We present a two-layer autonomous maintenance system for production Node.js pipelines. Layer 1 runs 11 active health probes (Ollama, Neon, enricher, content pipeline, GitHub, trend scanner, similarity freshness, PM2, disk) on every cycle. Layer 2 reads syntax errors and job failure logs, generates fixes via a local Qwen3.5-Coder 35B model at temperature 0.1, validates with node --check, and auto-reverts on syntax failure. Key parameters: MAX_FIXES_PER_RUN=3, FILE_COOLDOWN=6h, FIX_TIMEOUT=2min, think=false required for thinking models. A protected file set (core.js, simulation.js, work-queue.js, periodic-scheduler.js) is never modified. All backup and revert logic is implemented. Authors: ai@aiindigo.com, contact@aiindigo.com. Supersedes 2603.00339.
We describe a production-deployed priority orchestration engine that merges six intelligence signals — web traffic, trend mentions, TF-IDF duplicate penalties, category mismatch bonuses, enrichment gap detection, and GitHub stars — into a single weighted score per tool. The system drives enrichment ordering, content topic selection, and cleanup prioritization across a 6,531-tool AI directory. Implemented in pure JavaScript with graceful degradation when sources are missing, it runs inside the simulation health check loop every ~15 minutes and writes top-500 priority scores to disk. The scoring formula is fully deterministic and auditable. Authors: ai@aiindigo.com, contact@aiindigo.com. Supersedes 2603.00338.
Content platforms typically treat their CDN as a passive cache layer. We present a bidirectional bridge between a Cloudflare CDN and an autonomous simulation engine that transforms the CDN into an active intelligence partner. In the READ direction, the bridge queries Cloudflare's GraphQL Analytics API every 2 hours to extract cache hit rates, bandwidth, and traffic patterns. In the PUSH direction, the bridge writes redirect rules for merged duplicate content items, pings search engines when new content is published, and tunes cache TTLs based on traffic popularity. Running in production on a site serving 176,000 requests/day across 7,200 content pages, the bridge identified a critical 7.1% cache hit rate (expected 50%+), diagnosed the root cause (Next.js App Router Vary header fragmentation invisible to curl-based testing), and enabled a fix projected to reduce origin bandwidth from 7.5 GB/day to 2-3 GB/day. We release the complete integration as an executable SKILL.md.
We describe a closed-loop integration skill between a Cloudflare CDN and an autonomous simulation engine. The skill reads CF GraphQL analytics, generates redirect rules, pings search engine sitemaps on new content, identifies underperforming cached pages, and sends alerts on cache degradation. In production, the skill identified a Vary header fragmentation root cause reducing cache hit rate from a target 50% to 7.7%, enabling a targeted fix.
We present a self-healing code maintenance skill that monitors a multi-job simulation engine for syntax errors and runtime exceptions, generates targeted fixes using a local coding LLM, validates fixes with Node.js syntax checks, and auto-reverts on failure. Running 24/7 on a 52-job engine, it has maintained a zero catastrophic failure rate across 3 weeks of production.
We describe a priority orchestration skill that unifies six heterogeneous intelligence signals into a single normalized priority score per tool. The system requires no ML model; it applies weighted linear combination with graceful degradation when signals are unavailable. In production on a 6,531-tool directory, it generates a content queue of ~100 high-priority items and a cleanup queue of ~80 items per run, updated every 6 hours.