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.
npx skills add docxology/template --skill citationgit clone --depth 1 https://github.com/docxology/templateWrote 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/skills/docxology/template/citation)<a href="https://agentmods.dev/skills/docxology/template/citation"><img src="https://agentmods.dev/badge/skills/docxology/template/citation/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/skills/docxology/template/citation"><img src="https://agentmods.dev/badge/skills/docxology/template/citation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00144 | $0.00926 |
| Opus 5.5 | $0.00058 | $0.00370 |
| Sonnet 5.5 | $0.00029 | $0.00185 |
| Haiku 4.5 | $0.00014 | $0.00093 |
Grade A, and why
infrastructure-reference-citation 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 12d 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 — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Citation Submodule
BibTeX I/O matching projects/templates/template_code_project/manuscript/references.bib.
Reading
from infrastructure.reference.citation import parse_bibfile, parse_bibtex
db = parse_bibfile("projects/templates/template_code_project/manuscript/references.bib")
print(len(db)) # 8
print(db.keys()) # ['nocedal2006numerical', ...]
entry = db.find("boyd2004convex")
print(entry.entry_type) # 'article'
print(entry.get("author")) # 'Boyd, Stephen and Vandenberghe, Lieven'
print(db.preamble) # The @comment{...} block
Writing
from collections import OrderedDict
from infrastructure.reference.citation import (
BibEntry, BibDatabase, render_database, write_bibfile
)
db = BibDatabase()
db.add(BibEntry(
"article", "smith2024example",
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_database(db))
write_bibfile("output/refs.bib", db)
Convert from a search result
from infrastructure.reference.citation import paper_to_bibentry, generate_citation_key
from infrastructure.search.literature import Paper
paper = Paper(id="x", title="Adam", authors=["Kingma, Diederik P", "Ba, Jimmy"], year=2014, venue="ICLR", venue_type="conference")
entry = paper_to_bibentry(paper) # entry_type → "inproceedings", key → "kingma2014adam"
# Override either field:
entry = paper_to_bibentry(paper, citation_key="my_custom_key", entry_type="misc")
# Or generate a key without converting:
key = generate_citation_key(authors=["Cauchy, Augustin-Louis"], year=1847, title="Méthode générale")
# → "cauchy1847methode"
CLI
# Validate
uv run python -m infrastructure.reference.citation.cli validate refs.bib --strict
# Re-format in canonical layout
uv run python -m infrastructure.reference.citation.cli format refs.bib
# Convert literature-search JSON → BibTeX
uv run python -m infrastructure.reference.citation.cli convert papers.json refs.bib
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago Changed · +2 tokens per session 8d2607f642ca
- 1mo ago First seen · 101 lines · 142 tokens per session scan A 89442960db56
infrastructure-reference-citation is a skill published in the GitHub repository docxology/template (20 stars, last pushed today), licensed Apache-2.0. It adds 144 tokens to every session and 926 once invoked, about $0.0006 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 2026-09-03.
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