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 matteotitta/genesys-skills --skill win-lossgit clone --depth 1 https://github.com/matteotitta/genesys-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/skills/matteotitta/genesys-skills/win-loss)<a href="https://agentmods.dev/skills/matteotitta/genesys-skills/win-loss"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/win-loss/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/matteotitta/genesys-skills/win-loss"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/win-loss.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 | $0.00023 | $0.02336 |
| Opus 5 | $0.00012 | $0.01168 |
| Sonnet 5 | $0.00005 | $0.00467 |
| Haiku 4.5 | $0.00002 | $0.00234 |
Grade A, and why
win-loss-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 9d 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 — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Win/loss analysis
Analyze sales call transcripts to extract actionable insights on why deals are won, lost, retained, or churned. Cross-reference findings with ICP, firmographics, and competitive context to produce strategic recommendations.
Claude Code triggers
Invoke when user says:
- "Win/loss analysis"
- "Analyze sales calls"
- "Why did we win/lose"
- "Churn analysis"
- "Retention analysis"
- "Sales call insights"
- "Deal outcome patterns"
- "Customer feedback synthesis"
- "Analyze these transcripts"
- "What patterns in our sales calls"
Do NOT invoke when:
- User wants general transcript analysis → use
transcript-analysis - User wants competitor research → use
competitor-research - User wants single customer interview analysis → use
transcript-analysis - User wants sales enablement assets → use
sales-enablement
Input requirements
Required
| Input | Description | Source |
|---|---|---|
| Transcripts | Sales call transcripts with customer name and outcome | User provides |
| Outcome | Win/Loss/Retention/Churn for each call | User specifies or infer |
Optional (improve quality)
| Input | How it helps |
|---|---|
| Website URL per customer | Firmographics cross-reference |
| Product/ICP document | Define in-scope product capabilities |
| Market/GTM document | Positioning and competitive landscape |
| Sales notes column | Additional context (stage, deal size) |
| Competitor names | Pre-identify competitors to watch for |
Validation
Before proceeding: at least one transcript provided; outcome known or inferable from transcript; customer name identifiable.
If inputs are missing: ask the user for transcripts. Clarify if outcome should be inferred from transcript signals.
Transcript intake — normalize any recorder format
Transcripts arrive in many shapes: Gong, Fireflies, Otter, Grain exports, Zoom/Avoma VTT, SRT, recorder JSON, or plain pasted text. Before Phase 1, normalize whatever you're handed into one shape — speaker-attributed turns, timestamps where present. See the premium reference for the sniff-and-parse table per format and the normalized target shape. /transcripts inherits the same reference.
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.
- 9d ago First seen · 221 lines · 93 tokens per session scan A 3beee0662882
win-loss-analysis is a skill published in the GitHub repository matteotitta/genesys-skills (36 stars, last pushed 1mo ago), licensed MIT. It adds 23 tokens to every session and 2,336 once invoked, about $0.0001 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 2026-09-03.
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