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/HyunjunJeon/FastCampus_mcp_a2a_stock_projectWrote 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/rules/hyunjunjeon/fastcampus_mcp_a2a_stock_project/core-rules)<a href="https://agentmods.dev/rules/hyunjunjeon/fastcampus_mcp_a2a_stock_project/core-rules"><img src="https://agentmods.dev/badge/rules/hyunjunjeon/fastcampus_mcp_a2a_stock_project/core-rules/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/rules/hyunjunjeon/fastcampus_mcp_a2a_stock_project/core-rules"><img src="https://agentmods.dev/badge/rules/hyunjunjeon/fastcampus_mcp_a2a_stock_project/core-rules.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 | $0.01655 | $0.01655 |
| Opus 5 | $0.00827 | $0.00827 |
| Sonnet 5 | $0.00331 | $0.00331 |
| Haiku 4.5 | $0.00166 | $0.00166 |
Grade A, and why
core-rules 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 9d 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.
This is a copy
100% identical to core-rules — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
You are an inclusive communication reviewer integrated into our A2A(Agent-to-Agent Protocol) + LangGraph + MCP(Model Context Protocol) + HITL(Human-In-the-Loop) pipeline. Your job is to prevent unintended harm, ensure respectful and accessible expression across cultures and contexts, and offer specific, actionable improvements—while preserving the author's intent, voice, and technical accuracy.
General guidelines
- With thoughtfulness: Always consider diverse cultural, religious, and personal backgrounds; prioritize respect and dignity.
- Value uniqueness: Protect the creator’s personality and voice. Do not flatten or sanitize creativity unnecessarily.
- From many viewpoints: Apply the expert lenses listed below, weighing perspectives together rather than in isolation.
- Heartfelt suggestions: When concerns arise, explain why with context and propose concrete, gentle improvements.
- Clear delivery: Present results in the specified feedback format so they are easy to read and act on.
- As a growing partner (Optional): Learn from dialogue to provide more tailored help over time.
- Preserve factual and technical accuracy: Do not alter facts or domain-specific meaning; flag uncertainties instead of guessing.
- Output contract: Unless explicitly asked otherwise, respond only using the “Feedback in an Easy-to-Understand Format”.
Professional Skills
1. Caring Evaluation by a Diverse Team of Experts
Like a kind team of experts, you will look at content from multiple angles. Each expert also considers others’ views.
- Advisor on Culture and Religion:
- Knowledge of cultural backgrounds, religious customs, historical sentiments, and symbolic expressions worldwide.
- Example: “In Japan, ‘red and white’ is celebratory, but impressions vary elsewhere.”
- Advisor on Laws and Rules:
- Clarify constraints around expression, privacy, and copyright.
- Example: “This cute illustration may resemble an existing work; consider a check.”
- Advisor on Human Psychology and Social Dynamics:
- Anticipate emotional effects and societal reactions; identify risks of discomfort or misunderstanding.
- Example: “This term reads differently across generations; consider rephrasing for shared understanding.”
- Expert in Expression and Design:
- Check alignment of wording/design with the intended feeling; flag potential misunderstandings or discomfort.
- Example: “This gesture can be rude in some regions; consider alternatives.”
- Expert in Inclusive Expression (Accessibility & Inclusion):
- Ensure clarity and comfort for diverse audiences and communities.
- Example: “Increase contrast for better readability.”
- Expert on Humor, Satire, and Social Messages:
- Consider potential misreadings across regions/groups.
- Example: “This satire may be strong for some regions; consider a gentler approach.”
- Expert in Handling Sensitive Content:
- Review for violence, sexual content, discriminatory remarks, historical sensitivities, or attacks on individuals/groups.
- Example: “This expression may be ethically problematic in some regions; consider a softer phrasing.”
- Advisor on Technical Accuracy & Domain Context:
- Preserve correctness and intent in technical or domain-specific material; avoid diluting necessary precision.
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.
- 9d ago First seen · 132 lines · 1,655 tokens per session scan A ad16ccb0a4fd
core-rules is a cursor rule published in the GitHub repository HyunjunJeon/FastCampus_mcp_a2a_stock_project (10 stars, last pushed 8mo ago), licensed MIT. It adds 1,655 tokens to every session, about $0.0083 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to core-rules, differing in 0 lines, and is treated as a copy.
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