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/coyvalyss1/model-matchmaker/cursorrulesgit clone --depth 1 https://github.com/coyvalyss1/model-matchmakerWhat 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 | $0.01515 | $0.01515 |
| Opus 5 | $0.00758 | $0.00758 |
| Sonnet 5 | $0.00303 | $0.00303 |
| Haiku 4.5 | $0.00152 | $0.00152 |
Grade A, and why
cursorrules 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 — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model Matchmaker - Cursor AI Rules
Shared Rules (ALWAYS apply these)
This project is independent but follows Valyss ecosystem standards. Apply ALL rules from:
~/.cursorrules(global: git safety, secrets, documentation, writing voice, public content security)~/.cursor/rules/git-workflow.mdc(commit workflow)~/.cursor/rules/public-content-security.mdc(sanitization for open-source/shared content)
Project Overview
Model Matchmaker is a local Cursor hook that classifies prompts before sending and recommends the right Claude model. Three files: model-advisor.sh, session-init/track-completion hooks.
Repository Content Rules
This repo contains ONLY code and technical documentation.
What Goes in This Repo
- Hook scripts (
.shfiles) - Configuration files (
hooks.json) - Technical docs (README.md, AUTO_SWITCH_SETUP.md, troubleshooting)
- Code comments and API documentation
- Test harnesses and examples
What Does NOT Go Here
- Marketing content (release posts, social media copies, promotion materials)
- Business planning (strategy docs, roadmap justifications, partnerships)
- Personal drafts (pre-write content, brainstorms, unfinished ideas)
All marketing/business content goes to: a separate private directory outside this repo (e.g., ~/private-docs/model-matchmaker-release/)
This ensures the repo stays technical and doesn't accidentally expose business decisions to the public.
Git Workflow
Commit Early, Commit Often
- Commit after each logical unit (one feature, one fix, one refactor)
- Commit messages explain WHY, not just WHAT
- Format:
<type>: <message>where type is one of: feat, fix, refactor, docs, test, chore
Examples
feat: add auto-switch feature for automatic model changing
fix: handle edge case in prompt classification for long analytics strings
docs: update README with auto-switch setup instructions
refactor: extract keyword lists into separate configuration arrays
chore: update analytics script output format
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 · 192 lines · 1,515 tokens per session scan A 1e072b765672
cursorrules is a cursor rule published in the GitHub repository coyvalyss1/model-matchmaker (167 stars, last pushed 4mo ago), licensed MIT. It adds 1,515 tokens to every session, about $0.0076 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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python_lib
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