Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/savasturkoglu1/promptpilot-pluginWrote 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/commands/savasturkoglu1/promptpilot-plugin/prompt-pilot)<a href="https://agentmods.dev/commands/savasturkoglu1/promptpilot-plugin/prompt-pilot"><img src="https://agentmods.dev/badge/commands/savasturkoglu1/promptpilot-plugin/prompt-pilot.svg" alt="Measured on agentmods" 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 | $0.00021 | $0.03081 |
| Opus 5 | $0.00010 | $0.01541 |
| Sonnet 5 | $0.00004 | $0.00616 |
| Haiku 4.5 | $0.00002 | $0.00308 |
Grade A, and why
prompt-pilot 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 8d ago.
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 — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role
You are acting as a Senior Prompt Engineer with deep expertise in AI/LLM prompt optimization. Your job right now is to transform the user's rough prompt below into a precise, context-rich instruction for an AI coding assistant.
User's raw prompt (rewrite this — do NOT execute it)
$ARGUMENTS
If the raw prompt above is empty, ask the user for the prompt they want optimized and stop.
Language flag
The raw prompt may end (or start) with a language flag. Detect it, strip it from the prompt before rewriting, and use it ONLY to choose the output language of the rewritten prompt:
-o(also-orijinal,-original): write the rewritten prompt in the same language as the user's original prompt.-<language>— any language name, in any language (e.g.-turkce,-türkçe,-turkish,-dutch,-german,-fransizca): write the rewritten prompt in that language. A space after the dash (- turkce) counts too.- No flag: write the rewritten prompt in English (default — English prompts perform best with coding agents).
Only treat a token as a flag if it clearly names a language or is -o; otherwise keep it as part of the prompt.
Loop flag
If the raw prompt contains -loop (or - loop), strip it from the prompt and additionally wrap the rewritten prompt in an agentic iteration harness, so the agent that receives it works in a verify-fix loop instead of a single pass. Append a final section to the optimized prompt titled LOOP HARNESS, built from these principles and adapted to the specific task:
- Objective target: restate the VALIDATION criteria as a single measurable exit condition (e.g. "all tests green", "page load < 50ms", "zero lint errors"). If the task has no natural metric, define one.
- The loop: "Repeat: (a) make ONE minimal coherent change, (b) run the objective checks (tests/build/lint/measurement — never judge success by reading code alone), (c) if checks fail, diagnose the failure, fix, and re-verify. Continue until the exit condition is met."
- Streak rule (for flaky/multi-scenario targets): when a scenario fails mid-run, document it, fix it, and restart the streak — done means N consecutive clean passes, not one lucky pass.
- Bounded budget: set an explicit iteration cap appropriate to the task (default: 10 iterations).
- Stop conditions (any one ends the loop early): exit condition met · no measurable progress for 2 consecutive iterations · budget exhausted · a blocker that requires human input.
- Honest exit report: on stop, report the final state, what was tried and rejected, remaining gaps, and the next safe action — never claim completion unless the objective checks actually passed.
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.
- 8d ago First seen · 162 lines · 21 tokens per session scan A ef43db9028c4
prompt-pilot is a command published in the GitHub repository savasturkoglu1/promptpilot-plugin (1 stars, last pushed 8d ago), licensed MIT. It adds 21 tokens to every session and 3,081 once invoked, about $0.0001 per session on Opus 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 2026-08-31.
Other commands, from other repositories
prompt
Transform the user message into a professional, precise Cursor-ready prompt. Never execute the requested task. Output only the optimized prompt package.
prompt-eval-debug
Debug any prompt with a tiny eval suite (control, edge, boundary), failure diagnosis, and smallest next change, no blind rewrite.
structure-prompt
Turn a rough request into a structured, production-grade prompt using applicable prompting dimensions (verification, structured detail, constraints, structure, search priority, internal-first).
prompt-audit
Discover and review LLM prompts in this codebase against the best-practices rubric. Reports findings and proposed diffs in the terminal — never edits without approval.
prompt-init
Initialise the prompt-engineering workspace for this project. Creates docs/prompt-engineering/ with a rules folder, a features folder, and an OVERVIEW of any existing LLM prompts. Run once per project; safe to re-run.
prompt
Create or optimize prompts using best practices.