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/AGENTS.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/instructions/migoxlab/dingo/agents-md)<a href="https://agentmods.dev/instructions/migoxlab/dingo/agents-md"><img src="https://agentmods.dev/badge/instructions/migoxlab/dingo/agents-md/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/instructions/migoxlab/dingo/agents-md"><img src="https://agentmods.dev/badge/instructions/migoxlab/dingo/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.03364 | $0.03364 |
| Opus 5.5 | $0.01346 | $0.01346 |
| Sonnet 5.5 | $0.00673 | $0.00673 |
| Haiku 4.5 | $0.00336 | $0.00336 |
Grade A, and why
dingo AGENTS.md 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 — 318 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dingo — Agent Instructions
Project Overview
Dingo is a comprehensive AI data quality evaluation tool for ML practitioners, data engineers, and AI researchers. It systematically assesses training data, fine-tuning datasets, and production AI systems using rule-based, LLM-based, and agent-based evaluation methods.
Repository: https://github.com/MigoXLab/dingo
PyPI: pip install dingo-python
License: Apache 2.0
Tech Stack
| Layer | Technology |
|---|---|
| Language | Python 3.10+ |
| Data Models | Pydantic (BaseModel, extra="allow") |
| LLM Integration | OpenAI SDK (supports any compatible API) |
| MCP Server | FastMCP + SSE transport |
| Distributed | PySpark (optional) |
Directory Structure
dingo/
├── AGENTS.md ← this file (agent instructions)
├── setup.py ← package config (extras_require for optional deps)
├── mcp_server.py ← MCP server entry point (legacy, use `dingo serve` instead)
├── requirements/
│ ├── runtime.txt ← core dependencies (minimal)
│ ├── datasource.txt ← optional datasource deps (S3, SQL, Parquet, etc.)
│ ├── optional.txt ← heavy optional deps (torch, pyspark, etc.)
│ └── agent.txt ← agent evaluation deps (langchain, tavily)
│
├── SKILL.md ← AI agent skill definition (symlink → clawhub/SKILL.md)
├── dingo/ ← core Python package
│ ├── config/
│ │ └── input_args.py ← InputArgs, EvalPiplineConfig, EvaluatorGroupConfig
│ ├── io/
│ │ ├── input/data.py ← Data model (Pydantic, extra="allow")
│ │ └── output/ ← ResultInfo, EvalDetail, SummaryModel, BenchmarkReport
│ │ └── benchmark_report.py ← Multi-system/dataset comparison & aggregation
│ ├── data/
│ │ ├── datasource/ ← LocalDataSource, SQLDataSource, S3DataSource, HFDataSource
│ │ ├── dataset/ ← Dataset implementations per source
│ │ ├── searcher.py ← Searcher protocol + SearchResult + registry (mock, ES)
│ │ └── converter/ ← Format converters (JSON, JSONL, CSV, Parquet, MinerU, etc.)
│ ├── model/
│ │ ├── model.py ← Model registry (rule_register, llm_register)
│ │ ├── rule/ ← Rule-based evaluators (80+ built-in)
│ │ │ ├── base.py ← BaseRule
│ │ │ ├── rule_common.py ← Common rules (text quality, format, PII, etc.)
│ │ │ ├── guobiao/
│ │ │ │ └── rule_tc609_quality.py ← TC609 quality metrics and placeholders
│ │ │ ├── rule_search_ranking.py ← IR ranking metrics (NDCG, MRR, Recall, Precision, MAP, HitRate)
│ │ │ └── utils/ ← Shared utilities (normalize, ngrams, etc.)
│ │ └── llm/ ← LLM-based evaluators
│ │ ├── base_openai.py ← BaseOpenAI (base class for all LLM evaluators)
│ │ ├── text_quality/ ← Text quality evaluators (V4, V5)
│ │ ├── rag/ ← RAG metrics (Faithfulness, Precision, Recall, etc.)
│ │ ├── llm_search_result_relevance.py ← Search result relevance (Exa-style pointwise)
│ │ ├── hhh/ ← 3H evaluators (Honest, Helpful, Harmless)
│ │ ├── compare/ ← Document comparison evaluators
│ │ └── agent/ ← Agent-based evaluators
│ │ ├── base_agent.py ← BaseAgent
│ │ ├── tools/ ← Tool registry + implementations
│ │ ├── agent_fact_check.py
│ │ └── agent_hallucination.py
│ ├── exec/
│ │ ├── local.py ← LocalExecutor (single machine, cross-layer conflict detection)
│ │ ├── spark.py ← SparkExecutor (distributed)
│ │ └── retrieval.py ← RetrievalExecutor (MTEB retrieval benchmarks)
│ ├── retrieval/ ← Retrieval evaluation module
│ │ ├── search_client.py ← SearchClient ABC + registry + PaperResult/SearchResponse
│ │ ├── backends/
│ │ │ └── agentic.py ← AgenticSearchClient (local + public mode)
│ │ ├── mteb_adapter.py ← SearchClientModel (MTEB SearchProtocol adapter)
│ │ └── eval_utils.py ← normalize_title, resolve_hit, save_json
│ └── run/
│ └── cli.py ← CLI entry point (subcommands: eval, eval-retrieval, info, serve)
│
├── examples/ ← Usage examples (SDK, CLI, various scenarios)
├── test/ ← Test suite
│ ├── data/ ← Test data files
│ ├── env/ ← Test environment configs
│ └── scripts/ ← Test scripts (pytest)
└── docs/ ← Documentation
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 · 318 lines · 3,364 tokens per session scan A 58af5d192be3
dingo AGENTS.md is an instructions file published in the GitHub repository MigoXLab/dingo (766 stars, last pushed yesterday), licensed Apache-2.0. It adds 3,364 tokens to every session, about $0.0135 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 AGENTS.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| dingo AGENTS.mdMigoXLab/dingo | Instructions file | 766 | yesterday | Apache-2.0 | A | 3,364 | Codex, OpenCode |
| vscode buildNext.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 9,719 | GitHub Copilot |
| next.js AGENTS.mdvercel/next.js | Instructions file | 143k | today | MIT | A | 7,461 | Codex, OpenCode |
| langchain AGENTS.mdlangchain-ai/langchain | Instructions file | 149k | yesterday | MIT | A | 4,680 | Codex, OpenCode |
| vscode oss-third-party-notices.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 5,516 | GitHub Copilot |
| vscode copilot-instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 3,093 | GitHub Copilot |
| deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness | Instructions file | 274k | 2d ago | MIT | A | 4,313 | Codex, OpenCode |