PI SkillOpt Operator

Use this skill when the user wants the agent to operate the SkillOpt repository: run experiments, evaluate skills, configure local models, inspect outputs, or extend the repo with new benchmarks/backends.

Core repo facts

• SkillOpt optimizes a skill document / system prompt, not model weights.
• The repo supports a generic local OpenAI-compatible backend named openai_compat.
• The local example model config is configs/dotnetdebug/local_mitko.yaml.
• That config uses model mitko at http://localhost:8000/v1 for both optimizer and target.
• The runnable sample benchmark is dotnetdebug.
• The sample dataset is data/dotnetdebug/tasks.json.
• The seed skill is skillopt/envs/dotnetdebug/skills/initial.md.
• Training entry point: scripts/train.py.
• Eval-only entry point: scripts/eval_only.py.

Default workflow

1. Identify the user goal:

• run a SkillOpt training job
• evaluate an existing skill
• inspect outputs from a previous run
• add or modify a benchmark
• add or modify a backend

1. Confirm the benchmark, backend, target model, and desired output directory.
2. Prefer a small smoke test first before a larger run.
3. Use existing configs when possible instead of inventing new ones.
4. After a run, report:

• exact command used
• output directory
• best skill path
• headline metrics
• next recommended action

Local model workflow

When the user asks to use a local model or mentions mitko, localhost:8000, or an OpenAI-compatible endpoint:

• Prefer model.backend: openai_compat.
• Set both optimizer_backend and target_backend to openai_compat unless the user explicitly wants a mixed setup.
• Use configs/dotnetdebug/local_mitko.yaml when the task is the built-in dotnet debugging example.
• Keep smoke tests small, for example:
• train.num_epochs=1
• train.batch_size=2
• gradient.analyst_workers=1
• gradient.minibatch_size=2
• env.workers=1
• env.limit=2

Common commands

Activate the environment first if .venv exists:

bash
source .venv/bin/activate

Small local training run:

bash
python3 scripts/train.py \
  --config configs/dotnetdebug/local_mitko.yaml \
  --cfg-options \
    train.num_epochs=1 \
    train.batch_size=2 \
    gradient.analyst_workers=1 \
    gradient.minibatch_size=2 \
    env.workers=1 \
    env.limit=2 \
    optimizer.learning_rate=2 \
    env.out_root=outputs/dotnetdebug_smoke

Eval-only run:

bash
python3 scripts/eval_only.py \
  --config configs/dotnetdebug/local_mitko.yaml \
  --skill outputs/dotnetdebug_smoke/best_skill.md \
  --split test \
  --cfg-options env.limit=2 env.workers=1 env.out_root=outputs/dotnetdebug_eval_smoke

Extension workflow

When adding a new benchmark:

• Create files under skillopt/envs/<benchmark>/.
• Add config under configs/<benchmark>/default.yaml.
• Register the adapter in both:
• scripts/train.py
• scripts/eval_only.py
• Add or update docs when the new benchmark is user-facing.

When adding a new backend:

• Add the backend module under skillopt/model/.
• Update backend normalization and defaults in skillopt/model/common.py.
• Update allowed runtime backends in skillopt/model/backend_config.py.
• Update dispatch in skillopt/model/__init__.py.
• Expose config in skillopt/config.py, configs/_base_/default.yaml, and CLI entry points.

Guardrails

• Do not describe SkillOpt as model fine-tuning.
• Do not start with a long expensive run if a smoke test can validate the setup first.
• Prefer repo-native paths and configs over ad hoc scripts.
• If editing benchmark/backend code, validate syntax/errors after changes.
• If a run fails, report the first concrete failure point and propose the smallest next fix.

Response checklist

Before finishing, make sure the response includes:

• the chosen benchmark/config/backend
• the exact command(s) to run
• where outputs will be written
• what artifact to inspect next (best_skill.md, eval summary, predictions, patches, etc.)