persistent workspace learning.
by Ifásola
Diagnose RAG bottlenecks with precision metrics (Recall, MRR, nDCG) to identify retrieval or ranking failures.