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Agent skill

Backend Mvp Guardrails

by victorGPT in victorGPT/vibeusage

A skill your agent uses when designing or reviewing a backend MVP with tight budget, evolving schema, and reliance on third-party backends where idempotency, replay, and responsibility attribution…

MITAuto-check passedAI & LLM Engineering

Install Backend Mvp Guardrails

skills CLI
$ npx skills add victorGPT/vibeusage --skill backend-mvp-guardrails -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install victorGPT/vibeusage backend-mvp-guardrails --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/victorGPT/vibeusage.git skills-src && mkdir -p .claude/skills && cp -r skills-src/docs/skills/backend-mvp-guardrails .claude/skills/backend-mvp-guardrails && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
backend-mvp-guardrails
GitHub stars
130
Token cost
~1.3k tokens
SKILL.md length
466 words
Files
1
Skills in repo
6
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when designing or reviewing a backend MVP with tight budget, evolving schema, and reliance on third-party backends where idempotency, replay, and responsibility attribution…

  • Works in 6 steps: Source of truth is immutable or… → Idempotent writes. Deterministic keys +… → Replayable aggregates. Derived tables… → …
  • Reviewing a backend MVP with tight budget
  • SKILL.md covers Overview, When to Use, Core Pattern (Two Layers) and Responsibility Attribution…, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Backend Mvp Guardrails is an agent skill from victorGPT/vibeusage. Use when designing or reviewing a backend MVP with tight budget, evolving schema, and reliance on third-party backends where idempotency, replay, and responsibility attribution are high-risk.

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering LLM guardrails. The licence is MIT.

When your agent uses it

  • Reviewing a backend MVP with tight budget
  • Evolving schema
  • Reliance on third-party backends where idempotency
  • Responsibility attribution are high-risk

Example prompts

  • “/backend-mvp-guardrails”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Source of truth is immutable or append-only. Avoid online recomputation on read paths.
  2. Idempotent writes. Deterministic keys + upsert or unique constraint.
  3. Replayable aggregates. Derived tables can be rebuilt from the source of truth.
  4. Evidence-first attribution. No structured evidence, no blame, no destructive fix.
  5. Cost-first queries. Pre-aggregate, cap ranges, enforce limits, avoid full scans.
  6. Schema evolution is additive. New fields are optional and versioned; unknown fields are rejected by allowlist.

What it can do on your machine

Read from SKILL.md and the folder at commit 4891a58. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are javascript).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Backend Mvp Guardrails loads about 1.3k tokens when it runs. Until then it costs about 54 tokens; SKILL.md has 466 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~54
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from victorGPT/vibeusage at commit 4891a58, republished under its MIT licence (© victorGPT). 466 words, ~1,321 tokens.

Download SKILL.mdSave it as .claude/skills/backend-mvp-guardrails/SKILL.md (or your agent's skills folder).
name
backend-mvp-guardrails
description
Use when designing or reviewing a backend MVP with tight budget, evolving schema, and reliance on third-party backends where idempotency, replay, and responsibility attribution are high-risk.

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)
  1. Source of truth is immutable or append-only. Avoid online recomputation on read paths.
  2. Idempotent writes. Deterministic keys + upsert or unique constraint.
  3. Replayable aggregates. Derived tables can be rebuilt from the source of truth.
  4. Evidence-first attribution. No structured evidence, no blame, no destructive fix.
  5. Cost-first queries. Pre-aggregate, cap ranges, enforce limits, avoid full scans.
  6. 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_status is 5xx/timeout and function status is 5xx => likely vendor/backbone issue
  • upstream_status is 2xx and function status is 4xx/5xx => likely application validation/logic issue
  • latency_ms high and upstream_latency_ms low => likely application-side bottleneck

Stop rule: no data rewrite, schema change, or semantic patch without a replay plan and rollback.

Show full SKILL.md (175 more words)Show less

Quick Reference

GuardrailWhyMinimum Implementation
Idempotent writesPrevent double-countingUnique key + upsert
Replayable aggregatesSafe fixesSource-of-truth table + backfill job
Cost capsFit low budgetRange limits + pre-aggregates
Evidence-firstAvoid misfixM1 structured logs
Schema allowlistAvoid data bloatReject unknown fields

Implementation Example (Structured Log)

js
const start = Date.now();
const requestId = crypto.randomUUID();
const log = (entry) =>
  console.log(
    JSON.stringify({
      request_id: requestId,
      function: "example-function",
      ...entry,
    }),
  );

try {
  const upstreamStart = Date.now();
  const res = await fetch(upstreamUrl);
  const upstreamLatency = Date.now() - upstreamStart;

  log({
    stage: "upstream",
    status: res.status,
    upstream_status: res.status,
    upstream_latency_ms: upstreamLatency,
    latency_ms: Date.now() - start,
    error_code: res.ok ? null : "UPSTREAM_ERROR",
  });
} catch (err) {
  log({
    stage: "exception",
    status: 500,
    upstream_status: null,
    upstream_latency_ms: null,
    latency_ms: Date.now() - start,
    error_code: "UPSTREAM_TIMEOUT",
  });
  throw err;
}

