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 julianoczkowski/product-manager --skill pm-win-loss-analysisgit clone --depth 1 https://github.com/julianoczkowski/product-managerWrote 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/julianoczkowski/product-manager/pm-win-loss-analysis)<a href="https://agentmods.dev/skills/julianoczkowski/product-manager/pm-win-loss-analysis"><img src="https://agentmods.dev/badge/skills/julianoczkowski/product-manager/pm-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/julianoczkowski/product-manager/pm-win-loss-analysis"><img src="https://agentmods.dev/badge/skills/julianoczkowski/product-manager/pm-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 | $0.00135 | $0.01534 |
| Opus 5 | $0.00068 | $0.00767 |
| Sonnet 5 | $0.00027 | $0.00307 |
| Haiku 4.5 | $0.00014 | $0.00153 |
Grade A, and why
pm-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 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.
How it starts
The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Win/Loss Analysis (Framework: Market → Win/Loss Analysis)
Rule: Win/loss should be done by someone not involved in that sales effort. You are
evaluating the buying process, not the salespeople. Build a champion in sales
leadership to get access, and reassure everyone you're studying how the market buys.
Ideally the product team owns win/loss. See ../pm-copilot/references/framework.md.
Ranking scales (use verbatim)
- Perception / quality (most questions): rank 1–6, where 1 = Poor, 6 = Excellent, NA = Not Applicable. (Site-visit value: 1 = Low, 6 = High.)
- Price / value: rank 1–5, where 1 = Lower, 3 = On Par, 5 = Higher, plus "No Response."
- Conclusion perception: rank 1–6, 1 = Poor, 6 = Excellent.
The interview guide — 8 sections (~49 questions)
1. Customer Information — Company name, address, and a contacts table (Name / Title / Phone / Email).
2. Engagement Background — Did you know of us before initial contact? (Y/N) If so, how? Prior perception of company / products & services / support (1–6). How was initial contact made? (Unsolicited RFI / Unsolicited RFP / Cold Call / Channel Lead / Web). Who made contact, and when?
3. Marketing — Which informational tools did you use? (corporate & product brochures, website, white papers, analyst reports, trade magazines, technical papers…). Rate corporate literature / product literature / website (1–6) with comments.
4. Site Visits — Did you visit customer/reference sites? (Y/N) How many live-product sites? Which sites, and rate each. Overall value of the site visits (1–6).
5. RFI/RFP Process — Was the proposal well written? Presented well visually? Did it reflect understanding of your requirements? Meet functional / implementation / support requirements? What would have made it more compelling? (each 1–6 + comments)
6. Buying Decision — What were you using before? Extent of each contact's involvement (made final decision / voted / recommended). Who else was involved (Name / Title / Role)? What were you originally looking for, and what were your selection criteria? Did our sales team understand your needs? How did you build the vendor list? Final ranking of vendors (1st/2nd/3rd)? Did you use an outside consultant? Key factors that compelled the choice. Where were we strongest / weakest? Was there a clear point where we were winning or losing? Most important criteria in choosing the winner. Winner's major strengths over the loser. Expected business benefits. What would it have taken to change the outcome?
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 · 110 lines · 135 tokens per session scan A 157194ff373e
pm-win-loss-analysis is a skill published in the GitHub repository julianoczkowski/product-manager (32 stars, last pushed 1mo ago), licensed MIT. It adds 135 tokens to every session and 1,534 once invoked, about $0.0007 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 2026-08-30.
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