establish-empirical-baseline
SkillDev toolsLets your agent build a fair performance baseline by collecting, comparing, and normalizing results across methods.
Available today. Use it from your connected AI after setup.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the establish-empirical-baseline skill
About this skill
Establish a fair empirical baseline by inventorying methods, extracting comparable performance, normalizing conditions/compute, checking discrepancies, and estimating progress/headroom.
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/establish-empirical-baseline/SKILL.md and read by ahel’s review.
Purpose
Establish a fair empirical baseline by inventorying methods, extracting comparable performance, normalizing conditions/compute, checking discrepancies, and estimating progress/headroom.
Input contract
required: [method_records, benchmark_or_task, performance_measure]
optional: [historical_series, compute_metadata, condition_schema]
constraints: [comparability fields and source provenance required]
Execution protocol
Do not perform called SOP operations inline; each loaded SOP owns its contract and thresholds.
- You MUST load skill
inventory-reference-itemsto inventory methods and comparison records. - You MUST load skill
extract-evidence-recordto extract performance evidence. You MUST load skillaudit-reporting-qualityto audit missing or ambiguous reporting. You MUST load skillnormalize-comparison-scaleto normalize units, data, compute, and evaluation protocol. - You MUST load skill
detect-performance-discrepancyto detect comparison discrepancies. You MUST load skillestimate-performance-headroomto estimate headroom. You MUST load skillanalyze-temporal-trajectoryto analyze progress and leaderboard dynamics. You MUST load skillcheck-dominanceto identify dominated and incomparable records. - Synthesize the baseline with uncertainty and known incomparable records.
If the resulting methods or baselines require explicit comparative selection, consider
rank-candidatesas the next tactic.
Output contract
produces: [method_inventory, normalized_baseline, discrepancy_report, progress_curve, headroom_estimate]
delta_fields: [findings, evidence_updates, uncertainties, decisions, open_questions]
Thresholds and quality gates
- Baseline acquisition gates are relative to a declared eligible universe: record numerator, denominator, batch increment, stopping reason, and source references for each ratio.
method-inventory: method coverage ratio reaches a justified floor over the eligible method universe.performance-extraction: comparable-record coverage ratio reaches a justified floor over eligible records.condition-standardization: complete condition-vector ratio reaches a justified floor over comparable records.discrepancy-analysis: score-pair coverage ratio reaches a justified floor over eligible comparison pairs.progress-quantification: historical-time coverage and independent-source ratio reach justified floors; stop when added periods no longer change the trajectory conclusion.- Across modes, report marginal information gain and saturation state when added records or periods no longer change the baseline conclusion.
- Normalization must expose condition, compute, metric, and unit transformations.
Failure and counterexamples
Do not call a baseline fair when conditions are missing, metrics are incomparable, or leaderboard values are copied without protocol verification. Mark headroom unknown when the historical series is below its floor.
Provenance map
9 architecture old entries: baseline, inventory, extraction, standardization, discrepancy, progress, leaderboard, normalization, curve construction. Repeated reporting prose compressed.
Legacy context checkpoint / Delta notes
Append method IDs, normalized records, excluded records with reasons, discrepancy pairs, progress model, and headroom uncertainty.
Preserved source criteria ledger
| source | source line | kind | source criterion |
|---|---|---|---|
| baseline-establishment | 28 | textual | \ |
| baseline-establishment | 40 | textual | \ |
| baseline-establishment | 58 | textual | \ |
| baseline-establishment | 70 | numeric-table | \ |
| baseline-establishment | 71 | numeric-table | \ |
| baseline-establishment | 72 | numeric-table | \ |
| baseline-establishment | 73 | numeric-table | \ |
| baseline-establishment | 74 | numeric-table | \ |
| baseline-establishment | 75 | numeric-table | \ |
| method-inventory | 23 | numeric-table | \ |
| method-inventory | 24 | numeric-table | \ |
| method-inventory | 25 | numeric-table | \ |
| method-inventory | 30 | textual | |
| method-inventory | 33 | numeric-table | \ |
| method-inventory | 34 | numeric-table | \ |
| method-inventory | 35 | numeric-table | \ |
| method-inventory | 36 | numeric-table | \ |
| method-inventory | 37 | numeric-table | \ |
| method-inventory | 38 | textual | |
| method-inventory | 41 | numeric | Cannot exit until methods_discovered >= 40 (80% of target). |
| performance-extraction | 18 | textual | Extract structured performance data from papers, leaderboards, and reproducibility studies. Each data point is a (Task, Dataset, Metric, Score, Conditions) tuple with full provenance. Prioritizes primary sources (original papers) but cross-references against leaderboards and third-party reproductions. |
