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 agentmods add skills/migoxlab/dingo/clawhubnpx skills add MigoXLab/dingo --skill clawhubgit clone --depth 1 https://github.com/MigoXLab/dingoWhat 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 | $0.00000 | $0.04436 |
| Opus 5 | $0.00000 | $0.02218 |
| Sonnet 5 | $0.00000 | $0.00887 |
| Haiku 4.5 | $0.00000 | $0.00444 |
Grade A, and why
clawhub 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 yesterday.
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 — 509 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Quality Evaluation with Dingo
Dingo: A Comprehensive AI Data, Model and Application Quality Evaluation Tool.
- GitHub: https://github.com/MigoXLab/dingo
- SaaS Platform: https://dingo.openxlab.org.cn/ (free, no install needed)
- PyPI: https://pypi.org/project/dingo-python/
Installation
pip install dingo-python
Optional extras
pip install "dingo-python[agent]" # Agent-based evaluation (fact-checking)
pip install "dingo-python[hhem]" # HHEM hallucination detection
pip install "dingo-python[all]" # Everything
Verify installation
python -c "from dingo.config import InputArgs; print('Dingo OK')"
Two evaluation modes
| Rule-based | LLM-based | |
|---|---|---|
| API key required | No | Yes (any OpenAI-compatible API) |
| Speed | Fast | Slower (API calls) |
| Cost | Zero | Per-token cost |
| Metrics | 50+ deterministic rules | Text quality, RAG, 3H, security |
| Best for | Format checks, PII, completeness | Semantic quality, faithfulness |
Core workflow
- Prepare data: JSONL, JSON, CSV, plaintext, or Parquet file
- Choose evaluators: Rule-based (free, fast) or LLM-based (semantic understanding)
- Run evaluation: CLI with config file or Python SDK
- Review results:
summary.json+ per-item JSONL reports in output directory
CLI Usage
Dingo CLI takes a JSON config file as input:
dingo eval --input config.json
Minimal rule-based config
{
"input_path": "data.jsonl",
"dataset": {"source": "local", "format": "jsonl"},
"evaluator": [
{
"fields": {"content": "content"},
"evals": [
{"name": "RuleColonEnd"},
{"name": "RuleSpecialCharacter"},
{"name": "RuleContentNull"}
]
}
]
}
LLM-based config
{
"input_path": "data.jsonl",
"dataset": {"source": "local", "format": "jsonl"},
"evaluator": [
{
"fields": {"content": "content"},
"evals": [
{
"name": "LLMTextRepeat",
"config": {
"model": "deepseek-chat",
"key": "${OPENAI_API_KEY}",
"api_url": "https://api.deepseek.com/v1"
}
}
]
}
]
}
What ships with it
4 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.
- yesterday First seen · 509 lines · 0 tokens per session scan A 8fcf25a814c5
clawhub is a skill published in the GitHub repository MigoXLab/dingo (751 stars, last pushed 4d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 4,436 tokens. 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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