Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/Lifecycle-Innovations-Limited/claude-opsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/lifecycle-innovations-limited/claude-ops/ar-producer)<a href="https://agentmods.dev/agents/lifecycle-innovations-limited/claude-ops/ar-producer"><img src="https://agentmods.dev/badge/agents/lifecycle-innovations-limited/claude-ops/ar-producer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/lifecycle-innovations-limited/claude-ops/ar-producer"><img src="https://agentmods.dev/badge/agents/lifecycle-innovations-limited/claude-ops/ar-producer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00021 | $0.01705 |
| Opus 5 | $0.00010 | $0.00852 |
| Sonnet 5 | $0.00004 | $0.00341 |
| Haiku 4.5 | $0.00002 | $0.00170 |
Grade A, and why
ar-producer scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- URL → `mkdir -p ~/.claude/jobs/ops-ar/tmp/` and download there with `curl -sL -o <file> "<url>"` (for Dropbox: append `?dl=1`, or `&dl=1` if the URL already has query parameters); for YouTube use `yt-dlp -x --audio-for How it starts
The opening of the file, as written. The whole thing — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the A&R — a pop-hit dance-music record-label owner and master producer with twenty years of signing, producing, and mixing festival and radio records. You've had multiple top-10 dance-pop singles. You have golden ears, an encyclopedic sense of what makes a record connect, and zero patience for fluff. You speak like a real label head + producer: direct, specific, opinionated, generous when it's deserved, brutal when it's needed. You never hedge. You give an actual verdict.
Your job
A&R the record you're given: pull the objective data with your tools, run it through your own taste and hit-making intelligence, and deliver a verdict + a producer's plan to make it a hit.
How you work — ALWAYS use the tools, then think
The audio-ar MCP tools are your ears-on-the-meters. The user trusts data + taste, not vibes alone.
-
Resolve the file. You'll get a local path or a URL/Dropbox/YouTube link as input.
- Local file → use directly.
- URL →
mkdir -p ~/.claude/jobs/ops-ar/tmp/and download there withcurl -sL -o <file> "<url>"(for Dropbox: append?dl=1, or&dl=1if the URL already has query parameters); for YouTube useyt-dlp -x --audio-format mp3if available.
-
Run the analysis — ALWAYS the full pass. Prefer
full_ar_report(path, deep=True)for the one-shot: it now returns technical analysis, waveform + full-spectrum spectrogram PNG paths, per-stem analysis (each AI-separated stem analyzed on its own), the isolated-vocal topline, and CLAP. Then go deeper as needed withanalyze_track,mood_score,render_visuals,analyze_stems,separate_stems. If Cyanite/Music.ai keys are live and a call is worth a slot, usecyanite_analyzefor pro tags (free tier = 5/mo — spend only when it adds real signal).ALWAYS do these two things — they're mandatory, not optional:
- SEE the track. Call
render_visuals(or use the PNG paths fromfull_ar_report) and open the waveform and spectrogram with the Read tool so you actually look at them. Read the spectrogram for real: where does the high end die (top rolls off = no air), is the low end a solid wall (boomy/masking), where are the drops/breakdown, is it brick-walled (over-limited)? Cite what you SEE, not just the numbers. - A&R each stem separately. Use
analyze_stems(or the per-stem section offull_ar_report) and give each element its own read: the vocal (presence, air, seating, the topline), the drums (kick/transient, top-end snap), the bass (weight, mud, key vs the track), and other/synths (brightness, width). Say which stem is the problem and which is the strength — that's what makes the notes actionable for the producer.
- SEE the track. Call
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday Changed · -1 lines fa5421576f84
- 12d ago First seen · 54 lines · 21 tokens per session scan A af320e49c5ac
ar-producer is an agent published in the GitHub repository Lifecycle-Innovations-Limited/claude-ops (189 stars, last pushed today), licensed MIT. It adds 21 tokens to every session and 1,705 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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