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 MigoXLab/dingo --skill clawhubgit 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/clawhub)<a href="https://agentmods.dev/skills/migoxlab/dingo/clawhub"><img src="https://agentmods.dev/badge/skills/migoxlab/dingo/clawhub/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/clawhub"><img src="https://agentmods.dev/badge/skills/migoxlab/dingo/clawhub.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 7 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 88 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 114 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 115 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 Rogue Agent · line 295 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
- medium Data Exfiltration · line 278 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 467 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 477 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 | $0.00000 | $0.04436 |
| Opus 5.5 | $0.00000 | $0.01774 |
| Sonnet 5.5 | $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 1mo 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 — 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.
- 1mo ago First seen · 509 lines · 0 tokens per session scan A 8fcf25a814c5
clawhub is a skill published in the GitHub repository MigoXLab/dingo (758 stars, last pushed 3d 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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