Every time you write a prompt for a coding agent, you're writing a requirements spec. You just might not realize it yet.

Your prompt has the same failure modes as any requirements document: ambiguity, missing context, implicit assumptions, conflicting constraints. And just like in software engineering, the cost of getting requirements wrong compounds downstream. A vague prompt leads to a wrong first pass, which leads to a correction cycle, which leads to more tokens burned and more time wasted.

A research team from Nanyang Technological University and East China Normal University published a paper in January 2026 that formalizes this insight into a framework called REprompt. The idea is simple but powerful: run your raw prompts through the four classical stages of requirements development before the agent ever touches them.

REprompt applies the same pipeline that software engineers have used for decades to turn stakeholder wishes into actionable specs:

1. Elicitation extracts what's actually in the prompt, both stated and unstated. Functional requirements (what must the output do?), non-functional requirements (quality, format, tone), implicit assumptions the user didn't bother writing down, ambiguities that could go either way, and the stakeholder intent behind the request (the why, not just the what).

2. Analysis is where the real work happens. The paper's own ablation study confirmed this: removing the Analysis stage caused the largest drop in output quality. This stage detects conflicts between requirements, ranks them by priority, resolves every ambiguity with a reasoned default, and draws clear scope boundaries. The key word here is decisive. No hedging. Pick the interpretation most likely to match what the user actually meant.

3. Specification takes the analyzed requirements and writes the optimized prompt. Every instruction unambiguous. Explicit constraints on what to include and exclude. Output format specified. Success criteria defined. Token-efficient: dense, no filler. The output of this stage is the prompt, ready to use.

4. Validation compares the optimized prompt against the original to catch drift. Did we preserve intent? Did we add scope the user never asked for? Did we lose anything important? Is it so prescriptive it kills creative latitude the user wanted? If issues are found, correct. If clean, pass through.

The team tested REprompt on MetaGPT (multi-agent software document generation) and YouWare (a vibe-coding platform with over 100,000 projects). Both LLM-as-a-judge and human evaluation showed consistent improvements.

User satisfaction on the YouWare platform hit 6.5/7 for tool-building prompts and 6.3/7 for game-building prompts. Consistency scores on system design documents reached 4.7/5. Every stage contributed: the ablation study showed that removing any one of the four stages degraded output, with Analysis being the most critical and Validation the least (though still measurably useful).

The token economics make sense too. You spend a little more upfront on the refinement pass, but you save on failed generations, clarification round-trips, and regeneration cycles. For complex tasks the ROI is immediate.

I turned REprompt into a lightweight agent skill: a single 84-line SKILL.md file that any agent can read and follow. No dependencies, no API calls, no build step. The agent IS the LLM, so it processes the four stages using its own reasoning.

You can see the full landing page, results, and fetch the skill at:

reprompt.qstorage.quilibrium.com

OpenCode has a native skills system that discovers SKILL.md files automatically. One command to install globally:

mkdir -p ~/.config/opencode/skills/reprompt && \
curl -fsSL https://reprompt.qstorage.quilibrium.com/SKILL.md \
  -o ~/.config/opencode/skills/reprompt/SKILL.md

Once installed, OpenCode lists it in the agent's available skills. When you mention "reprompt" in your prompt, the agent loads the skill and runs the pipeline before proceeding.

You can also set it up as a slash command for explicit invocation. Create ~/.config/opencode/commands/reprompt.md:

---
description: Run REprompt pipeline on a prompt before execution
subtask: true
---

Read the skill at ~/.config/opencode/skills/reprompt/SKILL.md
and run its full four-stage pipeline on the following prompt:

$ARGUMENTS

Then just type /reprompt make me a todo app and the agent will run your prompt through all four RE stages, show you the optimized version, and ask whether to proceed, adjust, or show the full trace.

The subtask: true flag keeps the RE processing in a child session so it doesn't pollute your main working context.

REprompt isn't needed for every prompt. If you're asking the agent to fix a typo or run a test, skip it. But for anything where you'd normally expect to iterate, where the prompt is vague, the task is complex, or the domain has implicit conventions the agent might miss, running the pipeline first will save you time and tokens.

The quick mode (documented in the skill) combines stages for simple prompts under 30 words, so even the overhead is adaptive.

The deeper takeaway from the paper is worth sitting with: if you're spending real money on AI coding tools, the discipline of treating your prompts as requirements specs, not casual requests, might be the highest-leverage optimization available to you. Not a new model, not a new framework. Just being precise about what you want.

REprompt is based on "REprompt: Prompt Generation for Intelligent Software Development Guided by Requirements Engineering" by Shi et al. (2026), arXiv:2601.16507.

Skill and landing page by meta -- get in touch.

