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Samples context files, reads their L0 abstract (compressed ~100 token summary) and their full content, then evaluates whether the abstract accurately represents the content. Reports fidelity scores and flags abstracts that have drifted from their source.
Passed 22 of 23 test cases with the skill, and 5 of 23 without it.
and 15 more in the full report
17 fixed5 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 17 test cases the skill fixed. Same model, same prompt, with and without the skill.
During an abstract verification run, an abstract scores a 0.82 content match score. What letter grade will be assigned to this result?
| Checked | Without skill | With skill |
|---|---|---|
| assigns letter grade A | Missed | Passed |
The instructions your model reads when the skill loads.
Cold-read L0 fidelity test — checks if abstracts accurately predict content.
Samples N contexts, takes title + L0 abstract only, scores match against full content. Dual path: LLM prediction scoring (Ollama) or Jaccard keyword similarity (fallback). Reports grades: A (≥0.8), B (≥0.6), C (≥0.4), D (≥0.2), F (<0.2).
/verify # Sample 20 contexts
/verify --sample 50 # Larger sample
/verify --node ai-masters # Specific node only
| Flag | Description | Default |
|---|---|---|
--sample | Number of contexts to test | 20 |
--node | Limit to specific node | All |
cd engine && mix optimal.verify
cd engine && mix optimal.verify --sample 50 --node ai-masters
test cases passed, vs 5 / 23 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.