Common Mistakes

  • Online aggregation in dashboard endpoints under low budget
  • Adding new data sources without updating idempotency keys
  • Blame without upstream_status evidence
  • Storing full payloads "just in case" (privacy and cost risk)
  • Changing data semantics without replay/backfill plan

Rationalization Table

ExcuseReality
"We are a tiny team, logs are overkill"Small teams need stronger evidence, not weaker.
"Vendor is unstable, we cannot know"You still need M1 logs to avoid misfixes.
"Budget is low so scans are fine"Low budget means scans fail sooner.
"We can patch the numbers"Patches without replay create permanent drift.

Red Flags - STOP

  • No structured logs but attempting responsibility attribution
  • Data rewrite without replay/backfill plan
  • Dashboard reads from raw event tables
  • Unknown fields stored without allowlist
  • Idempotency key not updated when adding dimensions

© victorGPT, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in docs/skills/backend-mvp-guardrails of victorGPT/vibeusage.

Open the folder on GitHubat commit 4891a58

Compare with similar skills

Backend Mvp Guardrails next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Backend Mvp Guardrails compared with similar skills
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Backend Mvp Guardrails this skillvictorGPT/vibeusage130—~1.3kAutomated safety check: PassMIT
Aisafetyhotwuyoscar/AISafetyHot-Hub827—~1.4kAutomated safety check: PassCustom licence
ObliteratusRedWoodOG/Hermes-Desktop1775 repos~3.8kAutomated safety check: PassMIT
Lemonade Router Builderamd/skills408—~4kAutomated safety check: PassMIT
Writing Eval Scenariosopen-bias/open-bias143—~1.5kAutomated safety check: PassApache-2.0
Wp Project Triagegambitph/Stackable3513 repos~371Automated safety check: PassGPL-3.0

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Categories

Questions about Backend Mvp Guardrails

What does Backend Mvp Guardrails do?

A skill your agent uses when designing or reviewing a backend MVP with tight budget, evolving schema, and reliance on third-party backends where idempotency, replay, and responsibility attribution…. Backend Mvp Guardrails is an agent skill from victorGPT/vibeusage. Use when designing or reviewing a backend MVP with tight budget, evolving schema, and reliance on third-party backends where idempotency, replay, and responsibility attribution are high-risk.

When should I use Backend Mvp Guardrails?

Backend Mvp Guardrails fits situations like: reviewing a backend MVP with tight budget; evolving schema; reliance on third-party backends where idempotency; responsibility attribution are high-risk.

How do I install Backend Mvp Guardrails in Claude Code?

Run `npx skills add victorGPT/vibeusage --skill backend-mvp-guardrails -a claude-code`. Or copy the skill folder (docs/skills/backend-mvp-guardrails in victorGPT/vibeusage) into .claude/skills/backend-mvp-guardrails in your project. Claude Code loads it when a task matches its description.

How do I install Backend Mvp Guardrails in Codex?

Run `npx skills add victorGPT/vibeusage --skill backend-mvp-guardrails -a codex`. Or copy the skill folder (docs/skills/backend-mvp-guardrails in victorGPT/vibeusage) into .agents/skills/backend-mvp-guardrails in your project. Codex loads it when a task matches its description.

Can I use Backend Mvp Guardrails in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add victorGPT/vibeusage --skill backend-mvp-guardrails -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/backend-mvp-guardrails, .gemini/skills/backend-mvp-guardrails, .github/skills/backend-mvp-guardrails and .opencode/skills/backend-mvp-guardrails in your project.

What does Backend Mvp Guardrails need to run?

SKILL.md names no scripts, command-line tools or credentials: Backend Mvp Guardrails is instructions for the agent only.

Does Backend Mvp Guardrails access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Backend Mvp Guardrails safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Backend Mvp Guardrails use?

Backend Mvp Guardrails is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Backend Mvp Guardrails use?

About 1.3k tokens (SKILL.md is roughly 5.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Backend Mvp Guardrails?

Skills that share tags, products or a category with Backend Mvp Guardrails: Aisafetyhot (wuyoscar/AISafetyHot-Hub, 827 stars), Obliteratus (RedWoodOG/Hermes-Desktop, 177 stars), Lemonade Router Builder (amd/skills, 408 stars) and Writing Eval Scenarios (open-bias/open-bias, 143 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Backend Mvp Guardrails?

victorGPT (a GitHub user) maintains it in victorGPT/vibeusage, which has 130 GitHub stars. The repository holds 6 skills in this directory. The repository was last updated on July 31, 2026.

Source: victorGPT/vibeusage on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.