performance-extraction

Research

Systematically extract performance data and conditions from papers — 30 methods, 150 data points, 40 web searches budget

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Source SKILL.md: https://github.com/yogsoth-ai/de-anthropocentric-research-engine/blob/HEAD/skills/performance-extraction/SKILL.md

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Performance Extraction

Purpose

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.

Budget

Resource	Floor	Target
Methods covered	20	30
Data points extracted	100	150
Web searches	25	40
Papers read	15	30

State Ledger

<HARD-GATE>
| Metric | Current | Target | Status |
|--------|---------|--------|--------|
| Methods covered | 0 | 30 | BLOCKED |
| Data points extracted | 0 | 150 | BLOCKED |
| Web searches used | 0 | 40 | — |
| Papers read | 0 | 30 | — |
| Datasets covered | 0 | 5 | — |
| Metrics tracked | 0 | 3 | — |
</HARD-GATE>

Cannot exit until data_points >= 120 (80% of target).

Available Tactics

leaderboard-harvesting — Bulk data collection from structured platforms

Available SOPs

score-extraction — Extract tuples from individual papers
condition-cataloging — Record conditions alongside scores

Execution Guidance

For each method in the inventory, locate the original paper
Use score-extraction SOP on each paper to pull all reported results
Cross-reference against Papers With Code / benchmark leaderboards
Use condition-cataloging to record experimental setup for each score
Flag scores that lack essential condition information
Track provenance: which table/figure in which paper
Prefer results from official implementations over third-party

Output Format

{
  "data_points": [
    {
      "method": "string",
      "task": "string",
      "dataset": "string",
      "split": "test|val|dev",
      "metric": "string",
      "score": 0.0,
      "confidence_interval": [0.0, 0.0],
      "conditions": {
        "hardware": "string",
        "training_data_size": "string",
        "hyperparams_reported": true,
        "seeds_reported": true,
        "compute_budget": "string"
      },
      "provenance": {
        "paper_id": "string",
        "table_or_figure": "string",
        "is_primary_source": true
      }
    }
  ],
  "coverage_summary": {
    "methods_covered": 0,
    "datasets_covered": 0,
    "metrics_tracked": [],
    "missing_data_flags": []
  }
}

Available Tactics

Optional, no fixed order; the final leaf is always a sop.

Tactic	When to use
leaderboard-harvesting	Systematically collect performance data from platforms and papers

Available SOPs

Optional, no fixed order; the final leaf is always a sop.

SOP	When to use
condition-cataloging	Record evaluation conditions (data splits, hyperparams, hardware, seeds) from a paper
score-extraction	Extract (Task, Dataset, Metric, Score, Conditions) tuples from a paper