Baseline Establishment
SkillDev toolsSOTA Performance Baseline Campaign — 5 strategies for systematically
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Baseline Establishment skill
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/baseline-establishment/SKILL.md and read by ahel’s review.
Strategy Routing
| User Intent | Route To |
|---|---|
| Find all methods for a task | method-inventory |
| Extract scores from papers | performance-extraction |
| Normalize conditions across papers | condition-standardization |
| Check reproducibility / discrepancies | discrepancy-analysis |
| Track progress over time / headroom | progress-quantification |
Manifest
Strategies (5)
| Strategy | Purpose |
|---|---|
| method-inventory | Comprehensively identify all relevant methods for a task |
| performance-extraction | Systematically extract performance data and conditions from papers |
| condition-standardization | Standardize evaluation condition differences across papers |
| discrepancy-analysis | Identify discrepancies between reported and reproducible scores |
| progress-quantification | Track performance progress over time, quantify remaining headroom |
Tactics (3)
| Tactic | Purpose |
|---|---|
| leaderboard-harvesting | Systematically collect performance data from platforms and papers |
| condition-normalization | Compare and standardize experimental conditions across papers |
| progress-curve-construction | Build performance-over-time progress curves |
Subagent SOPs (10)
| SOP | Purpose |
|---|---|
| method-discovery | Identify methods via literature, leaderboards, citation chains |
| score-extraction | Extract (Task, Dataset, Metric, Score, Conditions) tuples |
| condition-cataloging | Record evaluation conditions per method |
| reproducibility-checklist-audit | Assess paper against ML Reproducibility Checklist |
| performance-table-assembly | Assemble unified comparison table |
| compute-normalization | Normalize results by compute budget |
| discrepancy-identification | Compare same-method scores across sources |
| headroom-estimation | Estimate ceiling vs current SOTA gap |
| progress-curve-fitting | Construct performance-over-time data |
| baseline-synthesis | Produce final structured baseline report |
Budget Table
| Strategy | Methods | Data Points | Web Searches |
|---|---|---|---|
| method-inventory | 50 | 0 | 60 |
| performance-extraction | 30 | 150 | 40 |
| condition-standardization | 20 | 60 | 30 |
| discrepancy-analysis | 15 | 45 | 30 |
| progress-quantification | 30 | 100 | 40 |
| TOTAL | 145 | 355 | 200 |
MCP Tools
| MCP Server | Tools |
|---|---|
| brave-search | brave_web_search, brave_llm_context |
| apify | rag-web-browser, google-scholar-scraper |
| alphaxiv | get_paper_content, answer_pdf_queries |
| semantic-scholar | ss_paper, ss_relevance_search, ss_citations, ss_references |
Context Management
Campaign outputs are accumulated in the calling knowledge-acquisition context:
methods_inventory.json— All discovered methods with metadataperformance_data.json— Extracted scores with provenanceconditions_matrix.json— Standardized conditions per methoddiscrepancy_report.json— Flagged score inconsistenciesprogress_curves.json— Time-series performance databaseline_report.md— Final synthesized baseline document
Available Strategies
Optional, no fixed order; the final leaf is always a sop.
| Strategy | When to use |
|---|---|
| condition-standardization | Standardize evaluation condition differences across papers — 20 methods, 60 data points, 30 web searches budget |
| discrepancy-analysis | Identify discrepancies between reported and reproducible scores — 15 methods, 45 data points, 30 web searches budget |
| method-inventory | Comprehensively identify all relevant methods for a task — 50 methods, 60 web searches budget |
| performance-extraction | Systematically extract performance data and conditions from papers — 30 methods, 150 data points, 40 web searches budget |
| progress-quantification | Track performance progress over time, quantify remaining headroom — 30 methods, 100 data points, 40 web searches budget |
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| context-checkpoint | Append research process and results to the current Phase's context file. Covers both process and results with genuine substance. Use this skill at plan-designated checkpoint points — typically after each strategy completes or at key decision nodes within a research Phase. |
| context-init | Create a new context file for a research Phase. Called once at Phase start to initialize the file that subsequent context-checkpoint calls will append to. Use this skill whenever a new research Phase begins and a fresh context file is needed. |
Signals
- GitHub stars
- 469
- Forks
- 37
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
baseline-establishment- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine