subagent-driven-development
OBobra/superpowers298k近 7 天新增 2,631 个 star
Build one compact choice card at a genuine mathematical-modeling judgment point. Use before method screening, after a meaningful experiment, or before final claim/freeze approval so the human chooses the trade-off while AI handles mechanical consequences.
Ask the smallest useful question that only the human modeler can answer. Present mutually exclusive options with consequences; do not turn mechanical checks into user questions.
planning/session_config.json.methods/Qx/qx_decisions.jsonl.interaction_mode; accept legacy mode for compatibility.learning: show 2–3 short questions and withhold the AI suggestion until the user answers.speed: show one compressed question and optionally show the AI suggestion alongside.rigor_profile does not change who owns the judgment.都不合适 / 补充约束 when the listed options may not cover the user's intent.learning mode before the answer.modeler-decision-logger; do not create a per-skill pending decision file.Ask only the missing high-impact items:
Do not ask the user to choose an algorithm name before evidence exists.
Example:
请选择这轮方案的首要取向:
- A. 可解释性优先——方法更透明,但可能牺牲部分拟合效果。
- B. 平衡——接受中等复杂度,要求能解释且优于可信 baseline。
- C. 性能优先——允许更复杂的方法,但需要额外稳健性和解释工作。
- D. 都不合适 / 我补充约束。
Use computed evidence to ask:
Name the consequence and evidence for each option. Do not silently convert an AI metric preference into the human verdict.
Use only when claim scope or confidence is genuinely judgment-bearing:
Return one choice_card block containing:
decision_iddecision_typequestionDo not save the card unless another skill needs a durable prompt record.
DECIDED.OBobra/superpowers298k近 7 天新增 2,631 个 star
MAmattpocock/skills288k近 7 天新增 10,178 个 star
MAmattpocock/skills288k近 7 天新增 10,178 个 star
MAmattpocock/skills288k近 7 天新增 10,178 个 star
AFaffaan-m/ECC278k近 7 天新增 4,046 个 star
AFaffaan-m/ECC278k近 7 天新增 4,046 个 star