ask-intentionality
The ask-intentionality Claude Code skill conducts structured dialogue to understand a user's deep motivations, success criteria, risk tolerance, innovation preferences, collaboration style, time constraints, and learning openness through natural conversation rather than rigid questionnaires. Use this skill at the beginning of actor profiling engagements to establish context that shapes all downstream research decisions, and re-invoke it whenever new motivational questions emerge during project work.
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/ask-intentionality && cp -r /tmp/ask-intentionality/skills/ask-intentionality ~/.claude/skills/ask-intentionalitySKILL.md
# Ask Intentionality Deep WHY probing (i* Intentionality). Understanding motivation shapes every downstream decision. ## Execution Dialogue — inline, no subagent. ## What to Ask About - If user has a stated goal: Why this specific goal? What's driving it? - Motivation depth: graduation requirement / pure interest / career advancement / impact / combination - Success definition: What does "done well" look like to you? - Risk tolerance: safe incremental improvement vs. high-risk high-reward breakthrough - Innovation preference: improve existing methods vs. create entirely new approaches - Independence preference: complete solo vs. depend on collaborators - Time urgency: deadline-driven vs. long-term accumulation - Learning willingness: use familiar tools/domains vs. learn new ones ## Key Behaviors - User may decline to answer (privacy). Accept gracefully. Note that downstream work becomes broader and may require more iteration. - This SOP can be re-invoked by the tactic whenever new WHY questions emerge. - Don't ask all dimensions at once — probe naturally based on conversation flow. ## Output Intentionality section of ActorProfile.
Experiment-specific - summarize the DARE executor's research design into a clean research_result report, forced to write back into the spec file produced by formated-specs.
Experiment-specific - replaces writing-specs, emits DARE's 4-layer call plan as a clean research_graph schema. Last step forces load formated-result.
loss-1 judge - read a sample's full dialogue and decide whether the user simulator semantically enacted its Policy Card. check-blind.
loss-2 judge - pairwise quality comparison across the n rungs within one topic; decide monotonicity and endpoint separation. check-blind, D1-D5 only.
Strategy: 面对异常的最佳解释推理
Remove components one by one, observe system changes to reveal hidden dependencies and generate ideas from structural gaps.
Map system architecture to ablatable units for ablation studies
Design ablation studies to isolate component contributions in ML systems