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 varunk130/ai-gtm-skill-library --skill win-loss-decodergit clone --depth 1 https://github.com/varunk130/ai-gtm-skill-libraryWrote 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/varunk130/ai-gtm-skill-library/win-loss-decoder)<a href="https://agentmods.dev/skills/varunk130/ai-gtm-skill-library/win-loss-decoder"><img src="https://agentmods.dev/badge/skills/varunk130/ai-gtm-skill-library/win-loss-decoder/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/varunk130/ai-gtm-skill-library/win-loss-decoder"><img src="https://agentmods.dev/badge/skills/varunk130/ai-gtm-skill-library/win-loss-decoder.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 | $0.00073 | $0.00800 |
| Opus 5.5 | $0.00029 | $0.00320 |
| Sonnet 5.5 | $0.00015 | $0.00160 |
| Haiku 4.5 | $0.00007 | $0.00080 |
Grade A, and why
win-loss-decoder 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 26d 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Win-Loss Decoder (SIGNAL Framework)
CRM loss reasons are picked by the rep in ten seconds, and "price" wins every time. Win-Loss Decoder rebuilds the reason from what the buyer said and did, codes it against a fixed taxonomy, and ranks patterns by revenue so product, marketing, and sales each get the fixes that are theirs.
Core Principle
The buyer's words decide the code. A reason without a quote or artifact from the buyer is logged as "unknown", not guessed.
The SIGNAL Framework
| Letter | Step | The Question |
|---|---|---|
| S | Sample | Which deals, and is the sample balanced across wins, losses, segments, and reps? |
| I | Interview | What did the buyer say in a 20-minute conversation after the decision? |
| G | Group | Which taxonomy code does each reason map to? |
| N | Number | How much ARR sits behind each code, for wins and for losses? |
| A | Assign | Which team owns the fix for each code? |
| L | Loop | Did the fix change the rate of that code next quarter? |
Reason Taxonomy
| Code | Covers | Default owner |
|---|---|---|
| VALUE | Buyer could not see enough return or urgency | Marketing |
| FIT | Missing capability, integration, or compliance need | Product |
| TRUST | Proof, references, security, or vendor-risk concerns | Marketing / Security |
| PROCESS | Lost the sales process: slow, no champion, wrong buyer | Sales |
| PRICE | Price or packaging, after the above are ruled out | Pricing |
| STATUS QUO | Buyer chose to do nothing | Marketing / Sales |
Process
- Sample - 15-30 recent decisions, at least one-third wins; exclude deals under the ACV floor
- Collect - buyer interviews first, then call notes and emails; note the source of every reason
- Code - up to two codes per deal (primary, secondary); PRICE only if VALUE and FIT are ruled out
- Weight - sum ARR by code for wins and losses; compute
loss share - win shareper code - Rank - largest positive gap first; these are the reasons you lose more than you win on
- Route - one fix per top code, with owner and the metric that will show it worked
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.
- 26d ago First seen · 66 lines · 73 tokens per session scan A cd890d0ef093
win-loss-decoder is a skill published in the GitHub repository varunk130/ai-gtm-skill-library (7 stars, last pushed 12d ago), licensed MIT. It adds 73 tokens to every session and 800 once invoked, about $0.0003 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 2026-09-20.
Other skills, from other repositories
customer-journey-map
Construye un mapa de recorrido del cliente para un producto, servicio o experiencia. Úsalo cuando se te pida mapear un recorrido del cliente, crear un viaje del usuario, documentar puntos de contacto y puntos de fricción, o diseñar un mapa de experiencia. Produce un mapa de recorrido completo con etapas, puntos de…
design-handoff-brief
Transform feature briefs into structured design briefs that give designers the context they need before opening Figma. Use when asked to write a design brief, create a design handoff, brief a designer on a new feature, or translate a PRD into design requirements. Produces a brief with user goal, emotional context…
session-deep-dive
Deep qualitative analysis of high-signal sessions. Spawns subagents with v2 template, synthesizes patterns, compares against known findings. Use after /session-scan.
elixir-idioms
OTP/BEAM patterns and Elixir idioms — GenServer, Supervisor, Task, Registry, pattern matching, with chains, pipes. Use when designing processes or debugging BEAM issues.
tidewave-integration
Tidewave MCP runtime tools — debugging, smoke testing, live state inspection, SQL queries, hex docs. Use when evaluating code in a running Phoenix app.
plugin-dev-workflow
Guide plugin development workflow — editing skills, agents, hooks, or eval framework in this repo. Use when modifying files in plugins/elixir-phoenix/, lab/eval/, or lab/autoresearch/. Ensures changes pass eval, lint, and tests before committing.