Generate Code

When this skill is invoked, act as a thin coordinator over the generate-code subagent — do not do the investigation or editing yourself.

Before doing anything else, read .claude/skills/rules/generate-code.md (target-handling and relaying policy), .claude/rules/scope-and-safety.md (filesystem write scope and safety boundaries every skill/agent follows), and .claude/rules/project-mission.md (what fat_llama actually is and how it works) — all three may be updated over time without this file changing.

Also read README.md at the repo root in full, right now, before Step 1 — it's fat_llama's own description of its purpose and method (iterative soft thresholding of FFT data to upscale compressed audio across supported formats, tested and built primarily against the MP3→FLAC outcome, CPU/FFTW-only, deliberately without AI/ML-based upscaling). This skill's job is to make sure the subagent it dispatches never drifts toward an out-of-scope "fix" (e.g. reaching for a trained/learned model or a CUDA/GPU-accelerated path), so carry this context into the subagent's prompt in Step 2.

args names the target: what to look at and what to fix or change. If args is empty, ask the user what target to work on before proceeding.

Logging

Before Step 1, open this run's log file per .claude/rules/logging.md (name: generate-code-<time>-<user>.log). Append one entry per step below, including the subagent dispatch in Step 2 and its own log filename (from the subagent's report).

Steps

1. Check whether docs/CURRENT_STATE.md exists.

   • If it's missing entirely, invoke the review-current-state skill first (via the Skill tool) to generate it — generate-code depends on that factblock as its starting map.
   • If it already exists, don't regenerate it automatically; the subagent itself will flag if it looks stale for the files it needs and read source directly in that case.

2. Launch the generate-code subagent via the Agent tool with subagent_type: "generate-code" and run_in_background: false (the caller needs the result in this turn). Give it a self-contained prompt containing the exact target text from args, plus the condensed mission context from .claude/rules/project-mission.md (mp3→flac is the primary tested outcome among the formats fat_llama supports, via IST on FFT data, CPU/FFTW-only, no AI/ML-based upscaling) — it also reads its own rules file, the mission-context file, and the factblock, so no further context is required beyond that.

3. The subagent's final message is JSON per the contract in .claude/agents/rules/scientific-coding.md. Relay it per the relaying policy in .claude/skills/rules/generate-code.md.

Note for coordinator agents

A coordinator does not need this skill at all — it can call the generate-code subagent directly via the Agent tool with subagent_type: "generate-code", passing the target in the prompt. This skill exists as the interactive /generate-code <target> entry point; the subagent is the reusable unit.