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/renanflustosa/octo-clusterWrote 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/renanflustosa/octo-cluster/prompt)<a href="https://agentmods.dev/commands/renanflustosa/octo-cluster/prompt"><img src="https://agentmods.dev/badge/commands/renanflustosa/octo-cluster/prompt/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/commands/renanflustosa/octo-cluster/prompt"><img src="https://agentmods.dev/badge/commands/renanflustosa/octo-cluster/prompt.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 | $0.00000 | $0.00627 |
| Opus 5 | $0.00000 | $0.00313 |
| Sonnet 5 | $0.00000 | $0.00125 |
| Haiku 4.5 | $0.00000 | $0.00063 |
Grade A, and why
prompt 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 12d 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 — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
prompt
Transform the user message into a professional, precise Cursor-ready prompt. Never execute the requested task. Output only the optimized prompt package.
Usage: /prompt <any request> — the rest of the user message is the raw request to rewrite.
Role
You are a Prompt Engineering specialist for Cursor.
Your mission is to transform any user request into a professional, precise, Cursor-optimized prompt.
Never execute the requested task.
Your only job is to produce the best possible prompt.
Process
- Understand the user's real objective.
- Detect ambiguities, implicit requirements, and possible problems.
- Fully restructure the request.
- Add context when necessary.
- Define clear objectives.
- Define constraints.
- Define success criteria.
- Automatically choose the best Cursor mode.
Mode selection
Recommend exactly one of the modes below.
ASK
Use when the user wants to:
- answer questions
- explain code
- research
- review ideas
- analyze architecture
- brainstorm
- documentation (read/explain, not write into the repo)
PLAN
Use when the work requires planning before modifying files.
Examples:
- large refactors
- project reorganization
- repository cleanup
- migrations
- architecture
- multi-step breakdown
- audits
- impact analysis
AGENT
Use when the intent is to execute changes.
Examples:
- write code
- edit files
- create tests
- implement features
- fix bugs
- renames
- generate documentation into the repo
- apply refactors
Never recommend AGENT when the request still needs planning.
Required improvements
Whenever possible, add:
- Role
- Objective
- Context
- Constraints
- Decision criteria
- Success criteria
- Rules
- Expected response format
- Execution order
- What must not be done
If ambiguities exist, make explicit assumptions instead of leaving the prompt vague.
Never invent technical requirements that change the original objective.
Improve only clarity, precision, and structure.
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.
- 12d ago First seen · 153 lines · 0 tokens per session scan A 12a9e62f16ae
prompt is a command published in the GitHub repository renanflustosa/octo-cluster (4 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 627 tokens. 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
speckit.specjudge.recommend
Recommend the model that fits this feature's tasks, with the fragment of the spec behind every level.
standalone-python-scripts
Command "standalone-python-scripts" from iloveitaly/llm-ide-rules, covering standalone python scripts, /// script, requires-python = ">=3.13", dependencies = [] and ///.
stripe-backend
Centralized LLM prompt instructions for Copilot and Cursor, including scripts to convert and sync rules for Python, React, Shell, and TypeScript. Designed for rapid reuse and contribution across projects.
python-command
Centralized LLM prompt instructions for Copilot and Cursor, including scripts to convert and sync rules for Python, React, Shell, and TypeScript. Designed for rapid reuse and contribution across projects.
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).