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+ marks — click for why it's written this way
Full size (image 1, opens in a new tab)+ 5 notes — why it's written this way
- 01
Set the right role
A vague persona is where prompt injection gets a foothold — a scoped role gives the model something concrete to refuse on behalf of.
- 02
Remove casual elements
Emoji and filler tokens are the first thing to drift when a model upgrade changes tone defaults — plain language survives version bumps.
- 03
Add reasoning and next steps
A model that states its next step is a model whose failures you can diff against a trace instead of re-reading the whole transcript.
- 04
Shorter and focused
Every extra clause is another place a tool-using agent can misread a constraint as optional.
- 05
More specific and practical
Generic examples train the model to generalize wrong — the failure only shows up on your actual schema, in production.
Redline Prompts
For you if you maintain LLM agents with real users, real tool calls and real failure modes — and you're the one paged when an agent loops, misuses a tool schema, or regresses after a model upgrade. Prototypes don't need this; something in production does, especially if you're tired of re-deriving the same diagnostic questions from scratch under deadline pressure.
one developer
For one developer. Use every prompt in your own projects, commercial included.
Get Solo — $39(opens in a new tab)up to 10 seats
One flat price for up to 10 engineers in one organisation — expense it without a PO or per-seat billing.
Get Team — $149(opens in a new tab)Prices are in USD. Applicable VAT or sales tax is calculated and added by Gumroad at checkout.
If you've ever asked an LLM to "write me a system prompt for a tool-using agent," you already know what comes back: plausible-sounding, but missing the refusal boundaries, ambiguity handling, and output contracts that actually hold up once real traffic hits it. Redline Prompts is 10 categories of prompts written for the specific, recurring problems in running agents in production that are hard to prompt well on the first try. Each one is built to pull structured, adversarial, root-cause-level output from whatever model you're already using — the way a senior engineer reviews your work, not the way a first draft reads. It's prompts only: a fast, cheap way to get better output today, not a substitute for the harness and evals you'll eventually want wired into CI.
- Guarantee
- 14-day money-back guarantee. Email maksim344551@gmail.com within 14 days of purchase and we'll refund your full order through Gumroad, no questions asked.
- Delivery
- Instant download from your Gumroad library right after purchase. Updates to a pack you own are free and arrive the same way: re-download it there for the latest version.
- Release
- v2026.09 · updated 2026-09-14
01What's inside
- Agent System Prompt Architecture (5) — role definition, tool-use policy, refusal boundaries, output contracts, ambiguous-instruction handling
- Eval & Regression Test Design (5) — eval datasets, grading rubrics, adversarial and edge-case inputs
- Agent Failure Diagnosis (5) — root-causing a wrong, unsafe, or looping output from a transcript or trace
- Tool & Function Schema Review (5) — auditing a schema for ambiguity, missing constraints, and over-broad scope
- Prompt Injection & Security Hardening (5) — red-teaming a system prompt, RAG pipeline, or tool surface
- Cost & Latency Optimization (5) — model-routing, caching, and prompt-compression strategy from a token breakdown
- Model Migration & Version Upgrade (5) — diffing behavior on a fixed eval set, flagging prompts likely to regress
- RAG Retrieval Quality Debugging (5) — chunking-strategy critique, query rewriting, embedding and reranking diagnostics
- Multi-Agent Orchestration Design (5) — dividing work, handing off context, recovering when a subagent fails
- Production Incident Postmortems for AI Systems (5) — timeline, root cause, blast radius, follow-up actions with owners
02Why it holds up
- Every prompt went through the same four-stage process: drafted against a category brief, refined against "would a professional pay for this," scored on specificity, actionability and uniqueness, then adversarially reviewed by three separate AI reviewer passes — independent model runs, each briefed to argue against including it. A prompt needed at least two of three "include" votes to survive.
- 6 of the original 56 drafted prompts were rejected at the adversarial stage and replaced before this pack shipped — nothing here is a first draft.
- Each prompt ships with the actual prompt text, when to reach for it, an example scenario, a sample of what a strong response looks like, notes on adapting it to your stack, and which model to run it on.
- Built for teams already maintaining agents with real users and real failure modes — not prototyping, and not a general-purpose prompt library.
How it's made The packs are drafted and reviewed with AI models and curated by the author. More on the About page.
03What this is not
- No code, working eval harness, safety guardrails, cost-control logic, or observability instrumentation — this is prompts only.
- No CI integration or done-for-you evals — you still wire those into your own stack.
- Not a substitute for a working eval harness wired into CI.
Going further
Redline audits what you already have. Baseline Presets gives you a solid prompt to start from, and Press Check grades the output at scale. An Agent Harness + Evals Kit that wires all of it into CI is in development; it is not available yet.
04Questions about Redline Prompts
01What exactly do I get after buying?
An instant download from your Gumroad library: redline-prompts-pack.md — 50 prompts across 10 categories, each with the prompt text, when to use it, an example scenario, a sample strong response, notes on adapting it to your stack, and which model to run it on. Updates are free and arrive the same way: re-download from your library.
02What's the difference between Solo and Team?
Solo is licensed to one developer. Team covers up to 10 seats in one organisation at one flat price — no per-seat billing, easy to expense without a PO. Both get the same pack.
03What if a prompt does not earn its keep?
Ask for your money back within 14 days of purchase. The steps are in the guarantee box on this page and in the Terms: the refund goes through Gumroad, no questions asked.
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06More packs
Baseline Presets
15 production system prompts for the agents every team ends up building — support, docs Q&A, SQL analyst, code review, intent router, extractor and 9 more — each with tool schemas, an output contract and 12 contract tests.
Press Check
15 ready-to-run LLM judges for agent output — groundedness, tool-call provenance, side-effect confirmation, injection compliance, refusal calibration and 10 more — each with anchored labels, strict JSON output and a gold set.