用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/yogsoth-ai/de-anthropocentric-research-engine --skill progress-curve-fitting命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
正在显示 SKILL.md
Strategy: Dialectic engine retuned for truth-seeking, not survival. A defender steelmans a claim into its MOST falsifiable form, a critic attacks to refute it, a judge classifies the exchange into BROKEN/CORROBORATED/UNFALSIFIABLE — the judge does NOT pick a winner or score persuasiveness. Methods: Irving debate (repurposed), Toulmin argumentation, Mayo severe testing.
Campaign: Logical extreme and boundary testing via reductio ad absurdum and edge-case analysis. Core question: Does this artifact collapse under logical limits and boundary conditions? Methods: Lakatos 1976, Dutilh Novaes 2016, BVA, Flyvbjerg Critical Case, Popper.
Campaign for mapping argument structures — extract claims, link evidence, assess strength, synthesize positions. Produces argument graphs in the wiki vault.
基于 SOC 职业分类
| 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"
}
}