| performance-extraction | 24 | numeric-table | \ |
| performance-extraction | 25 | numeric-table | \ |
| performance-extraction | 26 | numeric-table | \ |
| performance-extraction | 27 | numeric-table | \ |
| performance-extraction | 32 | textual | |
| performance-extraction | 35 | numeric-table | \ |
| performance-extraction | 36 | numeric-table | \ |
| performance-extraction | 37 | numeric-table | \ |
| performance-extraction | 38 | numeric-table | \ |
| performance-extraction | 39 | numeric-table | \ |
| performance-extraction | 40 | numeric-table | \ |
| performance-extraction | 41 | textual | |
| performance-extraction | 44 | numeric | Cannot exit until data_points >= 120 (80% of target). |
| condition-standardization | 25 | numeric-table | \ |
| condition-standardization | 26 | numeric-table | \ |
| condition-standardization | 27 | numeric-table | \ |
| condition-standardization | 28 | numeric-table | \ |
| condition-standardization | 33 | textual | |
| condition-standardization | 36 | numeric-table | \ |
| condition-standardization | 37 | numeric-table | \ |
| condition-standardization | 38 | numeric-table | \ |
| condition-standardization | 39 | numeric-table | \ |
| condition-standardization | 40 | numeric-table | \ |
| condition-standardization | 41 | textual | |
| condition-standardization | 44 | numeric | Cannot exit until data_points_standardized >= 48 (80% of target). |
| condition-standardization | 60 | textual | 3. Group methods by comparable condition sets |
| condition-standardization | 63 | textual | 6. Produce fair comparison subsets where conditions are controlled |
| discrepancy-analysis | 24 | numeric-table | \ |
| discrepancy-analysis | 25 | numeric-table | \ |
| discrepancy-analysis | 26 | numeric-table | \ |
| discrepancy-analysis | 27 | numeric-table | \ |
| discrepancy-analysis | 32 | textual | |
| discrepancy-analysis | 35 | numeric-table | \ |
| discrepancy-analysis | 36 | numeric-table | \ |
| discrepancy-analysis | 37 | numeric-table | \ |
| discrepancy-analysis | 38 | numeric-table | \ |
| discrepancy-analysis | 39 | numeric-table | \ |
| discrepancy-analysis | 40 | textual | |
| discrepancy-analysis | 43 | numeric | Cannot exit until score_pairs_compared >= 36 (80% of target). |
| discrepancy-analysis | 52 | textual | - reproducibility-checklist-audit - Assess paper reproducibility completeness |
| discrepancy-analysis | 59 | textual | 4. Apply reproducibility-checklist-audit to papers with large discrepancies |
| discrepancy-analysis | 85 | textual | "reproducibility_checklist_score": 0, |
| progress-quantification | 26 | numeric-table | \ |
| progress-quantification | 27 | numeric-table | \ |
| progress-quantification | 28 | numeric-table | \ |
| progress-quantification | 29 | numeric-table | \ |
| progress-quantification | 34 | textual | |
| progress-quantification | 37 | numeric-table | \ |
| progress-quantification | 38 | numeric-table | \ |
| progress-quantification | 39 | numeric-table | \ |
| progress-quantification | 40 | numeric-table | \ |
| progress-quantification | 41 | numeric-table | \ |
| progress-quantification | 42 | numeric-table | \ |
| progress-quantification | 43 | textual | |
| progress-quantification | 46 | numeric | Cannot exit until historical_data_points >= 80 (80% of target). |
| leaderboard-harvesting | 43 | numeric | - Flag discrepancies > 1 standard deviation |
| leaderboard-harvesting | 58 | textual | ## Minimum Yield |
| leaderboard-harvesting | 62 | numeric-table | \ |
| leaderboard-harvesting | 63 | numeric-table | \ |
| leaderboard-harvesting | 64 | numeric-table | \ |
| leaderboard-harvesting | 65 | numeric-table | \ |
| condition-normalization | 28 | textual | - Random seeds: number of runs, seed selection, variance reported |
| condition-normalization | 51 | textual | ### Stage 4: Fair Comparison Baseline |
| condition-normalization | 53 | textual | Apply normalization to produce fair comparison subsets: |
| condition-normalization | 58 | textual | Yield: Fair comparison tables with methodology notes. |
| condition-normalization | 60 | textual | ## Minimum Yield |
| condition-normalization | 64 | numeric-table | \ |
| condition-normalization | 65 | numeric-table | \ |
| condition-normalization | 66 | numeric-table | \ |
| condition-normalization | 67 | numeric-table | \ |
| progress-curve-construction | 62 | textual | ## Minimum Yield |
| progress-curve-construction | 66 | numeric-table | \ |
| progress-curve-construction | 67 | numeric-table | \ |
| progress-curve-construction | 68 | numeric-table | \ |
| progress-curve-construction | 69 | numeric-table | \ |
| progress-curve-construction | 73 | numeric | - progress-curve-fitting (for Stages 2-3) |
Context checkpoint / Delta notes
Append method IDs, normalized records, excluded records with reasons, discrepancy pairs, progress model, and headroom uncertainty.
Signals
- GitHub stars
- 501
- Forks
- 41
- Last commit
- Sep 2026
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establish-empirical-baseline- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine