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 instructions/skblaz/py3plex/copilot-instructionsgit clone --depth 1 https://github.com/SkBlaz/py3plexWhat 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 | $0.02573 | $0.02573 |
| Opus 5 | $0.01287 | $0.01287 |
| Sonnet 5 | $0.00515 | $0.00515 |
| Haiku 4.5 | $0.00257 | $0.00257 |
Grade A, and why
py3plex 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 2d 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 — 309 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitHub Copilot Instructions for py3plex
This file provides context and guidelines for GitHub Copilot when working with the py3plex codebase.
Project Overview
py3plex is a Python library for analyzing and visualizing multilayer and multiplex networks. It provides:
- Core Features: Native support for multilayer network structures, SQL-like DSL for network queries, dplyr-style chainable API, sklearn-style pipelines
- Version: 1.0 (see
pyproject.toml) - Python Support: 3.8+
Repository Structure
py3plex/
├── py3plex/ # Main package
│ ├── core/ # Core network classes (multinet.py, temporal_multinet.py)
│ ├── algorithms/ # Network algorithms (including temporal/)
│ ├── visualization/ # Visualization tools
│ ├── dsl/ # SQL-like DSL for queries
│ ├── io/ # I/O handlers
│ ├── datasets/ # Built-in datasets
│ ├── plugins/ # Plugin system
│ ├── dynamics/ # Network dynamics simulations
│ ├── uncertainty/ # Bootstrap and null model analysis
│ ├── nullmodels/ # Null model implementations
│ ├── multinet/ # Multilayer aggregation utilities
│ ├── cli.py # CLI entry point
│ ├── graph_ops.py # Dplyr-style API
│ ├── pipeline.py # Sklearn-style pipelines
│ ├── temporal_utils.py # Temporal network utilities
│ └── workflows.py # Config-driven workflows
├── tests/ # Test suite
├── examples/ # Example scripts (170+)
├── docfiles/ # Documentation source
├── gui/ # Web GUI (FastAPI + SvelteKit)
└── benchmarks/ # Performance benchmarks
Key Code Patterns
Network Creation
from py3plex.core import multinet
# Create network
net = multinet.multi_layer_network(directed=False)
# Add nodes (dict-based API)
net.add_nodes([
{'source': 'Alice', 'type': 'social'},
{'source': 'Bob', 'type': 'social'},
])
# Add edges (dict-based API)
net.add_edges([
{'source': 'Alice', 'target': 'Bob',
'source_type': 'social', 'target_type': 'social'},
])
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.
- 2d ago First seen · 309 lines · 2,573 tokens per session scan A 5f0f96a87e76
py3plex copilot-instructions.md is an instructions file published in the GitHub repository SkBlaz/py3plex (187 stars, last pushed 3d ago), licensed MIT. It adds 2,573 tokens to every session, about $0.0129 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 2026-08-30.
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