--- name: Ops A&R slug: ops-a-r category: AI Engineering description: Ops A&R scores tracks with an A&R verdict card, then outlines what works, what needs work, and the producer plan. Use it when reviewing demos, making sign/develop/pass calls, or routing releases to the right imprint. github: "https://github.com/Lifecycle-Innovations-Limited/claude-ops/tree/main/claude-ops/skills/ops-ar" language: Shell stars: 186 forks: 21 install: "npx degit https://github.com/Lifecycle-Innovations-Limited/claude-ops/tree/main/claude-ops/skills/ops-ar ~/.claude/skills/ops-ar" installs_to: ~/.claude/skills/ops-ar source_path: claude-ops/skills/ops-ar/SKILL.md collection_size: 25 category_size: 3278 collection_url: "https://dirskills.com/collections/Lifecycle-Innovations-Limited/claude-ops" added: 2026-09-06T05:20:34.952Z last_synced: 2026-09-06T05:20:34.952Z canonical_url: "https://dirskills.com/skills/ops-a-r" --- # Ops A&R Ops A&R scores tracks with an A&R verdict card, then outlines what works, what needs work, and the producer plan. Use it when reviewing demos, making sign/develop/pass calls, or routing releases to the right imprint. **Install:** ```bash npx degit https://github.com/Lifecycle-Innovations-Limited/claude-ops/tree/main/claude-ops/skills/ops-ar ~/.claude/skills/ops-ar ``` ## README # /ops:ops-ar — A&R Command Load `ops-rules` before acting. Public repo (no personal data). Outbound: one draft → one approval → one send. If `AskUserQuestion` / `Workflow` are missing, follow Rule 10 in `ops-rules` (Hermes: numbered options / two-turn Telegram card; `delegate_task`). 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 `$PREFS_PATH` | dance-pop / feel-good house | The skill must read these at runtime — never hardcode user names, mailboxes, label names, or absolute `/Users/...` paths. ### Taste profile injection (mandatory when `ar.profile` exists) Before spawning any ar-producer agent, read the profile once: ```bash PREFS="${CLAUDE_PLUGIN_DATA_DIR:-$HOME/.claude/plugins/data/ops-ops-marketplace}/preferences.json" jq '.ar.profile // empty' "$PREFS" ``` If non-empty, inject the **whole JSON object verbatim** into every ar-producer spawn prompt, with the instruction: *"Judge as the A&R for THIS owner/label. Calibrate the verdict per `verdict_calibration`. Weigh hooks and toplines against `songwriting_taste`. REFERENCE & POSITIONING must position against `catalog_recent`, `signature_classics`, and `label_roster_third_party` — name the closest catalog comparison, not generic genre acts. Check tempo against `tempo_sweet_spot` and brand fit against `lane`. If `signing_structure` is present, the NEXT section of a sign/develop verdict must state which entity signs the master."* Fields the profile may carry (all optional): `owner`, `label`, `lane`, `tempo_sweet_spot`, `ar_team`, `signing_structure`, `reference_acts`, `catalog_recent`, `signature_classics`, `label_roster_third_party`, `songwriting_taste`, `verdict_calibration`. If the profile is absent, fall back to the generic dance-pop / feel-good house default and say so in the card header. ### Imprint gate (mandatory when `ar.imprints` is non-empty) Imprints are data, not code. Never hardcode an imprint's name, sound, or test — read them: ```bash jq -r '.ar.imprints // {} | keys[]' "$PREFS" # imprint keys jq '.ar.imprints[""]' "$PREFS" # one imprint ``` For each imprint whose lane the track falls in, inject that object verbatim into the ar-producer spawn prompt **except `brand_book_corpus`** (the full scraped brand-book text — too large for a spawn prompt; consult it only when a card needs exact brand-book language, via `jq '.ar.imprints[""].brand_book_corpus.pages | keys'` then the specific page). Always inject `sound_description` — it is the imprint's sonic north star and the tiebreaker on the subjective points. Add an **IMPRINT** section to the A&R card between VERDICT and WHAT'S WORKING: score the track against that imprint's own `ten_point_test` per its `scoring_rule` (one line per point, pass/fail/N-A, with the failing evidence named), then one routing line — eligible for ``, or route to parent label. An imprint is a strict subset of the label: a failed gate never downgrades the main verdict, it only routes the release. Owner's own tracks always get the scorecard; third-party demos get it only when they are in that imprint's lane. Each imprint object may carry: `display_name`, `lane`, `sound_description`, `ten_point_test`, `scoring_rule`, `brand_book_corpus`. If `ar.imprints` is absent or empty, skip the IMPRINT section entirely — do not invent an imprint. ## Modes ### 1. Single track — `/ops:ops-ar ` 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 ...` (or multiple URLs) A&R multiple local paths or URLs in one invocation: 1. **Collect inputs** — every argument after the skill name is a track (local file or URL/Dropbox). Download URLs first (same rules as single-track). 2. **Dedupe** by md5 (same file copied under different names counts once). 3. **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. 4. **Summarize** — compile a ranked verdict table from the per-track cards. ### 3. Inbox sweep — `/ops:ops-ar inbox [from ...]` Pull every demo/song from the user's Gmail inbox and A&R them all: 1. **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). 2. **Download attachments to disk** without flooding context: per thread, `gog gmail thread get -j` → message ids from `thread.messages[].id` (envelope `{downloaded, thread: {messages: [...]}}` — NOT top-level `messages`) → `gog gmail raw -j` piped to `jq` for audio parts (filename + attachmentId) → `gog gmail attachment --out /