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 mehrad-dm/mastermind --skill promptgit clone --depth 1 https://github.com/mehrad-dm/mastermindWrote 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/mehrad-dm/mastermind/prompt)<a href="https://agentmods.dev/skills/mehrad-dm/mastermind/prompt"><img src="https://agentmods.dev/badge/skills/mehrad-dm/mastermind/prompt.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 3 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00076 | $0.01609 |
| Opus 5 | $0.00038 | $0.00805 |
| Sonnet 5 | $0.00015 | $0.00322 |
| Haiku 4.5 | $0.00008 | $0.00161 |
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 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MasterMind: Prompt
The single biggest lever on AI output quality is the prompt. Most weak results aren't a model failure, they're an under-specified request. This skill rewrites a rough ask into a prompt an AI executes well: clear intent, only the context that matters, structured, token-efficient.
The user's own words are not yours to rewrite. Fire only on an explicit ask, propose rather than apply, and name every requirement you added or cut,
~/.mastermind/engineering/core/rigor.md§ Stay in scope.
Do not fire unless asked
Auto-invocation is the danger here: rewriting a prompt the user meant you to answer replaces their work with your paraphrase and loses whatever they actually wanted. The bar is an explicit request to improve the prompt.
| The user does this | You do this |
|---|---|
| "improve / fix / sharpen this prompt" | This skill |
| Pastes a prompt and says "run this" / "what do you think?" / nothing | Answer or execute it. Not this skill |
| Pastes a prompt written for another tool, asking what it does | Explain it. Not this skill |
| Asks for a prompt to be written from scratch | Write it: then the checklist below applies |
If it's genuinely ambiguous, ask one question before rewriting. Guessing wrong wastes a turn; rewriting unasked destroys their text.
The prompt stays theirs
The output is a proposal, never an action:
- Never execute the rewritten prompt: not in the same turn, not "to show it works". Hand it back.
- Never drop a requirement. Every constraint in the original survives, or you say out loud that you cut it and why. Silent removal is the failure mode that makes this skill dangerous.
- Never add a requirement they didn't imply. Additions get named in "what changed": a specific the user never chose (a framework, a length, a tone) is a guess wearing their voice.
- Never rewrite a prompt containing credentials, private data, or client names into a form that
moves them somewhere new. Flag them and quarantine instead (
quarantine).
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.
- 8d ago First seen · 104 lines · 0 tokens per session scan A aaea25e4b340
prompt is a skill published in the GitHub repository mehrad-dm/mastermind (24 stars, last pushed 8d ago), licensed MIT. It adds 76 tokens to every session and 1,609 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
autoresearch
Self-improving optimization via Karpathy autoresearch pattern. Generates → evaluates → scores → mutates prompts/descriptions in a loop. Targets — tool-selection, system-prompt, skill, decision-parser. Use when "optimize tools", "autoresearch", "improve skill X", "self-improve prompts", "optimize tool descriptions".
hatch3r-ai-feature
Eval-driven development workflow for shipping AI features — write eval before prompt, measure, iterate, ship with caching + cost telemetry + model fallback + hallucination SLI.
prompt-architect
Designs clear, testable prompts for agent workflows. Use when creating new prompts, refining weak prompts, or establishing reusable prompting patterns.
sc:image-gen-prompting
Craft effective text-to-image prompts for AI generators (Higgsfield, Midjourney, DALL·E/GPT-Image, Stable Diffusion, Flux). Subject + style + composition + lighting structure, negative prompts, aspect ratios, model-specific tips, and Hebrew→English prompt translation. Activate for: generate image, AI art, product…
prompt-engineering-expert
Expert prompt engineering: prompt structure, few-shot, chain-of-thought, structured output, and iteration. Trigger keywords: prompt, prompt engineering, system prompt, few-shot, chain-of-thought, output format, JSON schema, structured output, instructions, role, examples, eval. Use for writing, debugging, or…
ollama
Local LLM generation via Ollama — image captioning, prompt generation, text enhancement, and creative writing. Use when captioning images for LoRA training datasets, generating image prompts, enhancing existing prompts, writing model descriptions, or producing creative text without requiring cloud API access.