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 csmar432/finai-research --skill fin-ref-papergit clone --depth 1 https://github.com/csmar432/finai-researchWrote 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/csmar432/finai-research/fin-ref-paper)<a href="https://agentmods.dev/skills/csmar432/finai-research/fin-ref-paper"><img src="https://agentmods.dev/badge/skills/csmar432/finai-research/fin-ref-paper/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/csmar432/finai-research/fin-ref-paper"><img src="https://agentmods.dev/badge/skills/csmar432/finai-research/fin-ref-paper.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 69 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 96 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00047 | $0.02933 |
| Opus 5 | $0.00023 | $0.01466 |
| Sonnet 5 | $0.00009 | $0.00587 |
| Haiku 4.5 | $0.00005 | $0.00293 |
Grade A, and why
fin-ref-paper 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 11d 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.
resp = requests.get( How it starts
The opening of the file, as written. The whole thing — 377 lines — stays where its author put it; the contents beside it link to each section on GitHub.
fin-ref-paper
从LIT_REVIEW.md和IDEA_REPORT.md中提取参考文献,自动生成符合JF/JFE/RFS/GB-T-7714格式的references.bib,并管理引用一致性。
触发条件
- 关键词:
参考文献refbib引用格式citationreference文献引用BibTeX - Skill语法:
Skill: fin-ref-paper - 前置条件: 已有研究输出文件 (LIT_REVIEW.md, IDEA_REPORT.md 等)
参考文献管理流程
第一步:提取参考文献
从现有文档中自动提取参考文献信息:
from scripts.ref_paper import ReferenceExtractor, ExtractedReference
extractor = ReferenceExtractor()
# 从多个来源提取
extracted = extractor.extract_from_multiple(
sources=[
"output/fin-literature/LIT_REVIEW.md",
"output/fin-ideas/IDEA_REPORT.md",
"output/fin-novelty/NOVELTY_REPORT.md",
]
)
print(f"提取到 {len(extracted)} 条参考文献")
for ref in extracted[:3]:
print(f" - {ref.author} ({ref.year}). {ref.title}. {ref.journal}.")
提取的信息:
| 字段 | 说明 | 示例 |
|---|---|---|
| author | 作者 | "Zhang, Y. and Li, X." |
| title | 标题 | "The Effect of Carbon Trading" |
| year | 年份 | 2023 |
| journal | 期刊 | "Journal of Finance" |
| volume | 卷 | "78" |
| issue | 期 | "3" |
| pages | 页码 | "1234-1289" |
| doi | DOI | "10.1111/jofi.12345" |
| url | URL | (optional) |
第二步:Enrich元数据
使用CrossRef API自动补全缺失信息:
import requests
def get_crossref_metadata(doi: str) -> dict:
"""通过DOI获取完整的文献元数据"""
resp = requests.get(
f"https://api.crossref.org/works/{doi}",
headers={"User-Agent": "fin-research/1.0 (mailto:[email protected])"}
)
if resp.status_code == 200:
return resp.json()["message"]
return {}
def enrich_reference(ref: ExtractedReference) -> ExtractedReference:
"""补全缺失字段"""
if ref.doi:
meta = get_crossref_metadata(ref.doi)
ref.volume = meta.get("volume", ref.volume)
ref.issue = meta.get("issue", ref.issue)
ref.pages = meta.get("page", ref.pages)
ref.publisher = meta.get("publisher", "")
# 规范化作者列表
authors_raw = meta.get("author", [])
ref.author = "; ".join([
f"{a['family']}, {a['given'][0]}."
for a in authors_raw
])
return ref
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.
- 11d ago First seen · 377 lines · 47 tokens per session scan A ba74b542b352
fin-ref-paper is a skill published in the GitHub repository csmar432/finai-research (100 stars, last pushed 2d ago), licensed MIT. It adds 47 tokens to every session and 2,933 once invoked, about $0.0002 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.
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