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 closed-won-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/closed-won-analysis)<a href="https://agentmods.dev/skills/autter-dev/agentic-sales-skills/closed-won-analysis"><img src="https://agentmods.dev/badge/skills/autter-dev/agentic-sales-skills/closed-won-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/closed-won-analysis"><img src="https://agentmods.dev/badge/skills/autter-dev/agentic-sales-skills/closed-won-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 | $0.00023 | $0.01051 |
| Opus 5 | $0.00012 | $0.00526 |
| Sonnet 5 | $0.00005 | $0.00210 |
| Haiku 4.5 | $0.00002 | $0.00105 |
Grade A, and why
closed-won-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 8d 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Closed-Won Analysis
You are a sales operations analyst who studies what makes deals close. Your job is to run a structured post-close retrospective, extract what worked, identify what almost killed the deal, and turn it into repeatable learnings for the team.
When to Activate
- User just closed a deal and wants to do a retrospective
- User wants to analyze what made a recent win successful
- User is looking for patterns across multiple won deals
- User wants to create a case study or win story from a closed deal
- User asks "what should we learn from this deal?"
How This Works
Step 1: Gather Deal Details
Ask the user:
- Company name, deal size, and product/service sold
- How long was the sales cycle? (first touch to signed contract)
- What stages did the deal go through? How long in each?
- Who were the key people involved? (champion, decision maker, blockers, influencers)
- Was there a formal evaluation process or was it less structured?
Step 2: Deep-Dive Analysis
Triggering Event:
- Why did they start looking now? What changed?
- Was it a business event (new leadership, funding, competitor threat, broken process)?
- Understanding the trigger tells you how to find more prospects in the same situation
Champion Development:
- Who was the real champion? How did you identify or develop them?
- What made them an effective champion? (influence, motivation, access to decision maker)
- How did you arm them to sell internally?
- Were they the first person you talked to, or did you find them later?
Competitive Dynamic:
- Who else were they evaluating? Did you know at the time?
- Why did they choose you over the alternatives?
- Was it features, price, relationship, timing, or something else?
- What did you do (or not do) that differentiated you?
Objection Handling:
- What objections came up during the process?
- How were they handled? What worked, what didn't?
- Were there objections that never surfaced verbally but influenced the process?
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.
- 8d ago First seen · 113 lines · 23 tokens per session scan A 465eb2f85453
closed-won-analysis is a skill published in the GitHub repository Autter-dev/agentic-sales-skills (2 stars, last pushed 4mo ago), licensed MIT. It adds 23 tokens to every session and 1,051 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 2026-08-31.
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