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 agentmods add skills/manusco/resonance/lead-opsnpx skills add manusco/resonance --skill lead-opsgit clone --depth 1 https://github.com/manusco/resonanceWhat 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 | $0.00064 | $0.01714 |
| Opus 5 | $0.00032 | $0.00857 |
| Sonnet 5 | $0.00013 | $0.00343 |
| Haiku 4.5 | $0.00006 | $0.00171 |
Grade A, and why
resonance-sales-lead-ops scanned grade A with 0 findings 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 3d ago.
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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/resonance-sales-lead-ops: no lead left behind
Role: resonance-sales Input: A lead cohort (CRM filter, date range, source, or specific lead IDs) + CRM access. Output: A lead treatment audit report with severity classifications, owner resolutions, CX maps, and recommended actions. Definition of Done: Every audited lead has a treatment classification, an owner resolution with provenance, a CX timeline, and a recommended next action. No inferred owners from timeline activity alone. No auto-escalation. Free of AI slop and em dashes. Passed the validator.
Prerequisites (fail fast)
- A lead cohort and source window are defined (which leads, from when).
- CRM access is available to read contact, company, deal, outreach, and meeting data.
Algorithm
Copy this checklist and tick items as you go.
Job 1: Lead Treatment Audit
-
Define the Cohort: Identify the leads to audit by source, date range, lifecycle stage, or ad-hoc list. Confirm the audit window (e.g., "all MQLs from the past 7 days"). → verify: cohort is bounded, not open-ended.
-
Review Each Lead's Treatment: For every lead in the cohort, inspect:
- CRM owner assignment and timestamp
- Outreach timeline (emails, calls, LinkedIn, meetings)
- Automation touches vs. real human follow-up
- Meeting status (booked, held, no-show, rescheduled)
- Relevant conversation evidence (notes, chat threads) → verify: automated touches are distinguished from manual human outreach.
-
Classify Treatment Quality: Assign each lead exactly one classification:
Classification Definition Manual A human rep made direct, personalized outreach Automated Only automation sequences touched the lead Mixed Both automation and human outreach occurred Missing No outreach of any kind within the SLA window Stale Outreach happened but stopped before a meeting or resolution → verify: every lead has exactly one classification.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 3d ago First seen · 124 lines · 64 tokens per session scan A c33d8de7f4ab
resonance-sales-lead-ops is a skill published in the GitHub repository manusco/resonance (37 stars, last pushed 3d ago), licensed MIT. It adds 64 tokens to every session and 1,714 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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