Every time you write a prompt for a coding agent, you're writing a requirements spec. You just might not realize it yet.

Your prompt has the same failure modes as any requirements document: ambiguity, missing context, implicit assumptions, conflicting constraints. And just like in software engineering, the cost of getting requirements wrong compounds downstream. A vague prompt leads to a wrong first pass, which leads to a correction cycle, which leads to more tokens burned and more time wasted.

A research team from Nanyang Technological University and East China Normal University published a paper in January 2026 that formalizes this insight into a framework called REprompt. The idea is simple but powerful: run your raw prompts through the four classical stages of requirements development before the agent ever touches them.

REprompt applies the same pipeline that software engineers have used for decades to turn stakeholder wishes into actionable specs:

1. Elicitation extracts what's actually in the prompt, both stated and unstated. Functional requirements (what must the output do?), non-functional requirements (quality, format, tone), implicit assumptions the user didn't bother writing down, ambiguities that could go either way, and the stakeholder intent behind the request (the why, not just the what).

2. Analysis is where the real work happens. The paper's own ablation study confirmed this: removing the Analysis stage caused the largest drop in output quality. This stage detects conflicts between requirements, ranks them by priority, resolves every ambiguity with a reasoned default, and draws clear scope boundaries. The key word here is decisive. No hedging. Pick the interpretation most likely to match what the user actually meant.

3. Specification takes the analyzed requirements and writes the optimized prompt. Every instruction unambiguous. Explicit constraints on what to include and exclude. Output format specified. Success criteria defined. Token-efficient: dense, no filler. The output of this stage is the prompt, ready to use.

4. Validation compares the optimized prompt against the original to catch drift. Did we preserve intent? Did we add scope the user never asked for? Did we lose anything important? Is it so prescriptive it kills creative latitude the user wanted? If issues are found, correct. If clean, pass through.

The team tested REprompt on MetaGPT (multi-agent software document generation) and YouWare (a vibe-coding platform with over 100,000 projects). Both LLM-as-a-judge and human evaluation showed consistent improvements.

User satisfaction on the YouWare platform hit 6.5/7 for tool-building prompts and 6.3/7 for game-building prompts. Consistency scores on system design documents reached 4.7/5. Every stage contributed: the ablation study showed that removing any one of the four stages degraded output, with Analysis being the most critical and Validation the least (though still measurably useful).

The token economics make sense too. You spend a little more upfront on the refinement pass, but you save on failed generations, clarification round-trips, and regeneration cycles. For complex tasks the ROI is immediate.

I turned REprompt into a lightweight agent skill: a single 84-line SKILL.md file that any agent can read and follow. No dependencies, no API calls, no build step. The agent IS the LLM, so it processes the four stages using its own reasoning.

You can see the full landing page, results, and fetch the skill at:

reprompt.qstorage.quilibrium.com

OpenCode has a native skills system that discovers SKILL.md files automatically. One command to install globally:

mkdir -p ~/.config/opencode/skills/reprompt && \
curl -fsSL https://reprompt.qstorage.quilibrium.com/SKILL.md \
  -o ~/.config/opencode/skills/reprompt/SKILL.md

Once installed, OpenCode lists it in the agent's available skills. When you mention "reprompt" in your prompt, the agent loads the skill and runs the pipeline before proceeding.

You can also set it up as a slash command for explicit invocation. Create ~/.config/opencode/commands/reprompt.md:

---
description: Run REprompt pipeline on a prompt before execution
subtask: true
---

Read the skill at ~/.config/opencode/skills/reprompt/SKILL.md
and run its full four-stage pipeline on the following prompt:

$ARGUMENTS

Then just type /reprompt make me a todo app and the agent will run your prompt through all four RE stages, show you the optimized version, and ask whether to proceed, adjust, or show the full trace.

The subtask: true flag keeps the RE processing in a child session so it doesn't pollute your main working context.

REprompt isn't needed for every prompt. If you're asking the agent to fix a typo or run a test, skip it. But for anything where you'd normally expect to iterate, where the prompt is vague, the task is complex, or the domain has implicit conventions the agent might miss, running the pipeline first will save you time and tokens.

The quick mode (documented in the skill) combines stages for simple prompts under 30 words, so even the overhead is adaptive.

The deeper takeaway from the paper is worth sitting with: if you're spending real money on AI coding tools, the discipline of treating your prompts as requirements specs, not casual requests, might be the highest-leverage optimization available to you. Not a new model, not a new framework. Just being precise about what you want.

REprompt is based on "REprompt: Prompt Generation for Intelligent Software Development Guided by Requirements Engineering" by Shi et al. (2026), arXiv:2601.16507.

Skill and landing page by meta -- get in touch.