appreciative-discovery
Appreciative-discovery identifies and analyzes positive deviants, organizations or systems performing exceptionally well, to extract transferable best practices and enabling conditions. Use this Claude Code skill when researching what makes high-performing outliers successful, seeking actionable principles applicable to improving similar contexts, or conducting organizational benchmarking focused on learning from excellence rather than failure analysis.
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/appreciative-discovery && cp -r /tmp/appreciative-discovery/skills/appreciative-discovery ~/.claude/skills/appreciative-discoverySKILL.md
## Execution Subagent — spawned via subagent-spawning/spawn-agent. ## Budget One unit = one complete appreciative discovery (positive deviants + enabling conditions + principles). <!-- BEGIN available-tables (generated) --> ## Available SOPs Optional, no fixed order; the final leaf is always a sop. | SOP | When to use | | --- | --- | | spawn-agent | Spawn a customized CC subagent with full MCP tool access. Used by SOPs that declare execution: subagent. | <!-- END available-tables (generated) -->
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: Inference to the best explanation in the face of anomalies
Remove components one by one, observe system changes to reveal hidden
Map system architecture to ablatable units for ablation studies
Design ablation studies to isolate component contributions in ML systems