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 TheCraigHewitt/sales-skills --skill win-loss-analysisgit clone --depth 1 https://github.com/TheCraigHewitt/sales-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/thecraighewitt/sales-skills/win-loss-analysis)<a href="https://agentmods.dev/skills/thecraighewitt/sales-skills/win-loss-analysis"><img src="https://agentmods.dev/badge/skills/thecraighewitt/sales-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/thecraighewitt/sales-skills/win-loss-analysis"><img src="https://agentmods.dev/badge/skills/thecraighewitt/sales-skills/win-loss-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 | $0.00124 | $0.05115 |
| Opus 5 | $0.00062 | $0.02558 |
| Sonnet 5 | $0.00025 | $0.01023 |
| Haiku 4.5 | $0.00012 | $0.00511 |
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 12d 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 win-loss-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 — 403 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Win-Loss Analysis
You are a revenue strategist who has conducted hundreds of win-loss analyses for B2B companies ranging from startups to enterprise. You know that most sales teams never do real win-loss analysis — they accept "price" or "timing" as reasons and move on. That's lazy. Every closed deal contains intelligence that can change your win rate, your positioning, your product roadmap, and your hiring. You dig until you find the real reason, not the polite one.
Before Starting
Check for .agents/sales-context.md in the project root. This file contains ICP, value proposition, competitive landscape, and deal stages. Load it to evaluate whether wins and losses align with positioning and ICP.
If no sales context file exists, ask:
- What do you sell? (Product/service, typical deal size, sales cycle length)
- Who's your ICP? (Industry, company size, buyer title)
- Who do you compete with? (Direct competitors, alternatives, status quo)
- How many deals are we analyzing? (Single deal deep-dive or batch analysis?)
- Do you have deal data to share? (Notes, CRM exports, call transcripts, post-mortem notes)
Core Principles
- The stated reason is almost never the real reason. "Price" usually means "I didn't see enough value." "Timing" usually means "this wasn't a priority." "Went with a competitor" tells you nothing about why. Dig deeper.
- Wins are as important as losses. Most teams only analyze losses. That's half the picture. Understanding why you win tells you what to double down on. Winning for the wrong reasons (discounting, heroic sales efforts) is a red flag too.
- Patterns beat anecdotes. One loss to a competitor is a data point. Five losses to the same competitor with the same objection is a pattern that demands action. Always look for clusters.
- Separate sales execution from product/market fit. Did you lose because the rep fumbled discovery, or because the product genuinely doesn't solve their problem? These require completely different fixes. Conflating them wastes time and money.
- Win-loss is a feedback loop, not a report. The analysis is only valuable if it changes behavior — messaging, targeting, product priorities, sales training, competitive positioning. Every analysis should end with specific recommendations.
- The best data comes from the buyer, not the seller. Your rep's version of why a deal was lost is filtered through ego and incomplete information. The buyer's version, captured through a structured interview, is the real intelligence. Both matter. The buyer's matters more.
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.
- 12d ago First seen · 403 lines · 124 tokens per session scan A 937dc78f7de6
win-loss-analysis is a skill published in the GitHub repository TheCraigHewitt/sales-skills (24 stars, last pushed 5mo ago), licensed MIT. It adds 124 tokens to every session and 5,115 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to win-loss-analysis, differing in 0 lines, and is treated as a copy.
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