/reprompter-reverse

name: reprompter-reverse
description: |
  RePrompter Reverse mode (advanced) — extract the prompt DNA from a great output and emit the XML prompt that would have produced it, with extracted success criteria; optionally seeds the Prompt Flywheel.
  Use when: "reverse reprompt", "reverse reprompter", "prompt dna", "prompt genome", "extract prompt from", "reprompt from example", "reprompt from this", "learn from this", "reverse engineer prompt", "prompt from output".
  Don't use to improve a raw prompt (use reprompter-improve) or for simple Q&A.
  Outputs: Reverse Extraction Card + reverse-engineered XML prompt with extracted <success_criteria> + quality score.
  Target score: spec >= 7/10.

RePrompter — Reverse (prompt DNA extraction)

> Mode skill of the `reprompter` plugin. Entry/routing: `../reprompter/SKILL.md`. Content below is verbatim from root SKILL.md lines 1314-1479; the reverse template is `references/reverse-template.md`. Task templates, scoring and `outcome-schema.md` live in `../reprompter-improve/`; headless-relay delivery of a finished Reverse prompt is in `../reprompter-goal-workflow/references/headless-relay.md`; the Prompt Flywheel is in `../reprompter/references/advanced-features.md`. `scripts/<name>` paths resolve to the bundled `../reprompter/scripts/<name>` (e.g. `../reprompter/scripts/reverse-engineer.js`). > > **Required reading:** `../reprompter/references/advanced-features.md`. The always-on rules in `../reprompter/SKILL.md` → "Core shared rules" (context check, uncertainty permission, WHAT not HOW) apply to every extracted prompt.

Advanced: Reverse Reprompter

TL;DR

Great output in → optimal prompt out. Extract the DNA that produced excellence.

Phase 1: EXTRACT — structural analysis of the exemplar (~5s)
Phase 2: ANALYZE — classify task type, domain, tone, quality (~5s)
Phase 3: SYNTHESIZE — generate full XML prompt matching the exemplar's pattern (~10s)
Phase 4: INJECT — seed flywheel with pre-graded exemplar outcome (optional, ~2s)

**Key insight:** Users encounter great outputs constantly but can't reproduce the quality. Reverse Reprompter closes that gap by extracting the prompt that would have produced it.

Trigger words

• "reverse reprompt", "reverse reprompter"
• "reprompt from example", "reprompt from this"
• "learn from this"
• "extract prompt from"
• "reverse engineer prompt"
• "prompt from output", "prompt dna", "prompt genome"

Process

1. **Receive exemplar** — user provides text (paste, file path, or points to an existing output) 2. **Input guard** — must be substantial output (>50 chars, has structure). Reject raw prompts (use Single mode instead), empty text, or single-word inputs 3. **Quick interview** (max 2 questions via AskUserQuestion):

• "What do you love about this output?" (with options: Structure / Depth / Tone / Coverage / Everything)
• "What context produced it?" (with options: Code review / Architecture / API work / Research / Other) — skip if task type is detectable with high confidence

4. **Analyze** — extract structure, classify type, detect domain and tone 5. **Extract criteria** — derive a v1 `<success_criteria schema_version="1">` block from the exemplar's observable features (see "Criteria extraction from exemplars" below). The exemplar *is* the target, so the criteria encode "future outputs should match this exemplar's distinguishing properties." 6. **Generate** — produce full XML prompt using reverse template + best-fit task template; embed the extracted `<success_criteria>` block. 7. **Score** — show quality dimensions of the generated prompt 8. **Flywheel injection** — offer to save as pre-graded exemplar outcome

Generate after analysis

After analysis completes, immediately: 1. Extract `<success_criteria>` from the exemplar (see "Criteria extraction from exemplars" below — 3–6 criteria anchored to observable features of the exemplar) 2. Generate the full reverse-engineered prompt, embedding the extracted `<success_criteria>` block 3. Show the Extraction Card (see below) 4. Show the generated prompt in XML format 5. Show quality score 6. Ask: "Save to flywheel? / Execute with this prompt? / Copy?"

❌ WRONG: Analyze exemplar → stop
❌ WRONG: Analyze exemplar → generate prompt → stop (skipping criteria extraction)
✅ RIGHT: Analyze exemplar → extract criteria → generate prompt with criteria → show Extraction Card → show score → offer actions

Extraction Card (transparency layer)

Rendered after analysis, before the generated prompt. Use this exact format:

## Reverse Extraction

| Dimension | Detected | Confidence |
|-----------|----------|------------|
| Task type | {code-review, architecture-doc, etc.} | {high/medium/low} |
| Domain | {primary domain} | - |
| Tone | {formal/neutral/casual} | - |
| Structure | {N sections, M bullets, K code blocks} | - |
| Quality | Clarity {N}/10, Specificity {N}/10, Coverage {N}/10 | - |

Template match: `{template-id}` | Flywheel injection: {ready/skipped}

Criteria extraction from exemplars

Reverse Reprompter converts the exemplar into criteria by examining three layers of its structure and encoding the distinguishing features as v1 `<criterion>` entries. Aim for 3–6 total, mix of methods.

**Structural layer** (produces `rule` / `predicate` criteria):

• Section/header count: `len(output_text) > N` bounded by the exemplar's length ±20%
• Presence of specific section names the exemplar uses (e.g. "## Summary", "## Trade-offs") → `rule` / `regex` matching those headers
• Minimum number of bulleted items, code blocks, or table rows if the exemplar has them → predicate

**Content layer** (produces `rule` / `regex` or `llm_judge` criteria):

• Required domain terminology that the exemplar uses distinctively (e.g. "CVE-", "SLO", "RFC 7231") → `rule` / `regex`
• Presence of quantitative claims (numbers + units) when the exemplar has them → regex like `\d+\s*(ms|MB|%|seconds)`
• Judgement calls that can't be regex-ch