win-loss-review

win-loss-review is a skill for Claude Code, Codex from fzfclee/consulting-skills. It costs 54 tokens per session (728 once invoked), scanned A, original, Apache-2.0.

A review method for understanding why a deal, project, pitch, proposal, or client pursuit succeeded or failed. It separates the factual timeline from interpretation and classifies the causes by how much they could be changed.

In plain words
What is it for?
Use it after an outcome to examine the timeline, buyer or stakeholder feedback, competitors, decisions, and actions, then plan improvements.
Why use it?
It turns an outcome into specific lessons without reducing the discussion to blame.

Skill for Claude CodeCodex

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

Good fit Use it after an outcome to examine the timeline, buyer or stakeholder feedback, competitors, decisions, and actions, then plan improvements.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/fzfclee/consulting-skills/win-loss-review
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 fzfclee/consulting-skills --skill win-loss-review
Clone the repo
git clone --depth 1 https://github.com/fzfclee/consulting-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 win-loss-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/fzfclee/consulting-skills/win-loss-review/github.svg)](https://agentmods.dev/skills/fzfclee/consulting-skills/win-loss-review)
Your own site
<a href="https://agentmods.dev/skills/fzfclee/consulting-skills/win-loss-review"><img src="https://agentmods.dev/badge/skills/fzfclee/consulting-skills/win-loss-review/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 win-loss-review

Your own site · 80×15
<a href="https://agentmods.dev/skills/fzfclee/consulting-skills/win-loss-review"><img src="https://agentmods.dev/badge/skills/fzfclee/consulting-skills/win-loss-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 54 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 728 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 $0.00054 $0.00728
Opus 5 $0.00027 $0.00364
Sonnet 5 $0.00011 $0.00146
Haiku 4.5 $0.00005 $0.00073

Measured 11d ago against content hash 1d8670d19fcf, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

win-loss-review 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 11d 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.

skills/win-loss-review/SKILL.md · 87 lines

How it starts

The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Win Loss Review

Use this skill to run Win Loss Review as a practical consulting method, not as a generic framework explanation.

Method Notes

  • Separate factual timeline from interpretation.
  • Classify drivers as controllable, influenceable, or external.

Required Inputs

Collect or infer these inputs before execution:

  • opportunity history
  • outcome
  • buyer feedback
  • competitor information
  • sales actions

If an input is missing, do not block automatically. Mark it as missing, state the assumption used, and add a validation action.

When Not To Use

Do not use as a blame review. Use it to improve the next pursuit, decision, or operating process.

Adjacent Methods

  • deal-strategy-map: plan a live opportunity before the outcome.
  • competitive-positioning: improve buyer-facing differentiation from repeated decision evidence.

Step-by-Step Execution

Step Required input How to execute Output
Reconstruct timeline Opportunity data, meetings, proposal, feedback, outcome. Write factual chronology without interpretation. Win/loss timeline.
Map decision drivers Buyer criteria, stakeholders, competitors, price, proof. Identify why the buyer chose the outcome. Decision driver map.
Classify drivers Decision driver map. Mark each driver as controllable, influenceable, or external. Controllability view.
Extract lessons Controllability view and future pursuits. Convert drivers into specific behavior or asset changes. Lessons learned.
Update playbook Lessons, owners, next opportunities. Define qualification, messaging, proof, pricing, or relationship changes. Improvement action plan.

Output Template

### 1. Decision Timeline
Opportunity:
Outcome:
Buyer decision date:
Sources:

### 2. Decision Drivers
| Driver | Buyer evidence | Our performance | Competitor / alternative |
|---|---|---|---|
|  |  |  |  |

### 3. Controllability
| Factor | Controllable / influenceable / external | Confidence | Lesson |
|---|---|---|---|
|  |  |  |  |

### 4. Playbook Changes
| Change | Owner | Apply when | Success signal |
|---|---|---|---|
|  |  |  |  |

### Evidence And Next Decision
- Confirmed facts:
- Assumptions:
- Missing evidence:
- Next action, owner, and timing:
- Expected signal and decision threshold:

Read the full file on GitHub · 87 lines

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. 11d ago First seen · 87 lines · 54 tokens per session scan A 1d8670d19fcf

Subscribe to this mod's changes

win-loss-review is a skill published in the GitHub repository fzfclee/consulting-skills (4 stars, last pushed 23d ago), licensed Apache-2.0. It adds 54 tokens to every session and 728 once invoked, about $0.0003 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.