Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add victorGPT/vibeusage --skill backend-mvp-guardrailsgit clone --depth 1 https://github.com/victorGPT/vibeusageWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/victorgpt/vibeusage/backend-mvp-guardrails)<a href="https://agentmods.dev/skills/victorgpt/vibeusage/backend-mvp-guardrails"><img src="https://agentmods.dev/badge/skills/victorgpt/vibeusage/backend-mvp-guardrails/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/victorgpt/vibeusage/backend-mvp-guardrails"><img src="https://agentmods.dev/badge/skills/victorgpt/vibeusage/backend-mvp-guardrails.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00043 | $0.01152 |
| Opus 5 | $0.00022 | $0.00576 |
| Sonnet 5 | $0.00009 | $0.00230 |
| Haiku 4.5 | $0.00004 | $0.00115 |
Grade A, and why
backend-mvp-guardrails scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
const res = await fetch(upstreamUrl); How it starts
The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Backend MVP Guardrails
Overview
Minimize irreversible decisions. Every write must be idempotent, every aggregate must be replayable, and every incident must be attributable with minimal evidence.
When to Use
- MVP backend with single-digit USD/month budget or strict capacity limits
- Fast schema evolution or new data sources with unknown fields
- Third-party backend dependency (e.g., InsForge) with no status page or DB metrics
- Repeated ambiguity about whether failures are vendor or application issues
When NOT to use: throwaway prototypes where data loss and misattribution are acceptable.
Core Pattern (Two Layers)
Layer 1: Principle Guardrails (platform-agnostic)
- Source of truth is immutable or append-only. Avoid online recomputation on read paths.
- Idempotent writes. Deterministic keys + upsert or unique constraint.
- Replayable aggregates. Derived tables can be rebuilt from the source of truth.
- Evidence-first attribution. No structured evidence, no blame, no destructive fix.
- Cost-first queries. Pre-aggregate, cap ranges, enforce limits, avoid full scans.
- Schema evolution is additive. New fields are optional and versioned; unknown fields are rejected by allowlist.
Layer 2: Platform Mapping (InsForge example)
- Fact table: half-hour buckets (e.g.,
vibescore_tracker_hourly) - Idempotency key:
user_id + device_id + source + model + hour_start - Aggregates: derived from buckets; do not read raw event tables for dashboards
- Retention: keep aggregates longer; cap any event-level tables
- Backfill: limited window + upsert; must be replayable
- Observability: M1 structured logs (see below)
Responsibility Attribution Protocol (M1)
Required fields: request_id, function, stage, status, latency_ms, error_code, upstream_status, upstream_latency_ms
Attribution rules:
- Missing
upstream_status=> UNKNOWN (do not change data semantics) upstream_statusis 5xx/timeout and function status is 5xx => likely vendor/backbone issueupstream_statusis 2xx and function status is 4xx/5xx => likely application validation/logic issuelatency_mshigh andupstream_latency_mslow => likely application-side bottleneck
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 128 lines · 43 tokens per session scan A 728685ebfb10
backend-mvp-guardrails is a skill published in the GitHub repository victorGPT/vibeusage (131 stars, last pushed 1mo ago), licensed MIT. It adds 43 tokens to every session and 1,152 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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