🎯 The prompt engineering tool that prevents AI from misunderstanding you. Anti-Ambiguity Interception × Web Pre-Search × Six-Tier Adaptive Grading × Information Completeness Simulation — turn a single sentence or a product document into a production-grade prompt that AI cannot misinterpret.
Install: npx skills add wlj103/super-prompt |
Trigger: "write me a prompt" / "optimize my prompt" / "forge a prompt"
- How It Works
- The Problem with Prompt Engineering
- What is Super-Prompt?
- Who Is This For?
- Quick Start
- Features
- Design Principles
- How It Compares
- super-spec × super-prompt
- FAQ
- License
When you say "write me a prompt," Super-Prompt doesn't just start writing — it runs a structured pipeline:
Your input → [Step 1: Six-Tier Grading] → picks the right funnel depth
→ [Step 2: Requirements Funnel] → digs into what you actually need
→ [Step 3: Completeness Simulation] → finds gaps before writing
→ [Step 4: Seven Gates] → locks down every ambiguity
→ [Step 5: Multi-Format Delivery] → prompt + visuals + docs
Not all users are the same. Super-Prompt auto-classifies your input and adapts:
| Tier | What You Say | What Happens |
|---|---|---|
| ① One-liner | "Write me a prompt" (no context) | Full four-layer requirements funnel |
| ② Missing params | Has direction, missing key details | Streamlined: problem discovery + assumption deconstruction |
| ③ Clear requirement | 200-500 word description, logically coherent | Confirm → Simulate gaps → "Which gaps to fill?" |
| ④ Diagnostic | "Review this prompt for issues" | Direct 18-item anti-ambiguity scan |
| ⑤ Existing prompt | Brings a prompt to optimize | Review mode + completeness simulation |
| ⑥ Complete document | Full PRD / spec / structured doc | Review mode + completeness simulation |
Key principle: For professional users (tiers ③⑤⑥), the deeper funnel is always offered, never forced.
For vague or incomplete needs, Super-Prompt uses a requirements mining funnel that fuses five industry methodologies:
| Layer | Method | What It Does | Industry Foundation |
|---|---|---|---|
| 1. Problem Discovery | STAR Model | Asks "what problem did you encounter?" not "what do you want?" | Customer Discovery (Steve Blank) |
| 2. Assumption Deconstruction | 5 Whys | Every user statement is a hypothesis, not a fact. "I want X" → "Why?" → dig to root need | 5 Whys (Toyota), Socratic Questioning |
| 3. Body Mining | Pointer Analysis | External pointers (URLs/screenshots) → open and analyze; internal pointers (vague words) → expand via questioning | Needfinding (Stanford d.school) |
| 4. Gap Discovery | Hypothesis Map | Verified vs Assumed vs Inherited → cross-reference → gap questions | Socratic Questioning, Funnel Questioning |
Core premise: Users don't say what they need — they say what they think the solution is. Every word is a pointer, not the requirement body.
Validated results:
- Chinese calendar widget: coverage 15% → 100% (16/16 requirements found)
- Pomodoro timer: 3/8 → 8/8
- Knowledge management: 3/14 → 14/14
- v2.1 calendar widget (280 words, tier ③): 4-layer funnel → completeness simulation → 3 gaps surfaced → user filled 1 → full prompt. "Better than I expected."
Before writing a single prompt word, Super-Prompt simulates a dry run and reports gaps with three markers:
| Marker | Meaning | Example |
|---|---|---|
| ✅ Confirmed | User said it, no need to ask | "磨砂半透明" (frosted translucent) |
| Agent can guess but shouldn't; guessing wrong = failure | Zoom behavior, weather API, highlight style | |
| ❌ Missing | Can't run without it; must fill | Font licensing, holiday data source |
The
Every prompt passes through seven sequential gates. No skipping, no "good enough."
