infrastructure-reference

infrastructure-reference is a skill for Claude Code, Codex from docxology/template. It costs 131 tokens per session (1,348 once invoked), scanned A, original, Apache-2.0.

A library for managing bibliographic references in BibTeX files used by research projects. It reads, edits, converts, and writes entries while preserving their meaning and format.

In plain words
What is it for?
It is for parsing `.bib` files, building entries in code, generating citation keys, escaping LaTeX text, and exporting reference databases.
Why use it?
It reduces manual editing of reference databases and helps keep citations compatible with manuscript-rendering tools. It also supports creating entries from paper records.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It is for parsing .bib files, building entries in code, generating citation keys, escaping LaTeX text, and exporting reference databases.

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Install with agentmods
npx agentmods add skills/docxology/template/reference
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.

Any agent
npx skills add docxology/template --skill reference
Clone the repo
git clone --depth 1 https://github.com/docxology/template

Made for: Claude Code, Codex.

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 infrastructure-reference

README.md
[![agentmods](https://agentmods.dev/badge/skills/docxology/template/reference/github.svg)](https://agentmods.dev/skills/docxology/template/reference)
Your own site
<a href="https://agentmods.dev/skills/docxology/template/reference"><img src="https://agentmods.dev/badge/skills/docxology/template/reference/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 infrastructure-reference

Your own site · 80×15
<a href="https://agentmods.dev/skills/docxology/template/reference"><img src="https://agentmods.dev/badge/skills/docxology/template/reference.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 131 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,348 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00131 $0.01348
Opus 5 $0.00066 $0.00674
Sonnet 5 $0.00026 $0.00270
Haiku 4.5 $0.00013 $0.00135

Measured 5d ago against content hash efdee73b72a7, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

infrastructure-reference 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 5d ago.

The scan reads SKILL.md. This mod also ships 16 executable files (__init__.py, citation/__init__.py, citation/__main__.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

infrastructure/reference/SKILL.md · 144 lines

How it starts

The opening of the file, as written. The whole thing — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Reference Module

Bibliographic-reference workflows for the template's two-layer architecture. Output is byte-compatible with the existing projects/templates/template_code_project/manuscript/references.bib format and round-trips through the parser without semantic loss.

citation — BibTeX read/write/convert

from infrastructure.reference.citation import (
    BibEntry, BibDatabase,
    parse_bibfile, parse_bibtex, BibParseError,
    render_entry, render_database, render_entries, write_bibfile,
    paper_to_bibentry, generate_citation_key,
    escape_latex, unescape_latex,
)

Read & validate an existing .bib

db = parse_bibfile("projects/templates/template_code_project/manuscript/references.bib")
print(len(db), "entries")
boyd = db.find("boyd2004convex")
assert boyd.entry_type == "article"
assert boyd.get("author") == "Boyd, Stephen and Vandenberghe, Lieven"

Build entries programmatically

from collections import OrderedDict
entry = BibEntry(
    entry_type="article",
    citation_key="smith2024example",
    fields=OrderedDict([
        ("title", "An Example Paper"),
        ("author", "Smith, Alice and Jones, Bob"),
        ("journal", "Cambridge UP"),
        ("year", "2024"),
        ("pages", "1-10"),  # auto-normalised to "1--10"
        ("doi", "10.1234/example"),
    ]),
)
print(render_entry(entry))

Output (matches the exemplar exactly):

@article{smith2024example,
  title={An Example Paper},
  author={Smith, Alice and Jones, Bob},
  journal={Cambridge UP},
  year={2024},
  pages={1--10},
  doi={10.1234/example}
}

Convert a literature search result to BibTeX

from infrastructure.search.literature import LiteratureClient, SearchQuery, ArxivBackend
from infrastructure.reference.citation import paper_to_bibentry, render_database
from infrastructure.reference.citation.models import BibDatabase

result = LiteratureClient([ArxivBackend()]).search(SearchQuery(text="adam optimizer"))
db = BibDatabase()
for paper in result.papers:
    db.add(paper_to_bibentry(paper))
write_bibfile("output/references.bib", db)

Read the full file on GitHub · 144 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. 5d ago First seen · 144 lines · 131 tokens per session scan A efdee73b72a7

Subscribe to this mod's changes

infrastructure-reference is a skill published in the GitHub repository docxology/template (19 stars, last pushed today), licensed Apache-2.0. It adds 131 tokens to every session and 1,348 once invoked, about $0.0007 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 2026-09-03.

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