Borrowing it
Nothing to install: this file belongs to Bazilio-san/mcp-vkusvill. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Bazilio-san/mcp-vkusvill/master/.claude/skills/feature-prompt-generator/SKILL.mdgit clone --depth 1 https://github.com/Bazilio-san/mcp-vkusvillWrote 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/bazilio-san/mcp-vkusvill/feature-prompt-generator)<a href="https://agentmods.dev/skills/bazilio-san/mcp-vkusvill/feature-prompt-generator"><img src="https://agentmods.dev/badge/skills/bazilio-san/mcp-vkusvill/feature-prompt-generator/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/bazilio-san/mcp-vkusvill/feature-prompt-generator"><img src="https://agentmods.dev/badge/skills/bazilio-san/mcp-vkusvill/feature-prompt-generator.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 | $0.00094 | $0.04211 |
| Opus 5 | $0.00047 | $0.02106 |
| Sonnet 5 | $0.00019 | $0.00842 |
| Haiku 4.5 | $0.00009 | $0.00421 |
Grade A, and why
feature-prompt-generator scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **Manual checks** — `yarn build && yarn start`, then HTTP (curl/PowerShell) or an MCP client; How it starts
The opening of the file, as written. The whole thing — 365 lines — stays where its author put it; the contents beside it link to each section on GitHub.
feature-prompt-generator — META-SKILL for generating prompts for an AI CLI
Essence
You do NOT write code. You generate a self-sufficient prompt for an AI CLI, which will then:
- study the code itself,
- design the solution itself,
- implement it itself,
- test it itself.
This is a META-skill (agent-building agent). Your output is a clean prompt, not an implementation. You do not touch any code in the target repository.
How to invoke
Command-only. The skill is never auto-invoked by the model — disable-model-invocation
is set to true. Runs solely when the operator explicitly calls it (e.g. /feature-prompt-generator
or the equivalent UI invocation). Ignore any implicit triggers from phrasing in user messages.
When to use
- The operator describes a feature/functionality but does not write code themselves.
- A production-ready prompt is needed to hand off to Claude Code / another AI agent.
Core principles (Karpathy-style, think-before-code)
- Think before code. Architecture first, implementation second. Do not rush to code.
- Simplicity first. KISS / YAGNI / DRY — the minimal sufficient solution. No speculative features.
- Surgical changes. Touch only what is required. Do not "improve" adjacent code.
- Goal-driven. Every step has a verifiable success criterion.
- Anti-hallucination. Do not invent files, functions, or APIs. Only what actually exists in the code (verified via Read/Grep/Glob).
- Surface assumptions. Explicitly mark anything inferred on behalf of the operator as
ASSUMPTION:. - Ask, don't guess. If anything is ambiguous — stop, name the ambiguity, ask.
Input
The operator passes via $ARGUMENTS:
- a free-form feature description, OR
- a path to a file with the description (
task.md, issue dump from a tracker, dialog excerpt).
If $ARGUMENTS is empty — request a feature description. Do not infer requirements on the
operator's behalf. If the project uses an issue tracker (Jira/Linear/GitHub Issues) — ask for
the task ID and reference it in the final prompt.
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 · 365 lines · 94 tokens per session scan A f3de9e4d7102
feature-prompt-generator is a skill published in the GitHub repository Bazilio-san/mcp-vkusvill (1 stars, last pushed 2mo ago), licensed MIT. It adds 94 tokens to every session and 4,211 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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