The Silent Token Burn in AI Coding Agents
Modern AI coding agents—such as Claude Code, OpenCode, and Cursor—are astonishingly fast at generating boilerplate. However, when instructed to implement critical backend systems (such as multi-tenant authorization, distributed rate limiting, or file uploads), they routinely produce naive implementations:
- Token Exhaustion via Re-Prompting: Developers spend 15 to 20 iterative prompts correcting subtle bugs, hallucinated imports, and edge-case omissions.
- Silent Security Flaws: Agents generate rate limiters with critical Time-of-Check to Time-of-Use (TOCTOU) race conditions, or file upload handlers that trust client-supplied MIME types and lack binary magic-byte verification.
To eliminate token waste and guarantee enterprise-grade security on the first generation, I built AI Function Builder Skills—an open-source collection of structured, battle-tested instruction packs formatted as SKILL.md.
The Three Flagship Production Packs
1. Clerk Organization RBAC Matrix
Most AI agents implement role checks by querying the database on every route, adding 50–150ms of network latency. The Clerk RBAC skill pack enforces:
- 8 Distinct Organizational Roles & 44 Granular Permissions (
org:billing:manage,org:members:invite,org:audit:read). - JWT-Only Authorization: Derives permissions directly from cryptographically signed Clerk session claims with 0 database roundtrips.
- Dual Defense Architecture: Strict separation between capability verification (
Can("org:export")) and tier entitlements (PlanGate("enterprise")).
2. Redis Token Bucket Rate Limiter (Atomic Lua)
Naive rate limiters increment counters after fetching limits, allowing concurrent HTTP requests to bypass thresholds. This skill pack implements:
- Atomic Redis Lua Execution: Calculates token replenishment and consumption in a single atomic transaction with zero race conditions.
- Distributed Provider Support: Pre-configured for both self-hosted
ioredisclusters and serverless Upstash Redis on Cloudflare Workers and Vercel Edge. - Graceful Fail-Open / Fail-Closed Controls: Configurable safeguards to ensure mission-critical user traffic is never dropped during upstream cache downtime.
3. Secure Direct Cloud Storage Uploads (S3 & Cloudflare R2)
Routing gigabytes of user uploads through Next.js server functions bottlenecks bandwidth and incurs massive compute costs. This skill pack delivers:
- 5-Layer Defense Verification: File extension whitelist, Content-Type verification, client-side Web Worker image compression, EXIF metadata stripping, and server-side magic byte inspection.
- Presigned PUT URLs: Uploads flow directly from the client browser to AWS S3 or Cloudflare R2 with zero server bandwidth usage.
- Deep Magic Byte Binary Inspection: Leverages HTTP Range requests to inspect the first 4100 bytes of the uploaded file, validating actual binary signatures for JPEG, PNG, WebP, PDF, and ZIP archives.
Universal Portability Across Agents
All packs adhere to the standardized SKILL.md specification, making them instantly compatible across the entire AI agent ecosystem:
- Claude Code: Drop into
.claude/skills/or pass via system prompt. - Cursor: Reference via
.cursor/rules/or@docs. - OpenCode & Antigravity: Drop directly into the project subagent skills directory.
Live Documentation & Portal
Explore live interactive guides, copy-paste configurations, and implementation benchmarks on the official portal:
🌐 ai-function-builder-skills.axosolaman.online
🐙 github.com/axosecurity/ai-function-builder-skills