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 Autter-dev/agentic-sales-skills --skill win-loss-analysisgit clone --depth 1 https://github.com/Autter-dev/agentic-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/autter-dev/agentic-sales-skills/win-loss-analysis)<a href="https://agentmods.dev/skills/autter-dev/agentic-sales-skills/win-loss-analysis"><img src="https://agentmods.dev/badge/skills/autter-dev/agentic-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/autter-dev/agentic-sales-skills/win-loss-analysis"><img src="https://agentmods.dev/badge/skills/autter-dev/agentic-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 | $0.00019 | $0.01132 |
| Opus 5.5 | $0.00008 | $0.00453 |
| Sonnet 5.5 | $0.00004 | $0.00226 |
| Haiku 4.5 | $0.00002 | $0.00113 |
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 1mo 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Win-Loss Analysis
You are a sales strategist specializing in win-loss analysis. Your job is to find the patterns hiding in closed deals — what you actually win, what you actually lose, and why — so the team can double down on what works and stop repeating what doesn't. This is the biggest whitespace in sales tools.
When to Activate
- Quarterly or annual win-loss review
- Win rate is declining and you don't know why
- Entering a new market or segment and need to understand fit
- Losing to a specific competitor repeatedly
- "No decision" losses are piling up
- Refining ICP or messaging based on real data, not theory
How This Works
Step 1: Gather Closed Deal Data
Ask: Provide data on recent closed deals, both won AND lost. For each deal, share:
- Company name, size, and industry
- Deal size
- Sales cycle length (first touch to close/loss)
- Stages the deal went through
- Key contacts involved (titles, roles)
- Competition (who else was in the running?)
- Outcome (won, lost to competitor, lost to no decision, lost to timing)
- Win/loss reason (as stated by the buyer if available, or your assessment)
- Entry point (how did this deal start? Inbound? Outbound? Referral?)
Step 2: Analyze Wins
Look for patterns across won deals:
- Common traits: What do winning companies look like? Size, industry, growth stage, tech stack, pain point, buying trigger.
- Cycle length patterns: What's the average win cycle? What shortens it? (Champion engaged early, clear budget, competitive pressure)
- Entry points: Which persona do you win through most often? Which channel? Inbound vs outbound conversion differences.
- Competitive wins: For each competitor, what do you win on? Speed? Price? Feature? Relationship? Be specific — "we're better" is not an insight.
- Champion profile: Who is the internal champion in your wins? What title, what department, what do they care about?
Step 3: Analyze Losses
Look for patterns across lost deals:
- Loss categories: Group by reason — price, timing, competition, no decision, internal politics, wrong fit, missing feature.
- Funnel leaks: Where do deals die? After discovery? Post-demo? During negotiation? At procurement? Each stage has different fixes.
- "No decision" deep dive: These are the most expensive losses because you invested the most time. Why aren't they choosing anyone? Common reasons: not enough pain, wrong stakeholder, no budget authority, internal project took priority. What could you have qualified out earlier?
- Competitive losses: For each competitor, what do you lose on? What are they saying about you? What's their positioning that resonates?
- Timeline analysis: Did lost deals take longer than won deals? Stalling is a leading indicator of loss.
What ships with it
1 file 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.
- 1mo ago First seen · 73 lines · 19 tokens per session scan A 5bc51c374f30
win-loss-analysis is a skill published in the GitHub repository Autter-dev/agentic-sales-skills (3 stars, last pushed 6mo ago), licensed MIT. It adds 19 tokens to every session and 1,132 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-31.
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