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 naveedharri/benai-skills --skill win-loss-analysisgit clone --depth 1 https://github.com/naveedharri/benai-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/naveedharri/benai-skills/win-loss-analysis)<a href="https://agentmods.dev/skills/naveedharri/benai-skills/win-loss-analysis"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/win-loss-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/skills/naveedharri/benai-skills/win-loss-analysis"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/win-loss-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Memory Poisoning · line 208 Skill injects content designed to persist in agent memory or context across interactions. Persistent injection can alter agent behavior long after the initial interaction.Fix: Do not allow untrusted input to persist in agent memory or context. Validate all content before storing and implement memory isolation between sessions.
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 | $0.00077 | $0.02955 |
| Opus 5 | $0.00039 | $0.01477 |
| Sonnet 5 | $0.00015 | $0.00591 |
| Haiku 4.5 | $0.00008 | $0.00296 |
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 6d 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 — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Win/Loss Pattern Analysis
Build a data-driven understanding of why deals close and why they don't, then deliver a comprehensive persona and strategy document as a professional .docx report.
The report includes: Executive summary with metrics, won/lost case studies with evidence from calls and emails, side-by-side pattern comparison, a complete winning prospect persona, red flags and disqualification criteria, strategic recommendations, and data appendix.
Phase 0: Discovery Questions
Use AskUserQuestion (3-4 questions max) to gather:
- CRM & Pipeline: Which CRM, and the pipeline/list name containing deals?
- Stage Labels: Exact names for Won and Lost stages (e.g., "Delivered"/"Lost", "Closed Won"/"Closed Lost")
- Business Context: 2-3 sentences on what the company sells and to whom
- Output Preferences: Full analysis or specific questions to answer?
Confirm understanding in one sentence before proceeding.
Phase 1: CRM Data Extraction
Goal: Clean Won and Lost deal lists with contact details, minimizing API calls.
Strategy
- Filter at API level, only request Won/Lost stage records, never pull all then filter locally
- Request only needed fields, name, email, company/domain, deal size, priority, source, close date
- Read CRM-specific reference, check available MCP tools, then read the appropriate file:
- Attio →
references/crm-attio.md| HubSpot →references/crm-hubspot.md| Salesforce →references/crm-salesforce.md| Other →references/crm-generic.md
- Attio →
Steps
- Discover pipeline structure (list attributes, stage IDs, custom fields)
- Pull Won deals filtered by stage
- Pull Lost deals filtered by stage
- Filter out personal email domains (
gmail.com,yahoo.com,hotmail.com,outlook.com,icloud.com,googlemail.com,aol.com,protonmail.com,live.com,me.com,mail.com,yandex.com,zoho.com,gmx.com,fastmail.com). Keep a count of filtered leads for the report. - Organize into two clean lists with counts
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.
- 6d ago First seen · 211 lines · 0 tokens per session scan A b80750dd3ab6
win-loss-analysis is a skill published in the GitHub repository naveedharri/benai-skills (61 stars, last pushed 7d ago), licensed MIT. It adds 77 tokens to every session and 2,955 once invoked, about $0.0004 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 2026-09-05.
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