| Gate | What It Does | What It Prevents |
|---|---|---|
| G0: Intent Anchoring | Classifies input into six tiers, picks the right funnel depth | Building a prompt for the wrong problem |
| G1: Role & Boundaries | Defines AI role, input/output format, hard/soft constraints | Role ambiguity, scope creep, format mismatch |
| G2: Structure Skeleton | Recommends prompt section structure; user confirms | Disorganized prompts with overlapping responsibilities |
| G3: Section-by-Section Refinement | Drafts each section, runs anti-ambiguity check before moving to next | Vague terms like "appropriate," "good," "reasonable" |
| G4: Anti-Ambiguity Audit | 18-item checklist, item by item; no empty "pass" | Ambiguity residues that survived earlier gates |
| G5: Adversarial Test | Simulates 3 ways AI could misinterpret key instructions | Prompts that work in testing but fail on edge cases |
| G6: Delivery | Final prompt + Quick Reference Card + maintenance obligations | Deploying a prompt without knowing how to maintain it |
Web search runs throughout all gates — every question is backed by domain knowledge, not assumptions.
Not just text. When the prompt involves workflows, architectures, or data flows, Super-Prompt generates supplemental outputs:
| Scenario | Output |
|---|---|
| Decision trees / branching logic | Flowcharts (Draw.io / Mermaid) |
| System architecture | Architecture diagrams |
| Data flows / processing pipelines | Data flow diagrams |
| Concept relationships | Mind maps |
| Data comparison / statistics | Charts (bar, pie, line) |
| Demo / presentation needs | Podcast audio or video |
| Formal delivery | .docx / .pptx / PDF |
Principle: If plain text isn't enough, supplement with visuals, documents, audio, or links.
Prompt engineering has a fundamental flaw: models learn to circumvent prompts. You say "enforce strict verification," and the model outputs four words: "Verification enforced successfully." You say "be detailed," and it writes 3 lines of fluff. You say "use JSON format," and it freely invents field names. You say "don't hallucinate," and it hallucinates with extra confidence.
This isn't a model intelligence problem — it's a prompt discipline problem. Traditional templates give you the what but not the how. They tell you to "be specific" without telling you what specific means, or how to verify it before the model runs.
Super-Prompt is the missing layer. It doesn't just help you write prompts — it forces you through a structured, evidence-backed process that leaves no ambiguity for the AI to exploit.
Super-Prompt is a prompt engineering framework and optimization tool designed as an Agent Skills skill. It's not a template, not a fill-in-the-blank sheet — it's a complete prompt discipline system that:
- 🔍 Pre-searches the web before you write a single word, so your prompt is backed by domain knowledge
- 🧠 Simulates information completeness before drafting — finds gaps with ✅
⚠️ ❌ markers so you choose what to fill - 🛡️ Runs 18 anti-ambiguity checks that systematically lock down every path the AI could misinterpret
- ⚔️ Adversarially tests key instructions by simulating 3 ways AI could misinterpret them
- 📋 Produces a Quick Reference Card with maintenance obligations, degradation handling, and exception protocols
The result: prompts that work the first time — whether you're writing a one-line classifier, a 500-line agent system prompt, or a multi-agent SOP.
| You are... | Super-Prompt helps you... |
|---|---|
| AI/LLM Engineer | Ship production-grade system prompts with built-in validation and anti-hallucination guards |
| Claude Code / Cursor / Copilot user | Stop wasting tokens on vague instructions; get precise, verifiable agent instructions |
| Prompt Engineer | Replace guesswork with structured anti-ambiguity checks and adversarial testing |
| Solo Developer | Get the quality of a team code review through automated anti-ambiguity checks |
| Technical Writer | Convert product requirements into structured, unambiguous AI instructions |
| AI Agent Builder | Design agent system prompts that handle edge cases, degradation, and exceptions |
npx skills add wlj103/super-promptThen trigger with any of these:
- "write me a prompt"
- "optimize my prompt"
- "forge a prompt"
Super-Prompt classifies your input into six tiers — from one-liners to complete PRDs — and adapts the funnel depth accordingly. Professional users get professional treatment: the four-layer funnel is offered, never forced.
