Menu
The review-first prompt is selected by default. You can switch to a direct command or download a local copy.
Read SKILL.md and any companion files shown by SkillsMP before deciding whether to install.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
A direct command skips the review prompt. Inspect the source before running it.
npx skills add https://github.com/yogsoth-ai/de-anthropocentric-research-engine --skill progress-curve-fittingThe command stays on one line. Scroll horizontally to inspect it before copying.
Prefer a local copy? Download the files currently available to SkillsMP.
Based on SOC occupation classification
Showing SKILL.md
| name | progress-curve-fitting |
| description | Construct performance-over-time visualization data |
| execution | subagent |
| prompt | ./prompt.md |
| input | historical_scores (method, score, date) |
Fit parametric curves to historical SOTA performance data, identify inflection points representing paradigm shifts, and extrapolate future progress trajectories. Produces structured data suitable for visualization and trend analysis.
| Field | Type | Description |
|---|---|---|
| historical_scores | object[] | Array of {method, score, date, dataset, metric} sorted chronologically |
{
"dataset": "string",
"metric": "string",
"time_range": {"start": "string", "end": "string"},
"sota_frontier": [
{"date": "string", "method": "string", "score": 0.0, "is_new_sota": true}
],
"curve_fit": {
"best_model": "logarithmic|linear|sigmoid|exponential|piecewise",
"parameters": {},
"r_squared": 0.0,
"residual_std": 0.0
},
"inflection_points": [
{
"date": "string",
"method": "string",
"score_before": 0.0,
"score_after": 0.0,
"jump_magnitude": 0.0,
"paradigm_shift": "string"
}
],
"trend_metrics": {
"annual_improvement_rate": 0.0,
"improvement_accelerating": false,
"years_since_last_major_jump": 0.0,
"current_plateau_duration": null
},
"extrapolation": {
"predicted_1yr": 0.0,
"predicted_3yr": 0.0,
"confidence_band_1yr": [0.0, 0.0],
"confidence_band_3yr": [0.0, 0.0],
"caveat": "string"
}
}