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.
npx skills add Lifecycle-Innovations-Limited/claude-ops --skill ops-argit 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/skills/lifecycle-innovations-limited/claude-ops/ops-ar)<a href="https://agentmods.dev/skills/lifecycle-innovations-limited/claude-ops/ops-ar"><img src="https://agentmods.dev/badge/skills/lifecycle-innovations-limited/claude-ops/ops-ar/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/skills/lifecycle-innovations-limited/claude-ops/ops-ar"><img src="https://agentmods.dev/badge/skills/lifecycle-innovations-limited/claude-ops/ops-ar.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 34 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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 | $0.00029 | $0.03579 |
| Opus 5.5 | $0.00012 | $0.01432 |
| Sonnet 5.5 | $0.00006 | $0.00716 |
| Haiku 4.5 | $0.00003 | $0.00358 |
Grade A, and why
ops-ar scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **Music.ai** (`pro_apis.py musicai <file>`, needs `$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 vocal How it starts
The opening of the file, as written. The whole thing — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/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 $PREFS_PATH |
dance-pop / feel-good house |
The skill must read these at runtime — never hardcode user names, mailboxes, label names, or absolute /Users/... paths.
Taste profile injection (mandatory when ar.profile exists)
Before spawning any ar-producer agent, read the profile once:
PREFS="${CLAUDE_PLUGIN_DATA_DIR:-$HOME/.claude/plugins/data/ops-ops-marketplace}/preferences.json"
jq '.ar.profile // empty' "$PREFS"
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.
- today Changed · +6 lines 138e1eb17704
- 23d ago Changed · +4 lines 4c36a0a8980f
- 1mo ago First seen · 139 lines · 29 tokens per session scan A 14c214bc92f8
ops-ar is a skill published in the GitHub repository Lifecycle-Innovations-Limited/claude-ops (547 stars, last pushed today), licensed MIT. It adds 29 tokens to every session and 3,579 once invoked, about $0.0001 per session on Opus 5.5. 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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