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 CarbeneAI/Forge --skill callintelligencegit clone --depth 1 https://github.com/CarbeneAI/ForgeWrote 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/carbeneai/forge/callintelligence)<a href="https://agentmods.dev/skills/carbeneai/forge/callintelligence"><img src="https://agentmods.dev/badge/skills/carbeneai/forge/callintelligence/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/carbeneai/forge/callintelligence"><img src="https://agentmods.dev/badge/skills/carbeneai/forge/callintelligence.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 | $0.00054 | $0.00874 |
| Opus 5.5 | $0.00022 | $0.00350 |
| Sonnet 5.5 | $0.00011 | $0.00175 |
| Haiku 4.5 | $0.00005 | $0.00087 |
Grade A, and why
CallIntelligence 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 1mo 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CallIntelligence
Extract actionable intelligence from call transcripts for tracking sales pipeline health, customer sentiment, feature requests, and competitive landscape.
Workflow Routing
| Workflow | Trigger | File |
|---|---|---|
| Analyze | "analyze this call", "process transcript" | workflows/Analyze.md |
| Report | "call intelligence report", "weekly call summary" | workflows/Report.md |
Quick Start
Manual Analysis (Paste Transcript)
User: "Analyze this sales call transcript: [paste transcript]"
→ Uses extract_call_intelligence Fabric pattern
→ Returns structured JSON with competitors, objections, deal probability
→ Optionally appends to Google Sheet
Automated Pipeline (n8n)
Webhook at calls.home.yourdomain.com receives:
- Client name
- Call type (sales/customer_success)
- Transcript text
Pipeline: Webhook → PAI Analysis → Google Sheets → Discord notification
Output Fields
Sales Calls
| Field | Description |
|---|---|
| summary | 2-3 sentence call overview |
| competitors | Companies/products mentioned as alternatives |
| objections | Concerns or pushback from prospect |
| feature_requests | Capabilities they asked about |
| deal_probability | 0-100% likelihood to close |
| next_steps | Agreed action items |
Customer Success Calls
| Field | Description |
|---|---|
| summary | 2-3 sentence call overview |
| feature_requests | Desired new capabilities |
| recurring_issues | Pain points or problems discussed |
| expansion_signals | Interest in upgrades/growth |
| sentiment | 1-10 overall satisfaction score |
| follow_up_required | Action items needed |
Google Sheets Integration
Sheet: "Call Intelligence" (two tabs)
Sales Calls Tab Columns
Date | Client | Summary | Competitors | Objections | Feature Requests | Deal Probability | Next Steps
Customer Success Tab Columns
Date | Client | Summary | Feature Requests | Recurring Issues | Expansion Signals | Sentiment | Follow-up
What ships with it
6 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.
- 1mo ago First seen · 118 lines · 54 tokens per session scan A 6495bdb45cab
CallIntelligence is a skill published in the GitHub repository CarbeneAI/Forge (9 stars, last pushed 2mo ago), licensed MIT. It adds 54 tokens to every session and 874 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-31.
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