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Get Started Free →docxology/Advanced Multi-Phase Literature Retrieval
Operations for phase-aware federated retrieval across nine literature engines, preserving provenance from broad discovery through focused synthesis.
Passed 8 of 15 test cases with the skill, and 8 of 15 without it.
and 3 more in the full report
5 fixed7 passed either way6 still failing4 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 5 test cases the skill fixed. Same model, same prompt, with and without the skill.
We have papers applying deep learning models to medical imaging and financial forecasting. Which taxonomy tier (A, B, or C) should receive these applied domain studies?
| Checked | Without skill | With skill |
|---|---|---|
| places practical application and domain deployment papers into tier C | Missed | Passed |
The instructions your model reads when the skill loads.
You are interacting with the src/literature/ module. This coordinates multi-source API harvesting across nine independently toggled engines, canonical-identifier deduplication, full-text resolution/extraction, unified bibliography export, and phase-aware synthesis.
pytest-httpserver fixtures rather than blinding the agent via mocker.patch.test cases passed, vs 8 / 15 without
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