Skills elpa

Name: elpa
Author: openclaw

Orchestrate real ELPA-style ensemble forecasting workflows by triggering external sub-model training jobs (for example PyTorch/Prophet/TiDE/transformers), then computing ELPA online/offline weights from validation errors. Use when you need production-oriented ensemble training instead of lightweight simulation adapters.

install

source · Clone the upstream repo

git clone https://github.com/openclaw/skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/anonymouscodemaker/elpa" ~/.claude/skills/openclaw-skills-elpa && rm -rf "$T"

OpenClaw · Install into ~/.openclaw/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/anonymouscodemaker/elpa" ~/.openclaw/skills/openclaw-skills-elpa && rm -rf "$T"

manifest: skills/anonymouscodemaker/elpa/SKILL.md

ELPA

Overview

This skill does not train toy adapters. It triggers real sub-model training commands from your own training codebases and then builds ELPA routing/weights from real validation errors.

Default model pool is intentionally larger than 4 and can be expanded freely.

Workflow

Prepare a training config JSON (see
```
assets/elpa_train_template.json
```
).
Dry-run the command plan to verify all sub-model commands.
Execute real sub-model training when resources are available.
Prepare validation error inputs per model.
Build ELPA ensemble policy JSON from those errors.

1) Prepare Config

Create a config based on

assets/elpa_train_template.json

Put your real training entrypoints in each model
```
train_cmd
```
.
Keep each model tagged as
```
online
```
or
```
offline
```
.
Add as many models as needed; ELPA is not limited to 4.

2) Dry-Run Plan (No Training)

python3 scripts/elpa_orchestrator.py \
  --config assets/elpa_train_template.json \
  --run-dir .runtime/elpa_run \
  --manifest-out .runtime/elpa_run/train_manifest.json

This prints and records the commands that would run, without training.

3) Execute Real Training

python3 scripts/elpa_orchestrator.py \
  --config /path/to/your_train_config.json \
  --run-dir .runtime/elpa_run \
  --manifest-out .runtime/elpa_run/train_manifest.json \
  --execute

Use this only in an environment that has the required ML dependencies and hardware.

4) Build ELPA Integration Policy

After each sub-model produces validation errors, run:

python3 scripts/elpa_integrator.py \
  --config /path/to/your_integrate_config.json \
  --output .runtime/elpa_run/elpa_policy.json

The output includes:

```
scores
```
for each model from validation errors
```
online_weights
```
and
```
offline_weights
```
```
best_online_model
```
and
```
best_offline_model
```

ELPA control fields (

beta

dirty_interval

amplitude_window

mutant_epsilon

)

Model Scaling

To support more models, append model blocks in your config with:

unique
```
name
```
```
group
```
as
```
online
```
or
```
offline
```
real
```
train_cmd
```

No script changes are needed for adding models.

Files

```
scripts/elpa_orchestrator.py
```
: real sub-model training command planner/executor
```
scripts/elpa_integrator.py
```
: ELPA score/weight builder from validation errors
```
assets/elpa_train_template.json
```
: >4-model real training template
```
assets/elpa_integrate_template.json
```
: ELPA integration template
```
references/config-schema.md
```
: config field reference and placeholders