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.
git clone --depth 1 https://github.com/gtmagents/gtm-agentsWrote 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/commands/gtmagents/gtm-agents/run-win-loss-program)<a href="https://agentmods.dev/commands/gtmagents/gtm-agents/run-win-loss-program"><img src="https://agentmods.dev/badge/commands/gtmagents/gtm-agents/run-win-loss-program/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/commands/gtmagents/gtm-agents/run-win-loss-program"><img src="https://agentmods.dev/badge/commands/gtmagents/gtm-agents/run-win-loss-program.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 | $0.00024 | $0.00387 |
| Opus 5 | $0.00012 | $0.00193 |
| Sonnet 5 | $0.00005 | $0.00077 |
| Haiku 4.5 | $0.00002 | $0.00039 |
Grade A, and why
run-win-loss-program 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.
What it actually says
Command: run-win-loss-program
Inputs
- segment – target segment or region (enterprise, smb, strategic, apac, etc.).
- period – timeframe (month, quarter, half, custom).
- sample-size – number of deals to include (default 10).
- audience – comma-separated recipients (product, sales, marketing, exec, pricing).
- focus-topics – optional keywords (pricing, onboarding, security, support).
Workflow
- Sampling & Prep – select deals across wins/losses, ensure mix of personas/products.
- Interview Coordination – schedule interviews, share discussion guide, capture consent.
- Analysis & Tagging – tag qualitative insights, align with quantitative pipeline metrics.
- Insight Synthesis – highlight key drivers, quotes, and recommended actions per audience.
- Routing & Tracking – assign follow-ups to owners, track status, and plan next cycle.
Outputs
- Win/loss insight report with themes, quotes, and data visualizations.
- Deal driver dashboard (e.g., pricing, product gap, competition) with trendlines.
- Follow-up tracker mapping insights to backlog items and deadlines.
Agent/Skill Invocations
win-loss-analyst– leads sampling, interviews, and analysis.market-insights-director– connects insights to strategic priorities.win-loss-datasetskill – structures qualitative/quantitative tags.market-signal-trackerskill – cross-links insights with other market signals.
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 · 35 lines · 24 tokens per session scan A bd285dac50ae
run-win-loss-program is a command published in the GitHub repository gtmagents/gtm-agents (398 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 24 tokens to every session and 387 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 2026-09-03.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.