What is goldencheck copilot-instructions.md?

Instructions for GoldenCheck, a tool that finds and validates data-quality rules from data files. It includes its commands, internal structure, and common code patterns.

Scanning, validating, comparing, watching, and safely fixing data files, including checks for domain-specific data types.

It gives a coding agent the project context needed to run checks, understand the codebase, and make compatible changes.

goldencheck copilot-instructions.md — Instructions file for GitHub Copilot by benseverndev-oss: Instructions for GoldenCheck, a tool that fi

Install

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.

agentmods

npx agentmods add instructions/benseverndev-oss/goldencheck/copilot-instructions

Clone the repo

git clone --depth 1 https://github.com/benseverndev-oss/goldencheck

Made for: GitHub Copilot.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/instructions/benseverndev-oss/goldencheck/copilot-instructions.svg)](https://agentmods.dev/instructions/benseverndev-oss/goldencheck/copilot-instructions)

Your own site

<a href="https://agentmods.dev/instructions/benseverndev-oss/goldencheck/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/benseverndev-oss/goldencheck/copilot-instructions.svg" alt="Measured on agentmods" height="20"></a>

Per session 1,906 This file is loaded in full into every session.

When invoked 1,906 The same file — it is already loaded in full.

Security scan A 0 findings. Scan, not verified.

Origin original No closer match found in the catalogue.

Token cost

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	$0.01906	$0.01906
Opus 5	$0.00953	$0.00953
Sonnet 5	$0.00381	$0.00381
Haiku 4.5	$0.00191	$0.00191

Measured 5d ago against content hash 8064d99945a4, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

goldencheck copilot-instructions.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 5d 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

goldencheck AGENTS.md — 100% identical, 0 lines differ

.github/copilot-instructions.md · 158 lines

How it starts

The opening of the file, as written. The whole thing — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.

GoldenCheck

Data validation that discovers rules from your data. DQBench Score: 88.40.

Commands

pip install -e ".[dev]"          # Dev install
pip install -e ".[llm]"          # With LLM boost
pip install -e ".[mcp]"          # With MCP server
pytest --tb=short -v             # Run tests (189+ passing)
ruff check .                     # Lint
ruff check . --fix               # Auto-fix lint
goldencheck data.csv --no-tui    # Scan a file (CLI output)
goldencheck data.csv             # Scan with TUI
goldencheck validate data.csv    # Validate against goldencheck.yml
goldencheck diff old.csv new.csv # Compare two files
goldencheck fix data.csv         # Auto-fix (safe mode)
goldencheck watch data/          # Poll directory for changes
goldencheck scan data.csv --domain healthcare  # Domain-specific types

Architecture

goldencheck/
├── cli/           # Typer CLI (9 commands: scan, validate, review, diff, watch, fix, learn, mcp-serve)
├── engine/        # Scanner, validator, confidence, fixer, differ, watcher
├── profilers/     # 10 column profilers (BaseProfiler ABC)
├── relations/     # Cross-column profilers (temporal, null correlation, numeric cross, age validation)
├── semantic/      # Type classifier + suppression engine + domain packs (healthcare, finance, ecommerce)
├── llm/           # LLM boost (providers, prompts, merger, budget, rule generator)
├── mcp/           # MCP server (9 tools incl. domain discovery)
├── config/        # Pydantic YAML config (goldencheck.yml)
├── models/        # Finding (with metadata dict), Profile dataclasses
├── notebook.py    # ScanResult wrapper + HTML renderers for Jupyter/Colab
├── reporters/     # Rich, JSON, CI output
└── tui/           # Textual TUI (4 tabs)

Pipeline Flow

read_file → maybe_sample → run profilers → classify semantic types
→ apply suppression → corroboration boost → sort by severity
→ (optional) LLM boost → confidence downgrade → report/TUI

Key Patterns

All profilers extend BaseProfiler with profile(df, column, *, context=None) -> list[Finding]
Findings are dataclasses — use dataclasses.replace(), never mutate
Confidence 0.0-1.0 on every Finding — high (≥0.8), medium (0.5-0.79), low (<0.5)
Severity: ERROR > WARNING > INFO (IntEnum)
source field: None = profiler, "llm" = LLM-generated
Polars-native — all data ops use Polars, never pandas
stdlib random only — no numpy for randomness

Read the full file on GitHub · 158 lines

Changes

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.

5d ago First seen · 158 lines · 1,906 tokens per session scan A 8064d99945a4

Subscribe to this mod's changes

goldencheck copilot-instructions.md is an instructions file published in the GitHub repository benseverndev-oss/goldencheck (2 stars, last pushed 4mo ago), licensed MIT. It adds 1,906 tokens to every session, about $0.0095 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 2026-08-31.

cli data-engineering data-quality data-validation polars profiling

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goldencheck copilot-instructions.md