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 rules/gosha70/code-copilot-team/copilot-conventionsgit clone --depth 1 https://github.com/gosha70/code-copilot-teamWhat 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 | $0.01259 | $0.01259 |
| Opus 5 | $0.00629 | $0.00629 |
| Sonnet 5 | $0.00252 | $0.00252 |
| Haiku 4.5 | $0.00126 | $0.00126 |
Grade A, and why
copilot-conventions 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cross-Copilot Conventions
Shared rules that apply whether using Claude Code, GitHub Copilot, Cursor, or local LLMs. These conventions ensure consistent behaviour regardless of which AI tool is driving.
Core Contract
- Read before write — understand existing code and patterns first.
- Minimal changes — only modify what was requested.
- Show your work — explain changes, provide diffs.
- Test everything — run linters and tests after code changes.
- Ask when uncertain — do not guess at ambiguous requirements.
- Verify before diagnosing — when asked to fix a reported bug, re-run the failing test or reproduce the symptom first. The issue may already be fixed. Do not spend time diagnosing a problem that no longer exists.
Target Alignment and Complexity Control
Before planning, implementing, or approving agent-produced work:
- Restate the originating goal and concrete acceptance criteria. Derived plans and agent summaries do not replace the original request.
- Map every changed file and each new abstraction, interface, state, dependency, or test layer to a current requirement or a concrete correctness or safety failure. Remove or defer anything without that link.
- Prefer the smallest solution that fits the existing architecture. Do not turn narrow work into a framework, generalized engine, speculative state model, broad hardening exercise, or unrelated cleanup.
- Keep tests, documentation, and proof proportional to the risk and acceptance criteria. More artifacts are not evidence of better alignment.
- Complete the required path before optional improvements. Record useful extras as follow-up work instead of silently expanding scope.
- Before approval, ask: What can be removed without weakening correctness, safety, or the acceptance criteria?
For a handoff from Claude Code or any other agent, explicitly classify the change as on-target, overcomplicated, or off-target, and explain the evidence. Green tests do not compensate for scope drift. Judge the artifact rather than guessing at a vendor's intent; complexity required by a concrete failure mode is not overengineering when the link is explicit.
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 · 89 lines · 1,259 tokens per session scan A c123ccc17621
copilot-conventions is a cursor rule published in the GitHub repository gosha70/code-copilot-team (6 stars, last pushed 2d ago), licensed MIT. It adds 1,259 tokens to every session, about $0.0063 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.
Other cursor rules, from other repositories
angular-20
This rule provides comprehensive best practices and coding standards for Angular development, focusing on modern TypeScript, standalone components, signals, and performance optimizations.
dev-standard
Apache Superset development standards and guidelines for Cursor IDE.
typescript
Changes to these high-fan-out internals can affect every message, delta, element, or rerun. Keep work in them minimal, and benchmark changes with representative stress-test apps.
coolify-ai-docs
Master reference to all Coolify AI documentation in .ai/ directory.
python_lib
Tips and guidelines specific to the development of the Streamlit Python library, not applicable to scripts and e2e tests.
specs
This directory contains product and tech specs for Streamlit features.