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/call-intelligencenpx skills add manusco/resonance --skill call-intelligencegit clone --depth 1 https://github.com/manusco/resonanceWrote 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/manusco/resonance/call-intelligence)<a href="https://agentmods.dev/skills/manusco/resonance/call-intelligence"><img src="https://agentmods.dev/badge/skills/manusco/resonance/call-intelligence.svg" alt="Measured on agentmods" 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 | $0.00049 | $0.00707 |
| Opus 5 | $0.00024 | $0.00353 |
| Sonnet 5 | $0.00010 | $0.00141 |
| Haiku 4.5 | $0.00005 | $0.00071 |
Grade A, and why
resonance-sales-call-intelligence 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 4d 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 — 40 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/resonance-sales-call-intelligence: analyze sales transcripts
Role: resonance-sales Input: Sales call transcripts (pasted text, CSV, or MCP connections). Output: A structured Persona Intelligence Report and Interactive React Dashboard. Definition of Done: The output dashboard parses speaker turns, extracts goals, pains, triggers, objections, and product feature gaps with direct verbatims, and provides a messaging vocabulary library. Free of AI slop and em dashes. Passed the validator.
Prerequisites (fail fast)
- Transcripts or call recordings are available for analysis.
- You have identified if speaker attribution (rep vs. prospect) is present or needs auto-inference.
Algorithm
Copy this checklist and tick items as you go.
- Profile Prospects: Extract job titles, seniority levels, and target department groupings (e.g., C-Suite, VP, Manager). → verify: dataset confidence level is declared at top.
- Ingest and Clean: Ingest raw text or CSV entries, map speakers, and separate conversation blocks. → verify: transcript source type is declared.
- Extract 10 Dimensions: Extract goals, pains (functional/emotional/social), triggers, objections, product gaps, competitor comments, buying processes, vocabulary words, buying signals, and red flags. → verify: all quotes are extracted exactly as spoken.
- Objection & Feature Analysis: Tabulate objection types and rank product feature gaps by frequency. → verify: objection handling is rated as Effective, Neutral, or Missed.
- Interactive Dashboard: Construct an interactive React-based dashboard displaying tabs for Overview, Personas, Objections, Feature Gaps, and Vocabulary. → verify: contains a bar chart representing objection frequency.
Recovery
- Transcript dataset size is extremely small (1-2 calls) → label confidence as LOW and append a standard directional hypothesis warning.
- Speaker attribution is missing or mixed → run speaker role auto-inference logic (detecting who explains pricing/product vs. who highlights constraints) before continuing.
- Tried to build a dashboard 3 times but React components hit rendering errors → stop, output the structured long-form markdown report instead, and escalate.
What ships with it
4 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.
- 4d ago First seen · 40 lines · 49 tokens per session scan A b6d1f3d90384
resonance-sales-call-intelligence is a skill published in the GitHub repository manusco/resonance (37 stars, last pushed 2d ago), licensed MIT. It adds 49 tokens to every session and 707 once invoked, about $0.0002 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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