| name | ops-ar |
| description | A&R any record like a dance-pop label owner + master producer. Single track, batch, or full Gmail-inbox demo sweep — runs the audio-ar analysis stack (BPM/key/loudness/structure, CLAP mood/genre/hit-lean, Whisper lyrics, Cyanite/Music.ai pro layer) and delivers verdict cards. Can email the full verdict with listen links on request. |
| argument-hint | <audio file | URL/Dropbox | "latest" | <file1> <file2> ... | inbox [from <sender>...]> |
| allowed-tools | ["Bash","Read","Write","Grep","Glob","Agent","TeamCreate","SendMessage","AskUserQuestion","WebSearch"] |
| effort | high |
/ops:ops-ar — A&R Command
A&R the given record(s) like a pop/dance-hit label owner + master producer. The deliverable is always the full A&R card per track:
VERDICT (hit/10 + sign / develop / pass) → WHAT'S WORKING → WHAT'S HOLDING IT BACK → THE PLAN (producer moves) → REFERENCE & POSITIONING → NEXT.
Configuration (templatable — no hardcoded personal data)
| Setting | Source | Default |
|---|
| Audio analysis stack home | $AUDIO_AR_HOME env or ar.stack_home in $PREFS_PATH | ~/audio-ai |
| Python venv | $AUDIO_AR_HOME/venv/bin/python | — |
| Music.ai workflow slug | $MUSICAI_WORKFLOW env or Doppler | (required for Music.ai) |
| Cyanite / Music.ai / Soundcharts keys | env / Doppler / ~/.mcp-secrets.env | — |
| A&R taste profile (label lane, reference acts, tempo sweet spot) | ar.profile in $PREFS_PATH | dance-pop / feel-good house |
The skill must read these at runtime — never hardcode user names, mailboxes, label names, or absolute /Users/... paths.
Modes
1. Single track — /ops:ops-ar <file|url|latest>
Spawn the ar-producer agent (Opus) with the track:
- Local file → pass the path directly.
- URL/Dropbox → agent downloads first (Dropbox: append
&dl=1; YouTube: yt-dlp -x --audio-format mp3).
latest / empty → newest audio file (.mp3, .wav, .m4a, .aiff, .flac, .ogg, .aac) in ~/.claude/jobs/*/tmp/ by mtime — ignore JSON, PNG, and other non-audio artifacts.- Subagent MCP rule: the spawn prompt MUST name the audio-ar tools and include the literal instruction
ToolSearch select:mcp__audio-ar__full_ar_report,mcp__audio-ar__analyze_track,mcp__audio-ar__mood_score,mcp__audio-ar__transcribe_vocals,mcp__audio-ar__separate_stems,mcp__audio-ar__render_visuals,mcp__audio-ar__analyze_stems,mcp__audio-ar__cyanite_analyze,mcp__audio-ar__musicai_analyze,mcp__audio-ar__soundcharts_lookup — subagents don't inherit MCP discovery.
- Relay the agent's A&R card back verbatim.
2. Batch — /ops:ops-ar <file1> <file2> ... (or multiple URLs)
A&R multiple local paths or URLs in one invocation:
- Collect inputs — every argument after the skill name is a track (local file or URL/Dropbox). Download URLs first (same rules as single-track).
- Dedupe by md5 (same file copied under different names counts once).
- Analyze per track — spawn ar-producer (Opus) per deduped track, in waves of ≤2 (stem separation is CPU-heavy; check
nproc/uptime first). Same subagent MCP rule as single-track mode (include the full ToolSearch select:mcp__audio-ar__... list). Relay each agent's full A&R card back verbatim.
- Summarize — compile a ranked verdict table from the per-track cards.
3. Inbox sweep — /ops:ops-ar inbox [from <sender> ...]
Pull every demo/song from the user's Gmail inbox and A&R them all:
- Find demos:
gog gmail search 'has:attachment (filename:mp3 OR filename:wav OR filename:m4a OR filename:aiff OR filename:flac OR filename:ogg OR filename:aac)' (add from: filters if senders given). Confirm scope with the user if the set is large (>10 threads).
