agent tool governance
by LocoLoboZ
A proactive governance layer that validates MCP tool intent and scope to ensure safe, compliant agent behavior.
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THE AGENSI STORE
160 skills found
by LocoLoboZ
A proactive governance layer that validates MCP tool intent and scope to ensure safe, compliant agent behavior.
by Ryan lyell
The intelligent installer for MARM, providing cross-agent persistent memory and shared context via MCP.
Lint a prompt template for the issues that cause injection and flaky output. Flags untrusted variables interpolated straight into the instructions (the injection surface), placeholders that are never provided or never used, contradictory instructions, a missing output-format spec where the result is parsed, unbounded context interpolation, and leftover placeholders. It detects problems; it does not write prompts.
A structured recovery framework to stop agent loops, handle malformed output, and manage autonomous error escalation.
by servrox
Audit, prune, and secure your AI agent's long-term memory to prevent pollution and data leakage.
Drastically reduce RAG costs and latency while improving retrieval accuracy through advanced memory architecture.
by LocoLoboZ
A structured governance auditor to optimize AI project instructions, clean up context, and manage workspace health.
One-line summary description Stop your agent from claiming "done" before it's proven. A verification gate that classifies each change by risk (payment, auth, database, user-facing), picks the tests that actually cover it, demands evidence, maps regression risk, and outputs an honest pass/fail report. Turns "looks good to me" into "here's what I ran, and here's what's still unverified."
An advanced FinOps engine to analyze AI usage, optimize token spend, and reduce LLM costs by up to 60%.
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.
by Nex AI
Maintain 100% uptime with an automated LLM fallback chain that routes from high-tier APIs to local models.
You changed the prompt, tried four inputs, it looked better, you shipped — and three days later support tickets say outputs are worse for an entire class of inputs you didn't test
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.
Paste any AI output. Get the production-ready prompt that made it.
Production prompts grow by accretion — every failure gets another appended rule until the prompt is two thousand words of contradictions that the model navigates unpredictably
by Nex AI
Production-ready Claude API integration with dual-model routing, token tracking, and resilient retry logic.
by Corey Jacobs
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.
Design, diagnose, and optimize high-performance RAG systems with an engineering-first framework.
by Timoranjes
Evaluate third-party agent skills for command injection, prompt injection, and data exfiltration before installation.
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.
Adversarial memory audit to remove PII, stale facts, and injected instructions from agent storage.
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.
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.