normsync copilot-instructions.md

Project-specific instructions for GitHub Copilot, an AI coding assistant, covering the normsync Python project. They describe its code rules and how to add output formats or integrations.

In plain words
What is it for?
Guide work on normsync, including locating modules, following strict type and lint rules, and adding tested output formats or framework adapters.
Why use it?
They give the assistant project context and conventions so generated changes fit the existing codebase.

Instructions file for GitHub Copilot

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/sandeep-alluru/normsync/copilot-instructions
Clone the repo
git clone --depth 1 https://github.com/sandeep-alluru/normsync

Made for: GitHub Copilot.

Per session 246 This file is loaded in full into every session.
When invoked 246 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.

ModelPer sessionOnce invoked
Fable 5 $0.00246 $0.00246
Opus 5 $0.00123 $0.00123
Sonnet 5 $0.00049 $0.00049
Haiku 4.5 $0.00025 $0.00025

Measured 2d ago against content hash 27077f9b6722, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

normsync 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

.github/copilot-instructions.md · 35 lines

What it actually says

GitHub Copilot Instructions — normsync

normsync: World constitution engine for norm-governed multi-agent games

Module map

src/normsync/
├── # TODO: fill in after implementation

Key invariants

  • TODO: document invariants

Code style

  • Python 3.10+, type-annotated, mypy strict mode
  • Ruff lint rules: E W F I UP B S N SIM RUF PT; ignore S101 (assert in tests), N806
  • No print() in library code — use rich.console.Console
  • All public classes and functions must have docstrings
  • Tests use pytest; CLI tests use click.testing.CliRunner

Adding a new output format

  1. Add to_<format>(result) -> str in report.py
  2. Add format name to --format choices in cli.py
  3. Add tests

Adding a new adapter / integration

  1. Create src/normsync/instrument_<framework>.py
  2. Export from __init__.py, add to __all__ alphabetically
  3. Add tests
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.

  1. 2d ago First seen · 35 lines · 246 tokens per session scan A 27077f9b6722

Subscribe to this mod's changes

normsync copilot-instructions.md is an instructions file published in the GitHub repository sandeep-alluru/normsync (0 stars, last pushed 16d ago), licensed MIT. It adds 246 tokens to every session, about $0.0012 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.