I got tired of AI writing sloppy boilerplate, so I built constraint-based prompt packs

Shane McGuirt — Wed, 12 Aug 2026 13:33:27 +0000

We all hit the wall eventually: same model, same question, wildly different output quality.

One day Claude hands me a Terraform config my code review would actually approve. The next day it's hardcoded secrets and containers running as root. Same model. Same session, even. The only thing I changed was the prompt.

That was the realization — the model wasn't the variable. The prompt was. And when you treat an LLM as a generalist, you get generalist-quality output. "Revolutionize your workflow" landing pages. Five marketing emails that all say the same thing. A Dockerfile with latest pinned and a --privileged flag because it "just worked locally."

The fix: negative constraints over positive ones

The standard advice is to add more positive constraints: "be concise," "follow best practices," "be accurate." I tried this. It didn't move the needle consistently.

What actually worked was the opposite: hard negative constraints that eliminate entire failure modes.

Here's the real constraint block from the DevOps prompt pack:

NEVER use floating image versions; pin + reference immutable tags
NEVER hardcode secrets; read from environment only
NO local state; remote state with locking required
Least-privilege IAM only; no wildcard actions or roles
Output plan-first for review; never auto-apply to production

The difference is specificity. "Be secure" is vague — the model already "knows" to be secure and interprets it differently every run. "NEVER hardcode secrets" eliminates a concrete failure mode. The output space narrows.

Running this against Terraform generation tasks, the hardcoded-secrets failure dropped to near-zero. With a positive-only prompt, it appeared roughly 30% of the time.

What I built

I packaged this approach into 9 MIT-licensed tools:

Prompt packs (8 specialist system prompts each, plain Markdown):

DevOps Prompt Pack ($29) — Terraform, K8s, CI/CD, cloud ops
Marketing Prompt Pack ($29) — SEO, email, ads, landing pages
Product Mgmt AI Prompt Pack ($49) — PRDs, user stories, sprint planning
Prompt Template Library (free core / $35 full) — 32 fill-in-the-blank prompts + Node.js fill library

Code boilerplates (MIT, Docker, tests included):

MCP Server Starter Kit (free core / $49 full) — TypeScript MCP server with rate limiting + Zod validation
Claude Agent Boilerplate ($29) — tool-use loop with parallel calls, conversation memory, token tracking
Multi-Agent Orchestration Kit ($79) — pipeline, fan-out, supervisor patterns
MCP Vertical Server Bundle ($99) — GitHub, Slack, Notion MCP servers with Docker Compose
FastMCP Python Boilerplate ($35) — Pydantic v2, token-bucket rate limiter, structlog to stderr

The MCP gotcha I keep seeing

If you're building MCP servers and hitting silent failures or parse errors, check your logging first.

MCP uses stdout for JSON-RPC frames. Any print() or console.log() that lands on stdout becomes part of the protocol framing — the client sees malformed JSON and either drops the message or errors out. All logging goes to stderr:

import structlog, sys
structlog.configure(logger_factory=structlog.PrintLoggerFactory(file=sys.stderr))

The FastMCP boilerplate has this wired by default. I've seen developers spend hours on this.

What's free

The free cores are genuinely free — not a page-trap:

MCP Server Starter Kit (TypeScript + rate limiting + Docker)
Prompt Template Library (32 prompts + Node.js library)
Claude Agent Boilerplate (tool-use loop core)
Multi-Agent Orchestration Kit (pipeline + fan-out patterns)

Clone them, use them in production, keep them if you never buy anything.

Honest limitations

These aren't magic. They're constraint-based prompts tested across Claude 3.5/4 and GPT-4o, mostly on codegen and infra tasks. The patterns may not transfer equally to every domain. The approach reduces variance — it doesn't eliminate it.

If you find a constraint that belongs in a pack, I want to know. The whole point is that these improve with real production feedback.