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 commands/quantumaikr/quant.cpp/scoregit clone --depth 1 https://github.com/quantumaikr/quant.cppWhat 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 | $0.00011 | $0.00177 |
| Opus 5 | $0.00005 | $0.00088 |
| Sonnet 5 | $0.00002 | $0.00035 |
| Haiku 4.5 | $0.00001 | $0.00018 |
Grade A, and why
score 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 today.
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.
What it actually says
Score
Run the quant.cpp scoring harness to measure project completeness across 5 dimensions.
Steps
- Run
bash score.sh(full evaluation) using the Bash tool - Read the
.scorefile for the numeric score - Present the results to the user in a clear summary:
- Total score (X.XXXX / 1.0000)
- Each dimension's percentage (structure, correctness, quality, performance, integration)
- The LOWEST scoring dimension (this is the bottleneck)
- Specific items scoring 0 that could be improved next
- If
.score_historyexists, show the trend (improving/declining/stagnant) - Suggest the single highest-impact next action based on the score breakdown
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.
- today First seen · 20 lines · 11 tokens per session scan A 2f2356f0340a
score is a command published in the GitHub repository quantumaikr/quant.cpp (399 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 11 tokens to every session and 177 once invoked, about $0.0001 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 2026-09-01.
Other commands, from other repositories
amk-autoresearch
Launch the unattended / overnight AMK autoresearch driver on $ARGUMENTS (model [gpu] [minutes|iters]).
amk-optimize
Drive an interactive AMK propose -> eval -> keep/revert megakernel schedule session on $ARGUMENTS (model [gpu]).
amk-compile
One-shot compile + verify a model into a CUDA megakernel via amk compile on $ARGUMENTS (model [gpu]).
ARCHITECTURE
Command "ARCHITECTURE" from cloudrift-ai/emmy, covering commands architecture, layered design, layers, emmy/recipe/ — recipe library and emmy/deploy/ — deploy library.
EXPECT
Expect command describes the desired output of the task (after post-processing).
FORMAT
Format command describes the desired output of the task (after post-processing).