run-campaign

name	run-campaign
description	Use whenever the user asks to run, start, kick off, or trigger an agentune HPO campaign in natural language (e.g. "run HPO with lgbm 40 trials/round", "tune xgboost on housing", "optimize california_housing"). Enforces parameter elicitation, init, and the autonomous round loop.

Running an Agentune Campaign

This skill fires whenever the user wants to run a hyperparameter optimization campaign. It overrides any tendency to guess defaults or skip the loop discipline. Follow it exactly.

Step 1 — Parse the request

Extract whatever the user did specify from their message. Map common phrasings:

User says	Maps to
lgbm, lightgbm	--backend lightgbm
xgb, xgboost	--backend xgboost
catboost	--backend catboost
N trials per round, N trials/round	--trials-per-round N
M rounds, max M rounds	--max-rounds M
housing, california housing	--dataset california_housing
aggressive, explore widely	--mode strong-exploration

Step 2 — Elicit missing required args (do NOT silently default)

agentune init requires: name, backend, metric, direction, dataset, trials-per-round, max-rounds, patience, mode.

For every missing arg, ask the user via AskUserQuestion. Batch all missing questions into a single tool call so the user answers them in one round-trip, not one-by-one.

Each question MUST include a one-line explanation of what the parameter controls. Use these templates:

• name — A short identifier for this campaign. Used in MCP calls and the report path (reports/<name>-report.html). Suggest one based on <backend>-<dataset> if obvious.
• backend — Which gradient boosting library to tune. Choices: xgboost, lightgbm, catboost.
• dataset — Which dataset to optimize on. Choices: breast_cancer, california_housing, digits, covertype, credit_g, phoneme, store_sales, rossmann.
• metric + direction — The validation score the agent optimizes and whether to minimize or maximize. Common pairings: rmse/min and mae/min for regression; auc/max, accuracy/max, logloss/min for classification. Suggest a default based on the dataset (regression vs classification) but still confirm.
• trials-per-round — How many model trainings the TPE sampler runs before handing back to the agent. Higher = better signal per round but slower. Typical: 30–60.
• max-rounds — Hard cap on how many agent decisions the campaign will make. Typical: 5–10.
• patience — How many consecutive rounds without improvement before the campaign stops itself. Typical: 3–5.
• mode — standard (conservative guardrails: 2-round cooldown on narrow↔widen reversals, max 3 param swaps per revise, revise only on plateau) vs strong-exploration (no cooldown, unlimited param swaps, revise allowed any round — use when you want aggressive exploration of the full parameter catalog).

If the user provided a value but it's ambiguous (e.g. "use auc" but the dataset is regression), ask to confirm rather than overriding silently.

Step 3 — Confirm and init

Before running agentune init, echo back the resolved config in one block so the user can spot mistakes:

Campaign: housing-demo
  backend: lightgbm
  dataset: california_housing
  metric: rmse (minimize)
  trials/round: 40
  max rounds: 6
  patience: 5
  mode: standard

Then run:

uv run agentune init <name> \
  --backend <backend> \
  --metric <metric> --direction <direction> \
  --dataset <dataset> \
  --trials-per-round <N> \
  --max-rounds <M> \
  --patience <P> \
  --mode <mode>

Step 4 — Run the autonomous loop

Follow CLAUDE.md's loop exactly. In summary:

Setup (once):

1. Call mcp__agentune__get_campaign_status for backend confirmation.
2. Call mcp__agentune__get_tuning_guide with the backend — read it before deciding anything.
3. Tell the user once: "Running campaign. Progress visible at reports/<name>-report.html — refresh anytime."

Loop (until terminal):

1. mcp__agentune__run_next_round
2. If status: COMPLETED → print final result + report path. Done.
3. If status: FAILED → print what failed. Done.
4. If status: AWAITING_AGENT → continue.
5. mcp__agentune__get_round_summary
6. Diagnose in this order (cite specific numbers in the proposal justification):
   • generalization gap (train vs val)
   • param importance (any param > 30%? all < 15%?)
   • plateau signal
   • boundary hits
7. mcp__agentune__submit_action_proposal with the chosen action.
8. Goto 1.

Step 5 — Output discipline

• Per round, print one line only: Round N/max: metric=score (delta).
• Do NOT narrate reasoning between rounds — the HTML report and the decision log capture it.
• Elaborate only at: final result, failure, or rejected proposal.
• If a proposal is rejected, read the rejection reason, adapt, and resubmit — don't re-explain to the user.

Red flags — STOP and re-read this skill

Thought	Reality
"I'll just default --patience to 5"	No. Ask.
"The user said lgbm, obvious dataset is housing"	No. Ask.
"I'll narrate what the agent decided this round"	No. One status line.
"I'll skip get_tuning_guide, I remember xgboost"	No. Always read it at the start of a campaign.
"I'll batch ask the user round-by-round"	No. Loop is autonomous; only stop on terminal state.