- Download attachments to disk without flooding context: per thread,
gog gmail thread get <tid> -j → message ids from thread.messages[].id (envelope {downloaded, thread: {messages: [...]}} — NOT top-level messages) → gog gmail raw <mid> -j piped to jq for audio parts (filename + attachmentId) → gog gmail attachment <mid> <aid> --out <dir>/<label>__<file>. Also grep text parts for external links (postal.music, disco.ac, wetransfer, dropbox) — flag link-only demos that need a login as NOT ANALYZED and tell the user to request a file re-send.
- Dedupe by md5 (forwarded demos repeat across threads).
- Analyze per track — spawn ar-producer (Opus) per deduped demo, in waves of ≤2 (stem separation is CPU-heavy; check
nproc/uptime first). Same subagent MCP rule as single-track mode (include the full ToolSearch select:mcp__audio-ar__... list). Relay each agent's full A&R card back verbatim.
- Summarize — compile a ranked verdict table from the per-track cards.
4. Email delivery — "send the verdict to my email"
House rule: every A&R email ALWAYS includes (a) a per-track DIRECT LISTEN LINK and (b) the FULL A&R card per track — never just the ranked summary.
- DIRECT listen link (mandatory): a link that actually PLAYS the audio — any external streaming link found in the thread (postal.music, disco.ac, …), OR upload the demo to Google Drive (
gog drive upload <file> → share → direct link). An email-thread link alone is NOT sufficient.
- Also include the Gmail deep-link (
gog gmail url <threadId>) — both direct + email link is ideal.
- Rule 6 applies in full: stage the final draft, get explicit per-message approval, then send (one approval = one send).
Pro APIs (Cyanite / Music.ai / Soundcharts) — operational notes
- Cyanite (
$AUDIO_AR_HOME/venv/bin/python pro_apis.py cyanite <file>): returns genreTags, moodTags, bpmRangeAdjusted, era, voice gender, energyLevel, valence, arousal. Free/trial plans have a LIFETIME library cap — deleting tracks does NOT free quota. On librarySizeLimitExceededError, route through Music.ai instead.
- Music.ai (
pro_apis.py musicai <file>, needs $MUSICAI_WORKFLOW, e.g. a "Metadata Suite" workflow): same Cyanite engine on separate billing + extras — ai_voice (Real vs AI-GENERATED — always flag AI guide vocals: they're placeholders needing a real singer), voice_gender, instruments. Implementation gotchas (already handled in pro_apis.py): requests need a browser User-Agent (Cloudflare 1010 blocks default python-urllib), and the upload-URL request must be a clean GET with no body. Convert .m4a to mp3 before upload.
- Soundcharts (
pro_apis.py soundcharts <query>): released-catalogue lookup only — useless for unreleased demos; use it for reference-track benchmarking in the REFERENCE section.
Interpretation guardrails (carry into every card)
- Demo bounces are loud and dull on top — judge song/topline/lane, not the demo master.
- Never infer missing verses / song incompleteness from a sparse Whisper transcript (low vocal in the bounce ≠ unwritten song).
- librosa BPM can read doubled/halved — trust the pro-layer
bpmRangeAdjusted when available; otherwise confirm by groove.
- Verify hit-claims with data (CLAP commercial lean, valence/arousal), but the verdict is producer judgment, not a printout.
Fallback
If mcp__audio-ar__* is unavailable, run the stack directly via Bash from $AUDIO_AR_HOME (venv/bin/python analyze.py <file>, clap_score.py, transcribe.py, pro_apis.py). Never fabricate analysis — if nothing ran, say so.
Agent Teams support
If CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1 is set, use Agent Teams when dispatching multiple ar-producer agents in batch or inbox-sweep mode. This enables:
- Agents share partial findings in real time (e.g., one agent finds a stem issue → others adjust their verdict framing accordingly)
- You can steer mid-sweep: "prioritize the demo from sender X first"
- Progress is visible per track as agents report back
Team setup (only when flag is enabled, batch/inbox dispatch phase):
TeamCreate("ar-batch")
Agent(team_name="ar-batch", name="ar-[track-slug]", ...)
If the flag is NOT set, use standard parallel subagents (fire-and-forget, waves of ≤2).
