awesome-deep-phenomena AGENTS.md

awesome-deep-phenomena AGENTS.md is an instructions file for Codex, OpenCode from MinghuiChen43/awesome-deep-phenomena. It costs 1,891 tokens per session, scanned A, original, MIT.

Instructions for maintaining an Awesome list: a curated collection of papers and resources about deep-learning phenomena. They focus on adding arXiv papers, an online archive of research papers.

In plain words
What is it for?
Use them to add an arXiv paper to the list while preserving its links, layout, table of contents, and contribution details.
Why use it?
They make paper additions reproducible by standardizing arXiv links, metadata retrieval, and README formatting.

Instructions file for CodexOpenCode

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

Install

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.

agentmods
npx agentmods add instructions/minghuichen43/awesome-deep-phenomena/agents-md
Clone the repo
git clone --depth 1 https://github.com/MinghuiChen43/awesome-deep-phenomena

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-deep-phenomena AGENTS.md

README.md
[![agentmods](https://agentmods.dev/badge/instructions/minghuichen43/awesome-deep-phenomena/agents-md.svg)](https://agentmods.dev/instructions/minghuichen43/awesome-deep-phenomena/agents-md)
Your own site
<a href="https://agentmods.dev/instructions/minghuichen43/awesome-deep-phenomena/agents-md"><img src="https://agentmods.dev/badge/instructions/minghuichen43/awesome-deep-phenomena/agents-md.svg" alt="Measured on agentmods" height="20"></a>
Per session 1,891 This file is loaded in full into every session.
When invoked 1,891 The same file — it is already loaded in full.
Security scan A 1 finding. Scan, not verified.
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 $0.01891 $0.01891
Opus 5 $0.00945 $0.00945
Sonnet 5 $0.00378 $0.00378
Haiku 4.5 $0.00189 $0.00189

Measured 6d ago against content hash 0a5c3f3dd70a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

awesome-deep-phenomena 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 6d 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 papers and resources about deep learning phenomena. The main file for Codex-assisted updates is:

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

Preserve the current Markdown style, table of contents, badges, image links, related-resource section, and contribution text 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-deep-phenomena-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. 6d ago First seen · 164 lines · 1,891 tokens per session scan A 0a5c3f3dd70a

Subscribe to this mod's changes

awesome-deep-phenomena AGENTS.md is an instructions file published in the GitHub repository MinghuiChen43/awesome-deep-phenomena (409 stars, last pushed 18d ago), licensed MIT. It adds 1,891 tokens to every session, about $0.0095 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 2026-08-30.

Related

Other instructions, from other repositories