progress-quantification
$
npx mdskill add yogsoth-ai/de-anthropocentric-research-engine/progress-quantificationQuantify performance progress and remaining headroom using historical data and progress curves
- Solves the problem of tracking task performance over time and identifying breakthrough needs
- Uses web searches, historical leaderboards, and curve-fitting algorithms
- Prioritizes data collection and curve construction to meet progress tracking targets
- Delivers progress curves, inflection points, and headroom estimates for actionable insights
SKILL.md
.github/skills/progress-quantificationView on GitHub ↗
---
name: progress-quantification
description: Track performance progress over time, quantify remaining headroom — 30 methods, 100 data points, 40 web searches budget
used-by: baseline-establishment
---
# Progress Quantification
## Purpose
Construct temporal performance trajectories for the target task. Fit progress curves, identify inflection points (paradigm shifts), estimate theoretical/practical ceilings, and quantify remaining headroom. Produces actionable intelligence about where the field is plateauing and where breakthroughs are needed.
## Budget
| Resource | Floor | Target |
|----------|-------|--------|
| Methods tracked | 20 | 30 |
| Historical data points | 70 | 100 |
| Web searches | 25 | 40 |
| Time span covered (years) | 3 | 5+ |
## State Ledger
```
<HARD-GATE>
| Metric | Current | Target | Status |
|--------|---------|--------|--------|
| Methods tracked | 0 | 30 | BLOCKED |
| Historical data points | 0 | 100 | BLOCKED |
| Web searches used | 0 | 40 | — |
| Progress curves built | 0 | 3 | — |
| Headroom estimates | 0 | 3 | — |
| Inflection points identified | 0 | 2 | — |
</HARD-GATE>
```
Cannot exit until historical_data_points >= 80 (80% of target).
## Available Tactics
- **progress-curve-construction** — Build temporal performance curves with inflection detection
- **leaderboard-harvesting** — Historical data collection
## Available SOPs
- **progress-curve-fitting** — Fit curves and extract trend parameters
- **headroom-estimation** — Estimate ceiling vs current SOTA gap
- **baseline-synthesis** — Produce final integrated report
## Execution Guidance
1. Arrange all extracted scores chronologically per dataset/metric
2. Use progress-curve-construction tactic for curve building
3. Identify paradigm shifts (method families that caused jumps)
4. Use headroom-estimation for ceiling analysis
5. Compare progress rates across different sub-tasks
6. Identify tasks approaching saturation vs. those with large headroom
7. Produce baseline-synthesis as the final integrated deliverable
## Output Format
```json
{
"progress_curves": [
{
"dataset": "string",
"metric": "string",
"time_series": [{"date": "string", "method": "string", "score": 0.0}],
"trend_type": "logarithmic|linear|sigmoid|stepped",
"annual_improvement_rate": 0.0,
"inflection_points": [{"date": "string", "method": "string", "cause": "string"}]
}
],
"headroom_analysis": [
{
"dataset": "string",
"metric": "string",
"current_sota": 0.0,
"human_performance": 0.0,
"theoretical_ceiling": 0.0,
"remaining_headroom_pct": 0.0,
"saturation_status": "saturating|active_progress|early_stage"
}
],
"paradigm_shifts": [
{
"year": 2024,
"method_family": "string",
"improvement_magnitude": 0.0,
"description": "string"
}
]
}
```
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