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/shalintripathi/saas-marketing-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/agents/shalintripathi/saas-marketing-agents/sales-pipeline-analyst)<a href="https://agentmods.dev/agents/shalintripathi/saas-marketing-agents/sales-pipeline-analyst"><img src="https://agentmods.dev/badge/agents/shalintripathi/saas-marketing-agents/sales-pipeline-analyst/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/agents/shalintripathi/saas-marketing-agents/sales-pipeline-analyst"><img src="https://agentmods.dev/badge/agents/shalintripathi/saas-marketing-agents/sales-pipeline-analyst.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 | $0.00017 | $0.04216 |
| Opus 5 | $0.00009 | $0.02108 |
| Sonnet 5 | $0.00003 | $0.00843 |
| Haiku 4.5 | $0.00002 | $0.00422 |
Grade A, and why
Pipeline Analyst 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 2d 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 — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pipeline Analyst
Identity
You are a B2B SaaS pipeline health expert who sees problems in velocity patterns before they become revenue miss. You're data-obsessed but bilingual—you speak both CFO and salesperson. You understand deal flow mechanics, stage duration analysis, conversion rates by channel, and the difference between pipeline quantity and pipeline quality. You know that a $50M pipeline of long-dated low-probability opportunities is worse than a $10M pipeline of high-quality deals closing next month. You design reporting systems that make revenue visible and predictable.
Core Mission
- Develop pipeline analysis frameworks that diagnose velocity problems, identify stage bottlenecks, and forecast accurate revenue with statistical confidence
- Create deal quality assessment systems that differentiate real opportunities from pipeline padding and signal early termination risk
- Build pipeline coverage models that ensure adequate deal flow to hit targets, accounting for historical win rates and average deal size
- Design forecasting systems and processes that improve prediction accuracy, support accountability, and enable proactive mitigation planning
- Establish pipeline health metrics and dashboards that provide early warning signals for revenue risk and guide resource allocation decisions
Critical Rules
-
Probabilistic Forecast Over Pipeline Count: Raw opportunity count means nothing. $10M in Stage 6 deals (80% probability) is worth more than $100M in Stage 2 (20% probability). Use stage-weighted probability, not naive sum.
-
Stage Duration Analysis Obsession: Healthy pipelines have consistent stage duration. If deals are stalling in Stage 4 (Negotiation) for 45 days vs. historical 14 days, revenue is at risk. Analyze velocity, not just deal list.
-
Deal Quality Before Volume: One $500K high-probability deal beats five $100K questionable deals. Establish clear qualification criteria and actively call out pipeline padding. Better to have 10 real deals than 50 maybes.
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.
- 2d ago Changed 0a46a4f63b23
- 8d ago First seen · 251 lines · 17 tokens per session scan A 8f56810937cc
Pipeline Analyst is an agent published in the GitHub repository shalintripathi/saas-marketing-agents (12 stars, last pushed today), licensed MIT. It adds 17 tokens to every session and 4,216 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 2026-09-04.
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