closed-won-analysis

closed-won-analysis is a skill for Claude Code, Codex from Autter-dev/agentic-sales-skills. It costs 23 tokens per session (1,051 once invoked), scanned A, original, MIT.

A post-sale review of a deal that was won, focused on what helped it close, what nearly stopped it, and what the team can repeat. A closed-won deal is a sales opportunity that ended with a signed agreement.

In plain words
What is it for?
Use it to review a recent win, compare several wins, identify decision-makers and blockers, build a case study, or create repeatable sales playbooks.
Why use it?
It turns one successful sale into concrete lessons instead of leaving useful details in people's memories or scattered notes.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to review a recent win, compare several wins, identify decision-makers and blockers, build a case study, or create repeatable sales playbooks.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/autter-dev/agentic-sales-skills/closed-won-analysis
Install

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.

Any agent
npx skills add Autter-dev/agentic-sales-skills --skill closed-won-analysis
Clone the repo
git clone --depth 1 https://github.com/Autter-dev/agentic-sales-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for closed-won-analysis

README.md
[![agentmods](https://agentmods.dev/badge/skills/autter-dev/agentic-sales-skills/closed-won-analysis/github.svg)](https://agentmods.dev/skills/autter-dev/agentic-sales-skills/closed-won-analysis)
Your own site
<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.

agentmods 80×15 button for closed-won-analysis

Your own site · 80×15
<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>
Per session 23 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,051 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 8d ago against content hash 465eb2f85453, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

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.

04-proposals-and-close/skills/closed-won-analysis/SKILL.md · 113 lines

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?

Read the full file on GitHub · 113 lines

Files

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.

Changes

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.

  1. 8d ago First seen · 113 lines · 23 tokens per session scan A 465eb2f85453

Subscribe to this mod's changes

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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