Automatically process user prompts through a custom prompt engineering binary on submission and session stop, enabling iterative refinement and optimization of prompts.
RePrompter is a prompt engineering skill for AI coding agents. It takes rough, low-quality prompts and transforms them into structured, high-scoring prompts that produce dramatically better results. Templates are aligned with 2026 vendor guidance: clear sectioning, calibrated emphasis, outcome-first instructions, load-bearing constraints, structured-output routing, context budgeting, and tool-description quality. Works with Claude Code, OpenClaw, Codex, Grok CLI, Hermes Agent, or any LLM that accepts structured prompts.
Five Output Lanes
Lane
What it does
Trigger
Single
Interview, structure, score one prompt
reprompt this, clean up this prompt
/goal preflight
Codex CLI, Claude Code CLI v2.1.139+, or Hermes Agent: infer intent, build the expanded prompt, then compress it into /goal <summary of expanded prompt>
before /goal, for /goal, Codex /goal, Claude Code /goal, Hermes /goal, /goal preflight
Repromptverse
Plan a team of 2-5 agents, reprompt each one, execute in parallel, evaluate, retry
reprompter teams, repromptverse, smart run
Reverse
Show a great output, extract the prompt DNA that produced it
reverse reprompt, learn from this, prompt dna
Workflow preflight
Compile a reprompted task into a runnable Claude .workflow.js (pure-literal meta, schema returns, bounded retry; ultracode adds adversarial verify + completeness critic); also Repromptverse Option H
compile to workflow, workflow preflight, build a workflow script, dynamic workflow
Before / After
Input: "uhh build a crypto dashboard, maybe coingecko data, add caching, test it too"
Score: 1.6/10
Output: Structured XML prompt with role, context, task, requirements,
constraints, output format, and success criteria
Score: 9.0/10 (+462%)
See the full generated prompt
<role>
Senior full-stack engineer specializing in real-time dashboard systems,
REST API integration, and cache-aware data pipelines.
</role>
<context>
- Frontend + backend + tests required (cross-layer scope)
- Data source: CoinGecko API (free tier, 30 calls/min rate limit)
- Existing API behavior must be preserved
</context>
<task>
Deliver a real-time crypto dashboard backed by a cache-aware CoinGecko proxy API,
with unit tests for both API and UI, without breaking existing API contracts.
</task>
<requirements>
- Build dashboard UI with loading, error, empty, and stale-data states
- Implement backend CoinGecko proxy with JSON schema validation + configurable cache TTL
- Preserve backward compatibility for all existing API consumers
- Add deterministic unit tests for frontend rendering states and backend edge cases
- Cache must serve stale data on upstream failure (stale-while-revalidate pattern)
</requirements>
<constraints>
- No direct client-side calls to CoinGecko (all traffic through proxy)
- No breaking changes to existing API response fields or status codes
- Mock all external network boundaries in tests
- Rate limit CoinGecko calls to stay within free tier (30/min)
</constraints>
<output_format>
- Backend: /api/prices endpoint returning { prices: [...], cached: bool, updatedAt: ISO }
- Frontend: React component with 5s auto-refresh interval
- Tests: Vitest suite with >=80% branch coverage
</output_format>
<success_criteria>
- Dashboard auto-updates every 5s and shows "stale" indicator when cache is old
- Proxy returns normalized data within 200ms (cache hit) / 2s (cache miss)
- Existing API integration tests still pass with zero modifications
</success_criteria>
Claude Code plugin that uses skill architecture to intercept vague prompts, ask clarifying questions, and return structured framework-aware prompts that has credit saving patterns built in.
Intelligent prompt optimization: injects the right context at the right moment so Claude lands a better first output. Clarifies vague prompts with research-based questions, plus targeted nudges for approach selection, plan readability, workflow routing, background execution, subagent routing, output readability, user-decision questions, and plan-mode assessment