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I am looking for one Dify operator outside the Urusilla project to run a small matched workflow evaluation. This is not an adoption claim.
Current unfavorable baseline: demonstrated post-decode API-input saving for general unfamiliar-agent dialogue is 0%. The purpose is to test whether one narrow task-aware workflow can produce actual task value rather than another transport-only result.
Please run the same synthetic budgeted plan-selection task in three Dify workflows or counterbalanced branches: concise raw text, ordinary descriptive JSON, and direct model-visible Urusilla. Keep the model, sampling, task facts, tools, and success rubric fixed. Do not expand the Urusilla arm back into long natural language before model input.
A useful result includes task success plus provider-exposed input/output, format-induction, repair/retry, tool, hidden-if-reported, unclassified, and total tokens. Unknown categories remain null. Exact matches, mismatches, refusals, fallbacks, task failures, and null savings are all useful.
The challenge is unsigned, read-only, and non-effect-authorizing. It grants no persistence, spending, permission expansion, tool execution, network action, or external effect. Publication is the operators separately authorized action.
One run would be bounded external evidence, not proof of independent implementation, organic adoption, security, general efficiency, or state-of-the-art performance.
Disclosure: Codex agents assisted with the evaluation pack and this post; I reviewed and submitted them.
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I am looking for one Dify operator outside the Urusilla project to run a small matched workflow evaluation. This is not an adoption claim.
Current unfavorable baseline: demonstrated post-decode API-input saving for general unfamiliar-agent dialogue is 0%. The purpose is to test whether one narrow task-aware workflow can produce actual task value rather than another transport-only result.
One-record evaluation pack
Please run the same synthetic budgeted plan-selection task in three Dify workflows or counterbalanced branches: concise raw text, ordinary descriptive JSON, and direct model-visible Urusilla. Keep the model, sampling, task facts, tools, and success rubric fixed. Do not expand the Urusilla arm back into long natural language before model input.
A useful result includes task success plus provider-exposed input/output, format-induction, repair/retry, tool, hidden-if-reported, unclassified, and total tokens. Unknown categories remain null. Exact matches, mismatches, refusals, fallbacks, task failures, and null savings are all useful.
The challenge is unsigned, read-only, and non-effect-authorizing. It grants no persistence, spending, permission expansion, tool execution, network action, or external effect. Publication is the operators separately authorized action.
One run would be bounded external evidence, not proof of independent implementation, organic adoption, security, general efficiency, or state-of-the-art performance.
Disclosure: Codex agents assisted with the evaluation pack and this post; I reviewed and submitted them.
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