Borrowing it
Nothing to install: this file belongs to MigoXLab/dingo. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/MigoXLab/dingo/main/.claude/skills/dingo-verify/SKILL.mdgit clone --depth 1 https://github.com/MigoXLab/dingoWrote 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/migoxlab/dingo/dingo-verify)<a href="https://agentmods.dev/skills/migoxlab/dingo/dingo-verify"><img src="https://agentmods.dev/badge/skills/migoxlab/dingo/dingo-verify/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/migoxlab/dingo/dingo-verify"><img src="https://agentmods.dev/badge/skills/migoxlab/dingo/dingo-verify.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 | $0.00070 | $0.00770 |
| Opus 5.5 | $0.00028 | $0.00308 |
| Sonnet 5.5 | $0.00014 | $0.00154 |
| Haiku 4.5 | $0.00007 | $0.00077 |
Grade A, and why
dingo-verify 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 2mo 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dingo Article Fact-Checker
Verify factual claims in articles using Dingo's ArticleFactChecker agent.
Prerequisites
Before running, verify:
- Dingo is installed:
python -c "from dingo.config import InputArgs; print('OK')" OPENAI_API_KEYis set (required)TAVILY_API_KEYis set (optional, enables web search verification)
If prerequisites fail, help the user fix them:
- Missing dingo:
pip install -e .(from project root) orpip install dingo-python - Missing LangChain:
pip install -r requirements/agent.txt - Missing API key:
export OPENAI_API_KEY='your-key'
Usage
Run the fact-check script with the article path:
python ${CLAUDE_SKILL_DIR}/scripts/fact_check.py $ARGUMENTS
Optional arguments
--model MODEL: Override LLM model (default: envOPENAI_MODELorgpt-5.4-mini)--max-claims N: Max claims to extract (default: 50, reduce for faster runs)--max-concurrent N: Parallel verification slots (default: 5)
Supported file formats
.md,.txt: Markdown/plaintext articles (auto-wrapped for Dingo).jsonl: JSONL with{"content": "..."}per line.json: JSON array format
Presenting Results
The script outputs JSON to stdout. Parse it and present to the user:
Success output
Present a formatted report with these sections:
- Summary: total claims, accuracy score, false/unverifiable counts
- False Claims Table: if any false claims found, show claim vs truth vs evidence
- All Claims Overview: list all claims with their verdicts (TRUE/FALSE/UNVERIFIABLE)
- Output Path: where the full Dingo report is saved
Example presentation:
## Article Fact-Check Report
**Accuracy**: 73.3% (11/15 claims verified true)
- Verified True: 11
- Verified False: 2
- Unverifiable: 2
### False Claims Found
| # | Article Claimed | Actual Truth | Evidence |
|---|----------------|-------------|----------|
| 1 | "released in Nov 2024" | Released Dec 5, 2024 | Official announcement |
### Full Report
Saved to: outputs/20260318_143022_abc123/
What ships with it
2 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.
- 2mo ago First seen · 96 lines · 70 tokens per session scan A e5e49a708edd
dingo-verify is a skill published in the GitHub repository MigoXLab/dingo (766 stars, last pushed yesterday), licensed Apache-2.0. It adds 70 tokens to every session and 770 once invoked, about $0.0003 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 2026-08-30.
dingo-verify compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| dingo-verifyMigoXLab/dingo | Skill | 766 | yesterday | Apache-2.0 | A | 70 | Claude Code |
| evalscopemodelscope/evalscope | Skill | 3.7k | yesterday | Apache-2.0 | A | 195 | Claude Code, Codex |
| tao-generate-video-reasoning-annotationsNVIDIA-TAO/tao-skill-bank | Skill | 99 | yesterday | Apache-2.0 | A | 134 | Claude Code |
| data-validatorfafa-ai-data-lab/ai-data-analyst-agent | Skill | 46 | 4mo ago | MIT | A | 112 | Claude Code |
| data-engineerbuiphucminhtam/forgewright | Skill | 49 | 5d ago | none | A | 85 | Claude Code |
| verify-with-andongulmezeren2-byte/andon | Skill | 1 | 3mo ago | MIT | A | 48 | Claude Code, Codex |
| data-quality-fixerauron-lmh/data-quality-loop | Skill | 0 | 3mo ago | MIT | A | 61 | Claude Code, Codex |