awesome-trustworthy-deep-learning: Instructions file for Codex

AGENTS.md

awesome-trustworthy-deep-learning AGENTS.md is an instructions file for Codex, OpenCode from MinghuiChen43/awesome-trustworthy-deep-learning. It costs 1,925 tokens per session, scanned A, original, MIT.

A set of instructions for maintaining a curated list of trustworthy deep-learning papers. It explains how to add an arXiv paper, including normalizing its identifier and retrieving its metadata reproducibly.

In plain words
What is it for?
Use it when adding an arXiv paper to the project's main README while preserving its formatting and other sections.
Why use it?
It prevents inconsistent paper links, duplicate entries, accidental edits to the full list, and unreliable metadata collection.

Instructions file for CodexOpenCode

Written for Codex and OpenCode: the file is AGENTS.md. Also seen: mentions AGENTS.md; mentions Codex.

This is MinghuiChen43/awesome-trustworthy-deep-learning's own configuration. It tells Codex and OpenCode how to work on awesome-trustworthy-deep-learning itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything awesome-trustworthy-deep-learning configures →

Reuse

Borrowing it

Nothing to install: this file belongs to MinghuiChen43/awesome-trustworthy-deep-learning. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/MinghuiChen43/awesome-trustworthy-deep-learning/master/AGENTS.md
Clone the repo
git clone --depth 1 https://github.com/MinghuiChen43/awesome-trustworthy-deep-learning

Made for: Codex, OpenCode.

Wrote 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.

agentmods badge for awesome-trustworthy-deep-learning AGENTS.md

README.md
[![agentmods](https://agentmods.dev/badge/instructions/minghuichen43/awesome-trustworthy-deep-learning/agents-md/github.svg)](https://agentmods.dev/instructions/minghuichen43/awesome-trustworthy-deep-learning/agents-md)
Your own site
<a href="https://agentmods.dev/instructions/minghuichen43/awesome-trustworthy-deep-learning/agents-md"><img src="https://agentmods.dev/badge/instructions/minghuichen43/awesome-trustworthy-deep-learning/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.

agentmods 80×15 button for awesome-trustworthy-deep-learning AGENTS.md

Your own site · 80×15
<a href="https://agentmods.dev/instructions/minghuichen43/awesome-trustworthy-deep-learning/agents-md"><img src="https://agentmods.dev/badge/instructions/minghuichen43/awesome-trustworthy-deep-learning/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 1,925 This file is loaded in full into every session.
When invoked 1,925 The same file — it is already loaded in full.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.01925 $0.01925
Opus 5.5 $0.00770 $0.00770
Sonnet 5.5 $0.00385 $0.00385
Haiku 4.5 $0.00193 $0.00193

Measured 2mo ago against content hash 8ec0cd0a1956, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

awesome-trustworthy-deep-learning AGENTS.md 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 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

import urllib.parse
AGENTS.md · 164 lines

How it starts

The opening of the file, as written. The whole thing — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md

Project Context

This repository is a curated Awesome list for trustworthy deep learning papers. The main paper file for Codex-assisted updates is:

  • README.md: the primary curated paper list shown on GitHub.

Do not edit FULL_LIST.md during the default arXiv-paper workflow unless the user explicitly asks for a full-list update. Preserve the current Markdown style, table of contents, badges, image links, and non-paper resource sections unless the user explicitly asks to edit them.

Default Task: Add an arXiv Paper

When the user gives an arXiv abstract link and asks to use this project's agent instructions, add the paper end-to-end unless it is already present.

1. Normalize the arXiv ID

  • Accept https://arxiv.org/abs/<id>, http://arxiv.org/abs/<id>, and arXiv PDF links.
  • Canonicalize the list URL to https://arxiv.org/abs/<base-id>.
  • Strip version suffixes such as v2 from the list URL unless the user explicitly asks to track a specific version.
  • Prefer modern IDs like 2507.16795; handle legacy IDs only when needed.

2. Fetch Metadata Reproducibly

Use the arXiv Atom API as the primary source, not ad hoc scraping of the HTML page. A reliable read-only helper is:

python3 - "$ARXIV_ID" <<'PY'
import json
import re
import sys
import time
import urllib.parse
import urllib.request
import xml.etree.ElementTree as ET
from urllib.error import HTTPError, URLError

raw = sys.argv[1]
match = re.search(r'([a-z-]+/)?\d{4}\.\d{4,5}(?:v\d+)?|[a-z-]+(?:\.[A-Z]{2})?/\d{7}(?:v\d+)?', raw)
if not match:
    raise SystemExit(f"Could not find an arXiv id in: {raw}")

paper_id = re.sub(r'v\d+$', '', match.group(0))
url = 'https://export.arxiv.org/api/query?' + urllib.parse.urlencode({'id_list': paper_id})
request = urllib.request.Request(
    url,
    headers={'User-Agent': 'awesome-trustworthy-deep-learning-agent/1.0'},
)
for attempt in range(2):
    try:
        with urllib.request.urlopen(request, timeout=30) as response:
            data = response.read()
        break
    except HTTPError as exc:
        if exc.code == 429 and attempt == 0:
            time.sleep(10)
            continue
        raise SystemExit(f"arXiv API request failed with HTTP {exc.code}: {exc.reason}") from exc
    except URLError as exc:
        raise SystemExit(f"arXiv API request failed: {exc.reason}") from exc

ns = {'atom': 'http://www.w3.org/2005/Atom', 'arxiv': 'http://arxiv.org/schemas/atom'}
root = ET.fromstring(data)
entry = root.find('atom:entry', ns)
if entry is None:
    raise SystemExit(f"No arXiv API entry found for: {paper_id}")

def text(path):
    node = entry.find(path, ns)
    return '' if node is None or node.text is None else ' '.join(node.text.split())

authors = [text_node.text.strip() for text_node in entry.findall('atom:author/atom:name', ns)]
primary = entry.find('arxiv:primary_category', ns)
categories = [node.attrib.get('term', '') for node in entry.findall('atom:category', ns)]

print(json.dumps({
    'id': paper_id,
    'url': f'https://arxiv.org/abs/{paper_id}',
    'title': text('atom:title'),
    'authors': authors,
    'published': text('atom:published'),
    'updated': text('atom:updated'),
    'primary_category': '' if primary is None else primary.attrib.get('term', ''),
    'categories': categories,
    'abstract': text('atom:summary'),
}, indent=2, ensure_ascii=False))
PY

Read the full file on GitHub · 164 lines

Changes

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.

  1. 2mo ago First seen · 164 lines · 1,925 tokens per session scan A 8ec0cd0a1956

Subscribe to this mod's changes

awesome-trustworthy-deep-learning AGENTS.md is an instructions file published in the GitHub repository MinghuiChen43/awesome-trustworthy-deep-learning (395 stars, last pushed 5d ago), licensed MIT. It adds 1,925 tokens to every session, about $0.0077 per session on Opus 5.5. 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 2026-08-30.

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