aht-init

name	aht-init
description	Collect and confirm the minimum required initialization info before starting any AHT run: project path, environment or conda env name, reference training launch script/method, and optimization target. Always send a user-facing confirmation request first, even when the values seem inferable from context, and wait for the user to confirm or update them before continuing.

AHT Init

Use this sub-skill as the front door of the AHT workflow.

Its job is to gather the minimum initialization information, present the concrete values back to the user, and obtain explicit confirmation before the main AHT workflow starts.

Required Fields

Collect and confirm these four user-provided items:

1. Project path: the target project root path
2. Environment: the runtime environment or conda environment name used to run training
3. Reference training launch method: the launch script or exact training invocation to tune
4. Optimization target: the metric/objective to optimize, ideally with direction (maximize/minimize)

Also identify one internal runtime field before handing off to the main AHT workflow:

5. Calling agent: detect whether the skill is currently being invoked by codex, claudecode, or openclaw

Core Rules

1. Extract candidate values from context when possible.

   • Reuse values already present in the current conversation, task context, pasted command, or provided script path.
   • If a value can be inferred directly from the supplied command or path, treat it as a candidate value.

2. Detect the calling agent before handoff.

   • Determine whether the current caller is codex, claudecode, or openclaw.
   • Prefer explicit runtime/context signals over guesswork.
   • If multiple signals conflict, choose the most direct one from the current runtime/tooling context and note low confidence internally.
   • If no reliable signal is available, default to openclaw.

3. Always ask for confirmation before execution.

   • Even if all four user fields appear available, do not proceed directly.
   • Always send one user-facing confirmation message that shows the current values and asks the user to confirm or update them.
   • Treat unconfirmed inferred values as provisional.
   • The detected calling_agent is internal metadata; do not ask the user to confirm it unless they specifically ask.

4. Block AHT until the user responds.

   • After sending the confirmation request, stop and wait.
   • Continue to the main auto-hparam-tuning workflow only after the user explicitly confirms the values or provides corrections.

5. Keep normalization minimal.

   • Preserve the user's wording when possible.
   • Normalize only enough to make downstream AHT steps unambiguous.

Output Contract

Case A: first-pass collection or unconfirmed information

Produce one concise English confirmation message to the user in this style:

Before I start AHT, please confirm or update the initialization info:
- project path: /path/to/project
- environment / conda env: my-env
- reference training launch method: python train.py ...
- optimization target: val/acc (maximize)

Reply with “confirm” if this is correct, or send the corrected fields.

Requirements:

• Always include all four fields.
• If a field is unknown, write TBD and ask the user to fill it in.
• Keep the message concise and action-oriented.
• Do not start project inspection, session creation, or any AHT execution in the same turn.

Case B: user has explicitly confirmed or updated the information

Produce a short readiness summary containing:

• project_path
• conda_env (or equivalent environment name)
• reference_command
• optimization_target
• goal (maximize or minimize, if derivable)
• calling_agent (codex, claudecode, or openclaw)

Then explicitly state that AHT can proceed to project/run understanding.

Extraction Hints

• If the user gives cd /path/to/proj && conda activate xxx && bash train.sh, you can extract provisional values such as:
  • project path: /path/to/proj
  • environment / conda env: xxx
  • reference training launch method/script: bash train.sh
• If the user says optimize val/acc, infer:
  • optimization target: val/acc
  • goal: maximize
• If the user says minimize val/loss, infer:
  • optimization target: val/loss
  • goal: minimize
• If the metric name is given without direction and direction is not obvious, keep the metric and leave the direction as part of the confirmation request.
• For calling_agent, use runtime/context/tooling signals such as:
  • codex if the current runtime or harness is clearly Codex
  • claudecode if the caller is clearly Claude Code
  • openclaw if invoked directly in OpenClaw or if no stronger signal is available

Handoff to Main AHT Skill

Only after the user confirms or corrects the initialization info should you continue with the main auto-hparam-tuning workflow:

1. Read the skill documentation of auto-hparam-tuning
2. Start from 1. UNDERSTAND PROJECT...

This sub-skill is intentionally small and reusable. It is only responsible for initial information extraction + explicit user confirmation gate.