appreciative-reframing
Appreciative Reframing identifies positive deviants and enabling conditions within a problem domain, then reframes challenges from deficit-based to asset-based perspectives using Appreciative Inquiry methodology. Use this skill when researching organizational, social, or systemic issues where discovering existing success patterns and working solutions would be more generative than focusing on failures or gaps.
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/appreciative-reframing && cp -r /tmp/appreciative-reframing/skills/appreciative-reframing ~/.claude/skills/appreciative-reframingSKILL.md
# Appreciative Reframing Instead of "what's wrong?", ask "what's already working and why?" ## Budget | Base SOP | Target | ±10% Range | |----------|--------|------------| | web-search | 25 | 22–28 | | web-research | 10 | 9–11 | | paper-overview | 25 | 22–28 | | paper-search | 15 | 13–17 | | paper-research | 5 | 4–6 | ## State Ledger ``` <HARD-GATE> | SOP | Done | Target | % | |-----|------|--------|---| | web-search | ? | 25 | ? | | web-research | ? | 10 | ? | | paper-overview | ? | 25 | ? | | paper-search | ? | 15 | ? | | paper-research | ? | 5 | ? | Budget Gate: OPEN/CLOSED (>=80% required to exit) </HARD-GATE> ``` ## Available SOPs **Import:** web-search, web-research, paper-overview, paper-search, paper-research **Subagent:** appreciative-discovery, reformulation-synthesis ## Execution Guidance Find positive deviants (cases that succeed despite the general problem), identify their enabling conditions, reframe the problem as "how to create more of what already works."
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