formated-results
Closing skill for the research-executor, loaded as the last step of formated-specs. Summarize the design just produced into one research-result JSON fenced…
Spec-slot skill for the research-executor. Emit the 4-layer DARE orchestration of the assigned topic as one research-graph JSON fenced block in your reply. Replaces the generic spec-writing step.
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill formated-specs --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/formated-specsContext preview
The summary Claude sees to decide when to auto-load this skill.
Spec-slot skill for the research-executor. Emit the 4-layer DARE orchestration of the assigned topic as one research-graph JSON fenced block in your reply. Replaces the generic spec-writing step.
name: formated-specs description: Spec-slot skill for the research-executor. Emit the 4-layer DARE orchestration of the assigned topic as one research-graph JSON fenced block in your reply. Replaces the generic spec-writing step.
You occupy the spec step of the research executor. Instead of writing a spec file, you emit the orchestration you are about to (notionally) run as a single JSON object inside one fenced block, directly in your reply.
1. Emit exactly one fenced block opened with ` ```research-graph ` (that exact info-string, hyphen, NOT ` ```json `) and closed with ` ``` `. 2. The block body is a single valid JSON object, the schema below. 3. Emit it **into your reply (the dialogue)** — do NOT write it to a file. The block lands in this session's transcript; the harness cuts it from there. 4. Atomicity: produce the whole block within one assistant turn. 5. If you revise after pushback, emit a new full `research-graph` block; the harness keeps the LAST one. Never emit a half block. 6. Mandatory final step: after the graph block, load and run `formated-results`.
{
"nodes": [ {"id": "n1", "skill": "<skill-name>", "layer": "campaign|strategy|tactic|sop"} ],
"edges": [ {"from": "n1", "to": "n2", "kind": "calls|sequences"} ],
"layer_labels": { "n1": "campaign|strategy|tactic|sop" },
"manifest": [ "<skill actually orchestrated>" ],
"prereq_dag": [ {"node": "n2", "requires": ["n1"]} ]
}Populate `manifest` only with skills you actually orchestrated; do not fabricate. `layer` and `layer_labels` must respect the 4-layer architecture (campaign → strategy → tactic → sop). Judge nothing against academic standards; this is a structural record of orchestration only.
The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.
Repo: yogsoth-ai/de-anthropocentric-research-engine
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