Your prompt sucks. Let's fix that.
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. Works with Claude Code, OpenClaw, Codex, or any LLM that accepts structured prompts.
| Mode | What it does | Trigger |
|---|---|---|
| Single | Interview, structure, score one prompt | reprompt this, clean up this prompt |
| 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 |
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>| Dimension | Before | After | Change |
|---|---|---|---|
| Clarity | 3 | 9 | +200% |
| Specificity | 2 | 9 | +350% |
| Structure | 1 | 10 | +900% |
| Constraints | 0 | 8 | new |
| Verifiability | 1 | 9 | +800% |
| Decomposition | 2 | 9 | +350% |
| Overall | 1.6 | 9.0 | +462% |
Scores are self-assessed. Treat as directional indicators, not absolutes.
mkdir -p skills/reprompter
curl -sL https://github.com/aytuncyildizli/reprompter/archive/main.tar.gz | \
tar xz --strip-components=1 -C skills/repromptercp -R reprompter /path/to/workspace/skills/reprompterUse SKILL.md as the behavior spec. Templates are in references/.
reprompt this: build a REST API with auth and rate limiting
reprompter teams - audit the auth module for security and test coverage
reverse reprompt this: [paste a great output you want to reproduce]
RePrompter interviews you (2-5 questions), generates a structured XML prompt, and shows a before/after quality score.
Rough prompt → Input guard → Quick mode gate → Interview (2-5 questions)
→ Template selection → XML prompt generation → Quality scoring → Delta rewrite if < 7/10
17 templates cover feature, bugfix, refactor, testing, API, UI, security, docs, content, research, and multi-agent swarm patterns.
Phase 1: Score prompt, interview if needed, plan team, show Plan Cards → user approves
Phase 2: Write XML prompt per agent (target 8+/10), show quality scorecard
Phase 3: Execute (tmux / TeamCreate / OpenClaw / sequential fallback)
Phase 4: Show Result Cards, evaluate, retry with delta prompts if needed (max 2)
Agents get non-overlapping scopes, explicit success criteria, and file:line reference requirements. The evaluator loop ensures quality before synthesis.
Exemplar output → EXTRACT structure → ANALYZE task type + domain + tone
→ SYNTHESIZE XML prompt → Score → Optional: INJECT into flywheel
11 task type classifiers (code review, security audit, architecture doc, API spec, test plan, bug report, PR description, documentation, content, research, ops report) with 8 domain detectors and tone analysis. Solves the flywheel cold-start problem by seeding it with known-good prompt/output pairs.
Prompt Flywheel - Every prompt carries a recipe fingerprint. After execution, outcomes are captured and linked back. Future runs query historical data and recommend the best-performing strategy. All data local.
Agent Cards - Plan Cards (before execution), Status Line (during), Result Cards (after). Full transparency into what agents will do, are doing, and found.
Dimension Interview - Low-scoring prompt dimensions trigger targeted questions. No more vague prompts spawning expensive agents.
Pattern Library - 6 pluggable prompt engineering patterns: constraint-first framing, uncertainty labeling, self-critique checkpoints, delta retry scaffolds, evidence-strength labeling, context-manifest transparency.
Capability Routing - When multiple models are available, routes each agent by capability tier (reasoning, long context, cost-optimized, latency-optimized) with provider-diverse fallback chains.
npm run check # 169 tests + 4 benchmarks
npm test # individual: npm run test:reverse-engineer| Suite | Tests |
|---|---|
| Intent router | 21 |
| Reverse engineer | 43 |
| Outcome collector | 30 |
| Strategy learner | 24 |
| Recipe fingerprint | 14 |
| Repromptverse runtime | 9 |
| Capability policy | 7 |
| Pattern selector | 7 |
| Runtime adapter | 5 |
| Flywheel E2E | 5 |
| Others | 4 |
| Total | 169 |
All benchmarks at 100%: routing (64/64), artifacts (84/84), flywheel (13/13), provider (9/9).
| Capability | Claude Code | Codex | OpenClaw | Any LLM |
|---|---|---|---|---|
| Single mode | yes | yes | yes | yes |
| Reverse mode | yes | yes | yes | yes |
| Multi-agent parallel | yes | yes* | yes | - |
| Multi-agent sequential | yes | yes | yes | yes |
* Depends on runtime session availability; sequential fallback is automatic.
// ~/.claude/settings.json
{
"env": {
"CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1"
},
"preferences": {
"model": "opus"
}
}Feature flags: REPROMPTER_FLYWHEEL, REPROMPTER_POLICY_ENGINE, REPROMPTER_LAYERED_CONTEXT, REPROMPTER_STRICT_EVAL, REPROMPTER_PATTERN_LIBRARY, REPROMPTER_TELEMETRY (all 0|1, enabled by default).
SKILL.md # Behavior spec (the product)
references/ # 18 templates (XML + markdown)
feature-template.md
bugfix-template.md
reverse-template.md
marketing-swarm-template.md
...
scripts/ # Runtime engine
intent-router.js # Mode + profile routing
reverse-engineer.js # Exemplar analysis + prompt extraction
capability-policy.js # Model selection + fallback chains
context-builder.js # Token-budgeted context assembly
artifact-evaluator.js # Output quality gates
pattern-selector.js # Pluggable prompt patterns
recipe-fingerprint.js # Strategy hashing
outcome-collector.js # Flywheel data capture
strategy-learner.js # Historical recommendation engine
repromptverse-runtime.js # Orchestration composer
See CONTRIBUTING.md. PRs welcome.