digoal/blog is a large collection of Chinese-language articles, courses, videos, and practical learning materials about databases, especially PostgreSQL and related systems, along with topics such as AI, open source, business, and finance. It is for database administrators, developers, architects, and others learning database technologies and their applications.
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 digoal/blog --skill article-fact-checkergit clone --depth 1 https://github.com/digoal/blogWrote 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/digoal/blog/article-fact-checker)<a href="https://agentmods.dev/skills/digoal/blog/article-fact-checker"><img src="https://agentmods.dev/badge/skills/digoal/blog/article-fact-checker/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/digoal/blog/article-fact-checker"><img src="https://agentmods.dev/badge/skills/digoal/blog/article-fact-checker.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 | $0.00096 | $0.01920 |
| Opus 5 | $0.00048 | $0.00960 |
| Sonnet 5 | $0.00019 | $0.00384 |
| Haiku 4.5 | $0.00010 | $0.00192 |
Grade A, and why
article-fact-checker 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 9d 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.
The source is not reproduced here
Licensed GPL-2.0
The repository is licensed GPL-2.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- agents/openai.yaml 231 B
- assets/report-template.md 6.3 KB
- references/evidence-verification.md 4.2 KB
- references/layer1-overall.md 3.1 KB
- references/layer2-paragraph.md 3.1 KB
- references/layer3-sentence.md 3.7 KB
- references/logical-fallacies.md 4.0 KB
- references/methodology.md 3.6 KB
- references/qualitative-assessment.md 4.4 KB
- scripts/evidence-extractor.py 9.8 KB runs code
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.
- 9d ago First seen · 153 lines · 96 tokens per session scan A 10f6e3291d8d
article-fact-checker is a skill published in the GitHub repository digoal/blog (8,568 stars, last pushed today), licensed GPL-2.0. It adds 96 tokens to every session and 1,920 once invoked, about $0.0005 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 2026-08-30.
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