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 fzfclee/consulting-skills --skill win-loss-reviewgit clone --depth 1 https://github.com/fzfclee/consulting-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/fzfclee/consulting-skills/win-loss-review)<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.
<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>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 | $0.00054 | $0.00728 |
| Opus 5 | $0.00027 | $0.00364 |
| Sonnet 5 | $0.00011 | $0.00146 |
| Haiku 4.5 | $0.00005 | $0.00073 |
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.
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:
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.
- 11d ago First seen · 87 lines · 54 tokens per session scan A 1d8670d19fcf
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.
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