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Runs multi-source research across GitHub, HN, Reddit, arXiv, and Semantic Scholar.
Passed 13 of 19 test cases with the skill, and 2 of 19 without it.
and 10 more in the full report
13 fixed3 passed either way8 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 12 test cases the skill fixed. Same model, same prompt, with and without the skill.
The user marks a presented research story gap as 'act' during the final session review for 'async runtime architecture'. What action is performed?
| Checked | Without skill | With skill |
|---|---|---|
| Begins immediate work without filing a new tracking issue | Missed | Passed |
The instructions your model reads when the skill loads.
Run a full multi-source research session: classify the domain, dispatch parallel agents, synthesize findings, and output a formatted report.
tome:dig)tome:synthesize)Run the domain classifier on the topic:
from tome.scripts.domain_classifier import classify
result = classify(topic)
# result.domain, result.triz_depth, result.channel_weights
If confidence < 0.6 the classifier abstains and refines
rather than rejecting: result.candidates lists the domains
that had keyword support, triz_depth becomes the deepest
of those candidates, and channel_weights is a
support-weighted blend. Coverage widens on ambiguity instead
of narrowing, because a topic spanning several vocabularies
is exactly what the cross-domain channel is for.
Report the abstention to the user with the candidate list and let them override the domain. Do not treat a refined plan as a failure; treat it as the classifier declining to guess.
When candidates is empty the topic produced no keyword hits
at all. That stays on the cheap two-channel plan, since there
is nothing to refine toward and escalating noise wastes
budget. If the topic is genuinely researchable, the
test cases passed, vs 2 / 19 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.