Borrowing it
Nothing to install: this file belongs to Khronossu/AcadeMong. 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/Khronossu/AcadeMong/main/CLAUDE.mdgit clone --depth 1 https://github.com/Khronossu/AcadeMongWrote 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/instructions/khronossu/academong/claude-md)<a href="https://agentmods.dev/instructions/khronossu/academong/claude-md"><img src="https://agentmods.dev/badge/instructions/khronossu/academong/claude-md.svg" alt="Measured on agentmods" 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 | $0.11781 | $0.11781 |
| Opus 5 | $0.05890 | $0.05890 |
| Sonnet 5 | $0.02356 | $0.02356 |
| Haiku 4.5 | $0.01178 | $0.01178 |
Grade A, and why
AcadeMong CLAUDE.md 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 — 1,099 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md — AcadeMong: Project Specification & Build Guide
This document is the single source of truth for the AcadeMong project. Scope: Production-grade system. Phases 1–11 cover the core build; Phases 12–13 cover production deployment and feedback automation. All team members must read this before writing a single line of code.
1. Project Overview
An adaptive AI decision-support system for Thai students applying to universities under the TCAS system. Combines deterministic eligibility validation, hybrid RAG-based advisory, personalized major recommendations, and career path guidance.
Key constraint: The system must never hallucinate eligibility criteria (GPAX minimums, subject requirements). These always come from SQL — never from an LLM.
Target users: Thai high school students (primarily Thai-language queries).
Target universities (initial): Chulalongkorn, Mahidol, Kasetsart, Thammasat, Srinakharinwirot.
2. Goals
- Help students identify which majors/universities they are eligible for
- Recommend majors aligned to their academic profile and interests
- Guide career path planning with major → career → salary → license mapping
- Retain user memory across sessions for personalized longitudinal guidance
- Run entirely locally via Docker (no mandatory external API dependency)
3. Tech Stack
| Layer | Technology | Reason |
|---|---|---|
| Backend | FastAPI (Python) | Async, lightweight, easy to structure |
| Primary LLM | scb10x/llama3.1-typhoon2-8b-instruct (Ollama) |
Thai language capable, runs locally |
| Fallback LLM | Gemma 3/4 (8B) — pending Phase 2.5 eval vs Llama3.1:8b | See §7.2 |
| Embeddings | nomic-embed-text (Ollama) |
Local, no API cost |
| Reranker | bge-reranker-base (cross-encoder) |
RAG Phase 6 reranking |
| Safety classifier | Llama Guard 3 (1B) | Output safety filter, Phase 9.5 |
| Vector DB | Qdrant | RAG retrieval, Docker-native |
| Relational DB | PostgreSQL 16 | Structured eligibility data + user memory |
| Session Cache | Redis 7 | Short-term session memory |
| Auth | Firebase (Google OAuth + Email/Password) | idToken verified by firebase-admin |
| Frontend | React (single page) | Chat UI + profile form + career view |
| Containerization | Docker Compose (dev) / ECS + RunPod (prod) | See §18 |
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 · 1,099 lines · 11,781 tokens per session scan A 8e7e298d36c4
AcadeMong CLAUDE.md is an instructions file published in the GitHub repository Khronossu/AcadeMong (2 stars, last pushed 3mo ago), licensed MIT. It adds 11,781 tokens to every session, about $0.0589 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 2026-08-31.
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