Before writing a single prompt word, Super-Prompt simulates a dry run of your requirement and reports gaps with three markers: ✅ Confirmed,
Before asking you a single question, Super-Prompt searches the web for domain-specific knowledge, industry best practices, known edge cases, and common pitfalls. Your prompt is backed by evidence, not assumptions.
The most comprehensive anti-ambiguity system in any prompt tool. Four categories: semantic vagueness, structural gaps, boundary ambiguity, and hidden assumptions. Each of the 18 items requires evidence — no checkbox theater.
Before delivery, Super-Prompt simulates 3 ways the AI could misinterpret your key instructions. Each weak point is hardened before you ship.
Every prompt ships with a maintenance card that includes degradation scenarios, exception handling protocols, update frequency recommendations, and known failure modes.
Full Chinese and English support. SKILL.md, README, CHANGELOG, and CONTRIBUTING are all available in both languages.
Built as an Agent Skills skill. Works with any Agent Skills-compatible AI coding assistant, including Claude Code, Cursor, and Coze.
- Pre-search, don't guess — Every question backed by web search; results converted into options for injection
- Layered guidance, not interrogation — ≤4 questions per round, starting from the simplest mandatory questions
- Anti-ambiguity, not assumption — 18-item checklist, systematically lock every ambiguity path
- Evidence-driven, not opinion-driven — Design decisions backed by search evidence or industry standards
- Adversarially tested, not "looks good" — Simulate AI misinterpretations before shipping
- Deliver with insurance — Quick Reference Card + maintenance obligations + degradation and exception handling
- Output on demand, not over-deliver — Never skimp, never bloat; safety margin only increases, never decreases
| Feature | Super-Prompt | Promptfoo | dair-ai Guide | orbit-prompt |
|---|---|---|---|---|
| Approach | Structured prompt discipline | Prompt testing framework | Educational guide | Claude Code skill |
| Anti-ambiguity | ✅ 18-item checklist | ❌ | ❌ | ❌ |
| Web pre-search | ✅ Built-in | ❌ | ❌ | ❌ |
| Adversarial testing | ✅ Simulated misinterpretations | ✅ CI-integrated | ❌ | ❌ |
| Production output | ✅ Prompt + QRC + maintenance | ❌ Test results only | ❌ | ❌ |
| Best for | Writing precise prompts | Testing existing prompts | Learning prompt engineering | Quick Claude Code prompts |
Super-Prompt is the upstream of super-spec. The flow is:
User need → [super-prompt] → precise prompt document → [super-spec] → 5 engineering docs → delivery
| super-prompt | super-spec | |
|---|---|---|
| Comes first | ✅ Defines what to do and how to say it | Takes the prompt and breaks it into engineering docs |
| Problem | AI expresses vaguely, lacks structure, leaves ambiguity | AI skips steps, claims success with zero evidence |
| Solution | Prompt discipline — 7 gates, 18 anti-ambiguity items, adversarial testing | Engineering discipline — 7 gates, 5 documents, anti-corner-cutting |
| Domain | How agents should express | How agents should execute |
Together: prompt defines the contract, spec enforces the execution.
Templates give you a structure to fill in. Super-Prompt gives you a process — it actively searches, checks, tests, and validates. A template won't tell you your prompt is ambiguous; Super-Prompt's 18-item checklist will.
Yes. Super-Prompt is model-agnostic. The prompt discipline principles apply to any LLM — Claude, GPT, Gemini, Llama, or local models.
Yes — that's the Heavy mode. It's designed for complex agent instructions, multi-agent SOPs, and production-grade system prompts.
A one-page maintenance document that ships with every prompt. It tells you when the prompt might degrade, how to detect it, and what to do about it.
Yes. MIT licensed, open source, free forever.
Absolutely. See CONTRIBUTING.md for guidelines. We welcome prompt patterns, anti-ambiguity item suggestions, and adversarial testing improvements.
MIT © 2026