pmtmax-autoresearch

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更新时间2026年7月2日 14:42

Use when running the LGBM autoresearch loop, editing candidate YAML specs, gating new variants, or promoting winners in polymarket-tmax-lab.

安装

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。

来源

YoungseokOh

YoungseokOh/polymarket-tmax-lab

打开 GitHub 仓库查看创作者相关仓库

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pmtmax-autoresearch

Use this skill for the agent-driven autoresearch loop around lgbm_emos.

First reads

Read AGENTS.md.
Read:
- docs/agent-skills/autoresearch.md
- docs/agent-skills/research-loop.md
- docs/codebase/modeling.md

Critical rules

current public champion variant is high_neighbor_oof; new champion-adjacent runs should pass --baseline-variant high_neighbor_oof.
existing run manifests are authoritative; do not silently rewrite older recency_neighbor_oof runs.
create or edit one candidate YAML at a time under artifacts/workspaces/historical_real/autoresearch/<run_tag>/candidates/.
run autoresearch against real-only historical_real data; synthetic augmentation is not allowed.
use autoresearch-step for quick keep/discard/crash, then autoresearch-gate, then autoresearch-analyze-paper.
for recurring checker-based operation, prefer scripts/pmtmax-workspace historical_real uv run python scripts/run_model_research_agent.py which can retrain the baseline when signatures change, create the next small candidate YAML automatically, and advance one candidate through the loop while updating checker/model_research_*.
canonical historical_training_set* / historical_backtest_panel stay immutable unless explicitly promoted elsewhere.
public champion publish is never implicit; autoresearch-promote only promotes YAML specs and publish-champion is the separate public-alias command.
promotion is fail-closed: require CLI gate leaderboard JSON/CSV, matching dataset/panel signatures, candidate calibrator, and paper overall_gate_decision=GO; INCONCLUSIVE blocks promotion.
report CRPS, DirAcc, and ECE together; do not promote from CRPS alone.
quick-eval CRPS is Celsius-normalized in code and also reports raw market-unit CRPS for audit.

CRPS unit normalization

The training dataset (data/workspaces/<workspace>/parquet/gold/v2/historical_training_set.parquet or the active PMTMAX_PARQUET_DIR) contains mixed temperature units:

Celsius (C): Seoul, Tokyo, London, etc.
Fahrenheit (F): NYC, Chicago, Atlanta, Dallas, Miami, Seattle, etc.

Raw CRPS on mixed units is not comparable. evaluate_saved_model and scripts/quick_eval.py now report crps_celsius_normalized and crps_market_unit; use the normalized value for model comparisons and keep the raw value for audit.

Evaluation must include CRPS + DirAcc + ECE — all three

Never report CRPS alone. Always include:

CRPS (°C-normalized): unit-normalized predictive accuracy
DirAcc: directional accuracy — fraction of markets where top-predicted bin = actual winner. Target: >73%
ECE: Expected Calibration Error — lower is better, well-calibrated model ~0.007

Use evaluate_saved_model(model_path, holdout) and report all three side-by-side for every candidate comparison.

Focus

run scaffold: artifacts/workspaces/historical_real/autoresearch/<run_tag>/
promoted spec registry: configs/autoresearch/lgbm_emos/promoted/
candidate artifacts: artifacts/workspaces/historical_real/autoresearch/<run_tag>/models/
shared program: artifacts/workspaces/historical_real/autoresearch/<run_tag>/program.md

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pmtmax-research-loop

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Use when working on bootstrap-lab, warehouse management, seed export or restore, legacy raw/parquet cleanup, or canonical data layout in polymarket-tmax-lab.

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Use when working on dataset materialization, model training, backtesting, scan-edge signals, paper-trading workflows, or experiment artifacts in polymarket-tmax-lab.

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pmtmax-autoresearch

YoungseokOh/polymarket-tmax-lab

Use when running the LGBM autoresearch loop, editing candidate YAML specs, gating new variants, or promoting winners in polymarket-tmax-lab.

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pmtmax-autoresearch

Use this skill for the agent-driven autoresearch loop around lgbm_emos.

First reads

Read AGENTS.md.

Read:

docs/agent-skills/autoresearch.md
docs/agent-skills/research-loop.md
docs/codebase/modeling.md

Critical rules

current public champion variant is high_neighbor_oof; new champion-adjacent runs should pass --baseline-variant high_neighbor_oof.

existing run manifests are authoritative; do not silently rewrite older recency_neighbor_oof runs.

create or edit one candidate YAML at a time under artifacts/workspaces/historical_real/autoresearch/<run_tag>/candidates/.

run autoresearch against real-only historical_real data; synthetic augmentation is not allowed.

use autoresearch-step for quick keep/discard/crash, then autoresearch-gate, then autoresearch-analyze-paper.

for recurring checker-based operation, prefer scripts/pmtmax-workspace historical_real uv run python scripts/run_model_research_agent.py which can retrain the baseline when signatures change, create the next small candidate YAML automatically, and advance one candidate through the loop while updating checker/model_research_*.

canonical historical_training_set* / historical_backtest_panel stay immutable unless explicitly promoted elsewhere.

public champion publish is never implicit; autoresearch-promote only promotes YAML specs and publish-champion is the separate public-alias command.

promotion is fail-closed: require CLI gate leaderboard JSON/CSV, matching dataset/panel signatures, candidate calibrator, and paper overall_gate_decision=GO; INCONCLUSIVE blocks promotion.

report CRPS, DirAcc, and ECE together; do not promote from CRPS alone.

quick-eval CRPS is Celsius-normalized in code and also reports raw market-unit CRPS for audit.

CRPS unit normalization

The training dataset (data/workspaces/<workspace>/parquet/gold/v2/historical_training_set.parquet or the active PMTMAX_PARQUET_DIR) contains mixed temperature units:

Celsius (C): Seoul, Tokyo, London, etc.

Fahrenheit (F): NYC, Chicago, Atlanta, Dallas, Miami, Seattle, etc.

Evaluation must include CRPS + DirAcc + ECE — all three

Never report CRPS alone. Always include:

CRPS (°C-normalized): unit-normalized predictive accuracy

DirAcc: directional accuracy — fraction of markets where top-predicted bin = actual winner. Target: >73%

ECE: Expected Calibration Error — lower is better, well-calibrated model ~0.007

Use evaluate_saved_model(model_path, holdout) and report all three side-by-side for every candidate comparison.

Focus

run scaffold: artifacts/workspaces/historical_real/autoresearch/<run_tag>/

promoted spec registry: configs/autoresearch/lgbm_emos/promoted/

candidate artifacts: artifacts/workspaces/historical_real/autoresearch/<run_tag>/models/

shared program: artifacts/workspaces/historical_real/autoresearch/<run_tag>/program.md

name	pmtmax-autoresearch
description	Use when running the LGBM autoresearch loop, editing candidate YAML specs, gating new variants, or promoting winners in polymarket-tmax-lab.