/baseline-synthesis
Produce final structured baseline report integrating all analysis results
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill baseline-synthesis --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/baseline-synthesis
Context preview
The summary Claude sees to decide when to auto-load this skill.
Produce final structured baseline report integrating all analysis results
SKILL.md
baseline-synthesis.SKILL.mdname: baseline-synthesis
description: Produce final structured baseline report integrating all analysis results
execution: subagent
prompt: ./prompt.md
input: all_analysis_results
Baseline Synthesis
Purpose
Synthesize all outputs from the baseline establishment campaign into a single coherent report. Integrates method inventory, performance tables, condition analysis, discrepancy findings, and progress curves into an actionable baseline document.
Input Schema
| Field | Type | Description | |-------|------|-------------| | all_analysis_results | object | Combined outputs from all prior strategies and SOPs |
Output Schema
{
"report": {
"title": "string",
"task": "string",
"domain": "string",
"date_generated": "string",
"summary": "string"
},
"method_landscape": {
"total_methods": 0,
"method_families": [{"name": "string", "count": 0, "era": "string"}],
"active_methods": 0,
"deprecated_methods": 0
},
"performance_summary": {
"primary_table": {},
"sota": {"method": "string", "score": 0.0, "date": "string"},
"fair_sota": {"method": "string", "score": 0.0, "conditions": "string"},
"pareto_optimal": []
},
"reliability_assessment": {
"high_confidence_baselines": ["string"],
"questionable_baselines": ["string"],
"known_discrepancies": []
},
"progress_assessment": {
"current_status": "saturating|active_progress|early_stage",
"annual_rate": 0.0,
"headroom_remaining": 0.0,
"next_breakthrough_needed": "string"
},
"actionable_insights": [
{
"finding": "string",
"implication": "string",
"recommendation": "string"
}
]
}Read more
name: baseline-synthesis description: Produce final structured baseline report integrating all analysis results execution: subagent prompt: ./prompt.md input: all_analysis_results
Baseline Synthesis
Purpose
Synthesize all outputs from the baseline establishment campaign into a single coherent report. Integrates method inventory, performance tables, condition analysis, discrepancy findings, and progress curves into an actionable baseline document.
Input Schema
| Field | Type | Description | |-------|------|-------------| | all_analysis_results | object | Combined outputs from all prior strategies and SOPs |
Output Schema
{
"report": {
"title": "string",
"task": "string",
"domain": "string",
"date_generated": "string",
"summary": "string"
},
"method_landscape": {
"total_methods": 0,
"method_families": [{"name": "string", "count": 0, "era": "string"}],
"active_methods": 0,
"deprecated_methods": 0
},
"performance_summary": {
"primary_table": {},
"sota": {"method": "string", "score": 0.0, "date": "string"},
"fair_sota": {"method": "string", "score": 0.0, "conditions": "string"},
"pareto_optimal": []
},
"reliability_assessment": {
"high_confidence_baselines": ["string"],
"questionable_baselines": ["string"],
"known_discrepancies": []
},
"progress_assessment": {
"current_status": "saturating|active_progress|early_stage",
"annual_rate": 0.0,
"headroom_remaining": 0.0,
"next_breakthrough_needed": "string"
},
"actionable_insights": [
{
"finding": "string",
"implication": "string",
"recommendation": "string"
}
]
}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
Other skills on de-anthropocentric-research-engine.
- /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 block in your reply. Do not execute the research.
Open skill - /formated-specs
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.
Open skill - /injection-fidelity
Loss-1 judge (codex role). Given one sample's de-identified dialogue and its PolicyCard, decide axis-by-axis whether the user-simulator enacted the card's per-axis pressure. Judge enactment of the card, never whether the research is good.
Open skill - /ladder-quality-order
Loss-2 judge (codex role). Over one topic's 6 shuffled research-design samples, pairwise-rank by quality using the D1–D5 standard. Emit the pairwise log; the harness computes the order and the ladder verdicts. Judge quality difference, never against academic standards.
Open skill - /optimization-loop
The optimizer brain for the ladder-foundry pretraining loop. Runs the two-level nested batch loop, delegates gating to gate_eval, attributes a failing batch to one weight (attribute-first), and recovers from disk after compaction. Control flow is fully scripted; only the
Open skill - /acu-nugget-recall
Tactic: Extract atomic units from one paper and score how much of a caller-supplied summary covers. Use for ACU-style binary or Nugget-style ternary recall checks; cannot run without a target summary.
Open skill