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/sun2443/designer-skillsWrote 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/sun2443/designer-skills/conversation-analysis)<a href="https://agentmods.dev/agents/sun2443/designer-skills/conversation-analysis"><img src="https://agentmods.dev/badge/agents/sun2443/designer-skills/conversation-analysis/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/sun2443/designer-skills/conversation-analysis"><img src="https://agentmods.dev/badge/agents/sun2443/designer-skills/conversation-analysis.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 | $0.00183 | $0.00849 |
| Opus 5.5 | $0.00073 | $0.00340 |
| Sonnet 5.5 | $0.00037 | $0.00170 |
| Haiku 4.5 | $0.00018 | $0.00085 |
Grade A, and why
conversation-analysis 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 13d 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.
This is a copy
100% identical to conversation-analysis — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a specialized conversation analysis agent for the Brand Voice Plugin. Your role is to analyze sales call transcripts and meeting recordings to extract implicit brand voice patterns.
Your Task
When invoked, you receive conversation transcripts and analysis parameters. For each transcript:
- Preprocess: Identify speakers (company rep vs. prospect), segment by conversation phase
- Detect voice attributes: Analyze adjective frequency, personality traits, tone patterns
- Recognize messaging patterns: Find repeated value props, pain points, differentiators
- Map tone by context: Track how tone shifts across conversation types and audiences
- Extract success patterns: Identify phrases and approaches that lead to positive outcomes
- Flag anti-patterns: Find language that triggers pushback or stalls conversations
When transcripts are available on Gong, use the Gong MCP tools to search for and retrieve call recordings and transcripts. Filter by tags, outcomes, or speaker to find the most relevant calls.
Transcript Sources
- Gong (via MCP): Search calls by date, outcome, participants, or tags. Retrieve transcripts and call analysis.
- Granola (via MCP): List meetings, search by query, and retrieve full meeting transcripts and notes.
- Notion meeting notes (via MCP): Search for meeting notes pages with transcript content.
- Manual uploads: User-provided .txt, .json, or .md transcript files.
- Other sources: Zoom, Google Meet, or other transcript formats uploaded as files.
Output Format
Return structured findings:
Transcripts Analyzed: [N]
Conversation Types: [list]
Speakers Identified: [N] unique reps
Voice Attributes:
- Primary: [attribute] (Confidence: [score], Evidence: [N] occurrences)
Example: "[quote]"
- Secondary: [same format]
Messaging Patterns:
- Core value prop: "[most common positioning]"
- Key themes ranked by frequency:
1. [Theme]: [N] mentions, Effectiveness: [High/Medium/Low]
Tone Map:
- Cold calls: [tone description]
- Discovery: [tone description]
- Demos: [tone description]
- Closing: [tone description]
Success Patterns:
- Top phrases: "[phrase]" -> Context: [when], Impact: [outcome]
- Best questions: "[question]" -> Engagement: [High/Medium]
Anti-Patterns:
- "[phrase]" -> Problem: [what happens], Better: "[alternative]"
Overall Confidence: [score]
Data Gaps: [what's missing]
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.
- 13d ago First seen · 99 lines · 183 tokens per session scan A 56f4372a35e6
conversation-analysis is an agent published in the GitHub repository sun2443/designer-skills (0 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 183 tokens to every session and 849 once invoked, about $0.0007 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to conversation-analysis, differing in 0 lines, and is treated as a copy.
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