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.
npx skills add renanflustosa/octo-cluster --skill promptgit 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/skills/renanflustosa/octo-cluster/prompt)<a href="https://agentmods.dev/skills/renanflustosa/octo-cluster/prompt"><img src="https://agentmods.dev/badge/skills/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/skills/renanflustosa/octo-cluster/prompt"><img src="https://agentmods.dev/badge/skills/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 | $0.00017 | $0.00810 |
| Opus 5 | $0.00009 | $0.00405 |
| Sonnet 5 | $0.00003 | $0.00162 |
| Haiku 4.5 | $0.00002 | $0.00081 |
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 2d 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 — 170 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 prompt for the AI coding tool running this skill (Cursor or Claude Code). 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.
Raw request: $ARGUMENTS
Role
You are a Prompt Engineering specialist for AI coding agents.
Your mission is to transform any user request into a professional, precise, agent-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 mode.
Mode selection
Recommend exactly one of the modes below. Name the matching control of the tool in use:
| Mode | Cursor | Claude Code |
|---|---|---|
| ASK | Ask mode | Normal chat; prompt says "do not edit files" |
| PLAN | Plan mode | Plan mode (Shift+Tab) |
| AGENT | Agent mode | Normal / auto-accept edits |
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
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.
- 2d ago First seen · 170 lines · 17 tokens per session scan A 8554aca006cb
prompt is a skill published in the GitHub repository renanflustosa/octo-cluster (4 stars, last pushed 3d ago), licensed MIT. It adds 17 tokens to every session and 810 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 2026-09-16.
Other skills, from other repositories
firebase-ai
Use when setting up firebaseai, generating text/chat with Gemini, streaming AI output, building multimodal prompts, or handling AI errors.
coding-agents-prompt-authoring
To author, adapt, review, and validate prompts (skills, agents, workflows, rules, etc.) with brief, contracts, and a validation pack.
opik-optimizer
Optimize LLM prompts, tools, and agents in Opik using standardized optimizer workflows (prompt optimization, tool optimization, and parameter tuning), dataset/metric wiring, and result interpretation.
prompt-optimization
Improves LLM-facing context while preserving intent, execution boundaries, and proportional work. Use when creating or reviewing prompts, agent definitions, skill definitions, or other instructions for an LLM.
standalone-python-scripts
Skill "standalone-python-scripts" from iloveitaly/llm-ide-rules, covering standalone python scripts, /// script, requires-python = ">=3.13", dependencies = [] and ///.
recipe-eval-prompt
Compares original and optimized prompts through repeated blind paired execution in git worktrees. Use when evaluating prompt improvement effects or learning prompt engineering through concrete examples.