ask-obstacle-acceptance
The ask-obstacle-acceptance skill presents identified obstacles with severity assessments, mitigations, and effort estimates to users, then requests their acceptance through structured dialogue. Use this skill after obstacles have been identified to gauge whether a proposed direction remains viable, or whether investigation should return to candidate generation.
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/ask-obstacle-acceptance && cp -r /tmp/ask-obstacle-acceptance/skills/ask-obstacle-acceptance ~/.claude/skills/ask-obstacle-acceptanceSKILL.md
# Ask Obstacle Acceptance Get user's acceptance of the obstacle landscape. ## Execution Dialogue — inline, no subagent. ## What to Present Each obstacle with: - Severity assessment - Proposed mitigation - Estimated effort ## What to Ask (one at a time) - Given these obstacles and mitigations, can you accept this direction? - Are there difficulties I missed that you're aware of? - Is there any obstacle here that's a complete deal-breaker? ## Search Optional — may use the 5 imported skills if user raises new concerns that need investigation. ## If Unacceptable Signal to tactic that direction needs to change (return to present-candidates in direction-narrowing tactic). ## Output User's acceptance decision + any new concerns raised.
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