truth first
Eliminate hallucinations with an evidence-first verification framework for system state, configs, and file contents.
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THE AGENSI STORE
48 skills found
Eliminate hallucinations with an evidence-first verification framework for system state, configs, and file contents.
Design, debug, and harden AI control loops with explicit contracts and automated verification harnesses.
Turn raw agent traces and tool logs into professional production-readiness audits and remediation reports.
Cuts the back-and-forth in half. 12 rules that stop your AI from rushing, guessing, and making you repeat yourself.
Turn erratic AI tool calls into a reliable, verified, and safe execution strategy.
Enforce explicit context discipline, artifact-gated transitions, and verification evidence for AI agent workflows.
Designs and upgrades business automation systems into modular, reliable, observable, secure, low-maintenance, enterprise-grade workflows.
Reliable, health-gated autonomous operations for agents in restricted or sandboxed terminal environments.
Audit, score, and improve your AI agent skills for higher quality, lower token costs, better reliability, and marketplace success. Get actionable recommendations for prompts, instructions, tool usage, error handling, and user experience.
A reusable rubric that grades every source by type, recency, authority, independence, and corroboration, then ranks them and resolves conflicts by evidence weight.
Analyzes AI agents for performance, reliability, security, and optimization opportunities.
Audits any AI draft for unsupported claims — flags each one, grades its source, and returns a substantiation report.
Enforce security, reliability, and deployment best practices for Docker Compose files.
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."
A structured recovery framework to stop agent loops, handle malformed output, and manage autonomous error escalation.
Builds a Python FallbackProvider that chains Claude, Gemini, Qwen, and Ollama so your AI pipeline never fully stops.
Find the LLM integration code that breaks when a model blocks a response or falls back to a different model. Flags calls with no try/except or refusal branch, responses used or parsed with no guard for a blocked or empty answer, and hardcoded model ids with no fallback handling. Built for the Fable 5 era, where a high-risk call is blocked and silently falls back to Opus 4.8.
Battle-tested prompting patterns to eliminate LLM output drift. Sandwich structure, few-shot examples, history limits, retry, and token caps — 6 composable layers for production-grade agent reliability.
High-reliability Dutch content engine with a Claude-Gemini-Qwen fallback chain and template safeties.
Transform fragile AI prototypes into resilient, enterprise-ready production agents with professional hardening tools.
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.
Architect designed system degradation and choreographed failure sequences to prevent chaotic breakdowns.