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 skills/coyvalyss1/model-matchmaker/optimize-classifiernpx skills add coyvalyss1/model-matchmaker --skill optimize-classifiergit 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 | $0.00030 | $0.01656 |
| Opus 5 | $0.00015 | $0.00828 |
| Sonnet 5 | $0.00006 | $0.00331 |
| Haiku 4.5 | $0.00003 | $0.00166 |
Grade A, and why
optimize-classifier 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.
Optimize My Classifier
This skill helps you personalize your Model Matchmaker classifier based on your actual usage patterns. After you've collected 50+ recommendations, this skill analyzes when you disagreed with the advisor and tunes your local classifier to match your preferences.
What This Skill Does
- Reads your local Model Matchmaker logs (
~/.cursor/hooks/model-matchmaker.ndjson) - Analyzes when you overrode the advisor's recommendations
- Finds patterns in the prompts where you disagreed (common words, task types)
- Suggests keyword additions to your
model-advisor.shfile - Updates your classifier (with your approval) so future recommendations match your preferences
Privacy: Everything happens locally. No data leaves your machine. You review and approve every change.
Instructions for the AI
You are helping the user optimize their Model Matchmaker classifier based on their personal override patterns. Follow these steps:
Step 1: Read and Validate Log File
Read the NDJSON log file at ~/.cursor/hooks/model-matchmaker.ndjson.
Check if there's enough data:
- Need at least 50
recommendationevents total - Need at least 5
OVERRIDEevents to find patterns - If not enough data, tell the user: "You need more usage data. Come back after 50+ prompts with at least a few overrides."
Step 2: Analyze Override Patterns
Filter to action: "OVERRIDE" events and group by model direction:
Group A: User preferred Opus over recommended Haiku/Sonnet
- These are prompts where the classifier said "use a cheaper model" but you said "no, I need Opus"
- Extract common words from
prompt_snippetfields in this group
Group B: User preferred Sonnet over recommended Opus
- These are prompts where the classifier said "use Opus" but you said "no, Sonnet is fine"
- Extract common words from
prompt_snippetfields in this group
Group C: User preferred Sonnet over recommended Haiku
- Extract common words from
prompt_snippetfields in this group
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 · 30 tokens per session scan A 3d9c7fc30662
optimize-classifier is a skill published in the GitHub repository coyvalyss1/model-matchmaker (168 stars, last pushed 4mo ago), licensed MIT. It adds 30 tokens to every session and 1,656 once invoked, about $0.0002 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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