Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/AytuncYildizli/reprompternpx agentmods add skills/aytuncyildizli/reprompter/reprompter-reverseWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/aytuncyildizli/reprompter/reprompter-reverse)<a href="https://agentmods.dev/skills/aytuncyildizli/reprompter/reprompter-reverse"><img src="https://agentmods.dev/badge/skills/aytuncyildizli/reprompter/reprompter-reverse/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/aytuncyildizli/reprompter/reprompter-reverse"><img src="https://agentmods.dev/badge/skills/aytuncyildizli/reprompter/reprompter-reverse.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00157 | $0.02524 |
| Opus 5.5 | $0.00063 | $0.01010 |
| Sonnet 5.5 | $0.00031 | $0.00505 |
| Haiku 4.5 | $0.00016 | $0.00252 |
Grade A, and why
reprompter-reverse scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RePrompter — Reverse (prompt DNA extraction)
Mode skill of the
reprompterplugin. Entry/routing:../reprompter/SKILL.md. Content below is verbatim from root SKILL.md lines 1314-1479; the reverse template isreferences/reverse-template.md. Task templates, scoring andoutcome-schema.mdlive 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
- Receive exemplar — user provides text (paste, file path, or points to an existing output)
- Input guard — must be substantial output (>50 chars, has structure). Reject raw prompts (use Single mode instead), empty text, or single-word inputs
- 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
- Analyze — extract structure, classify type, detect domain and tone
- 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." - Generate — produce full XML prompt using reverse template + best-fit task template; embed the extracted
<success_criteria>block. - Score — show quality dimensions of the generated prompt
- Flywheel injection — offer to save as pre-graded exemplar outcome
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 183 lines · 157 tokens per session scan A a8176bdcbee3
reprompter-reverse is a skill published in the GitHub repository AytuncYildizli/reprompter (118 stars, last pushed yesterday), licensed MIT. It adds 157 tokens to every session and 2,524 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-16.
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