Borrowing it
Nothing to install: this file belongs to hevangel/dvcon_ai_library. 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/hevangel/dvcon_ai_library/main/AGENTS.mdgit clone --depth 1 https://github.com/hevangel/dvcon_ai_libraryWrote 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/hevangel/dvcon_ai_library/agents-md)<a href="https://agentmods.dev/instructions/hevangel/dvcon_ai_library/agents-md"><img src="https://agentmods.dev/badge/instructions/hevangel/dvcon_ai_library/agents-md/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/instructions/hevangel/dvcon_ai_library/agents-md"><img src="https://agentmods.dev/badge/instructions/hevangel/dvcon_ai_library/agents-md.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 | $0.10523 | $0.10523 |
| Opus 5.5 | $0.04209 | $0.04209 |
| Sonnet 5.5 | $0.02105 | $0.02105 |
| Haiku 4.5 | $0.01052 | $0.01052 |
Grade A, and why
dvcon_ai_library AGENTS.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 2mo 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 — 509 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
This repository contains a full-stack DVCon paper search and chat application. This file is the handoff guide for future AI coding agents.
Purpose
Build and maintain a web app that:
- downloads DVCon papers from
https://dvcon-proceedings.org/ - stores raw PDFs under
data/paper/ - extracts markdown, images, and metadata under
data/ - supports keyword search and semantic search
- supports grounded chat over selected papers
- provides a professional React frontend and a FastAPI backend
User Preferences
- Use
uvfor Python dependency management and Python commands. - Use snake_case and 4-space indentation.
- Do not commit secrets or the full generated runtime corpus. A curated example corpus is acceptable only when explicitly requested by the user.
- Default interpretation of a change request: do the implementation work, update the code, update
AGENTS.md, and updatePROGRESS.mdunless the user explicitly narrows the scope.
Current Architecture
- Backend:
FastAPI,SQLModel,SQLite FTS5,ChromaDB - Frontend:
React,TypeScript,Vite,MUI - PDF extraction:
PyMuPDF,pymupdf4llm - Metadata enrichment: local
GROBIDsidecar producing TEI XML, enabled by default - Scraping:
httpx,BeautifulSoup4 - Chat: OpenAI Responses API via configurable
OPENAI_BASE_URLandOPENAI_API_KEY - Embeddings: local
sentence-transformersmodel viatorch - Local embedding device: CUDA preferred, CPU fallback
- Agent access: MCP server (standalone FastMCP 4 over stdio, built on MCP SDK v2) reusing the service layer; Claude plugin marketplace + agent skill mirror the same tool surface
Key Product Requirements
- Only index DVCon items whose detail page says
Type: PaperandFormat: pdf. - Save PDFs at
data/paper/{year}/{location}/{slug}.pdf. - Save markdown at
data/markdown/{year}/{location}/{slug}.md. - Save extracted images at
data/markdown/{year}/{location}/images/{slug}/. - Save raw GROBID TEI at
data/tei/{year}/{location}/{slug}.tei.xmlwhen available. - Extract and persist metadata such as:
- title
- authors
- affiliations / company names
- abstract
- references
- year
- conference location
- Left panel tabs:
- Search Results
- Markdown
- Metadata Graph
- Right panel:
- chat transcript
- input box
- Enter submits
- Shift+Enter inserts newline
- Metadata Graph nodes are clickable (per node type):
- author / company → jump to Search Results filtered by that name (keyword FTS match against
authors/affiliations) - conference → jump to Search Results filtered by that conference's year + location (precise, not free-text)
- reference → if the citation's normalized title resolves to an in-corpus paper, jump to that paper's PDF tab; unresolved references render non-clickable
- paper (the active paper) → no-op (already viewing it)
- The graph tab stays mounted across author/company/conference clicks (graph_query is keyed on
active_paper_id, which doesn't change), so the user returns to the graph by re-clicking the Metadata Graph tab.
- author / company → jump to Search Results filtered by that name (keyword FTS match against
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.
- 2mo ago Changed · +4 lines · +792 tokens per session 84d99a04151c
- 2mo ago Changed · +1 lines · +118 tokens per session d58475f2de60
- 2mo ago First seen · 504 lines · 9,613 tokens per session scan A 1ad25a21bc11
dvcon_ai_library AGENTS.md is an instructions file published in the GitHub repository hevangel/dvcon_ai_library (11 stars, last pushed 2mo ago), licensed MIT. It adds 10,523 tokens to every session, about $0.0421 per session on Opus 5.5. 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.
dvcon_ai_library AGENTS.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| dvcon_ai_library AGENTS.mdhevangel/dvcon_ai_library | Instructions file | 11 | 2mo ago | MIT | A | 10,523 | Claude Code, Codex, OpenCode |
| vscode buildNext.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 9,719 | GitHub Copilot |
| next.js AGENTS.mdvercel/next.js | Instructions file | 143k | yesterday | MIT | A | 7,461 | Codex, OpenCode |
| langchain AGENTS.mdlangchain-ai/langchain | Instructions file | 149k | yesterday | MIT | A | 4,680 | Codex, OpenCode |
| vscode oss-third-party-notices.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 5,516 | GitHub Copilot |
| vscode copilot-instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 3,093 | GitHub Copilot |
| deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness | Instructions file | 274k | 2d ago | MIT | A | 4,313 | Codex, OpenCode |