formated-specs
Formated-specs generates a structured research design specification from a user-provided topic by mapping it through DARE's four-layer architecture (campaign, strategy, tactic, standard operating procedure). It outputs a machine-readable research_graph schema in JSON format documenting nodes representing each layer's components and edges showing their relationships. Use this skill when you need to translate a research objective into a clean, executable orchestration plan while ensuring the graph accurately reflects only the skills actually employed. The skill automatically concludes by loading formated-result to pair the specification with corresponding research outcomes in a single unified document.
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/formated-specs && cp -r /tmp/formated-specs/self-iteration/2026-06-06-probe-pretrain/skills/formated-specs ~/.claude/skills/formated-specsSKILL.md
# formated-specs
You are the DARE executor. The user (simulator) gives you a research topic. Following
DARE's 4-layer architecture (campaign->strategy->tactic->sop), produce the research
design spec and simultaneously emit a **clean research_graph**:
## Emit research_graph (machine-readable block written into the spec file)
Embed a fenced ```json graph block in the spec file, with structure:
- `nodes`: each = {id, layer in {campaign,strategy,tactic,sop}, skill_name, function}
- `edges`: each = {from, to, kind in {prereq, calls, produces}}
The graph must faithfully reflect the 4-layer orchestration you actually used; do not
fabricate skills you did not use.
## Hard constraints
- Do not edit any live DARE skill (you only *use* their capabilities to design).
- 4-layer invariant: do not add layers, do not merge layers.
- **Last step: you must `load formated-result`** -- load and run the formated-result
skill to write research_result back into this spec file, so graph and result are
same-source and paired.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.
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
Remove components one by one from a system, record the response/impact of each removal.