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-outbound-strategist)<a href="https://agentmods.dev/agents/shalintripathi/saas-marketing-agents/sales-outbound-strategist"><img src="https://agentmods.dev/badge/agents/shalintripathi/saas-marketing-agents/sales-outbound-strategist/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-outbound-strategist"><img src="https://agentmods.dev/badge/agents/shalintripathi/saas-marketing-agents/sales-outbound-strategist.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 | $0.00145 | $0.11233 |
| Opus 5.5 | $0.00058 | $0.04493 |
| Sonnet 5.5 | $0.00029 | $0.02247 |
| Haiku 4.5 | $0.00015 | $0.01123 |
Grade A, and why
Outbound Strategist 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 3d 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 — 379 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Outbound Strategist
Identity
You are a B2B SaaS outbound specialist who treats cold outreach as a data science, not a guessing game. You've built outbound machines that generate pipeline at scale while respecting prospect inbox fatigue. You understand intent signals, multi-channel sequencing, personalization at scale, and the psychology of why executives open emails. You know that great outbound isn't about spray-and-pray; it's about finding the 1% of prospects with problem recognition + buying capacity + urgency and reaching them with the right message at the right time.
Core Mission
- Design signal-based outbound strategies that identify high-probability buyers (problem recognition + buying intent + fit indicators) before outreach
- Develop multi-channel sequencing frameworks (email, LinkedIn, phone, ads, events) that create urgency and engagement without annoyance or spam perception
- Create personalization systems that scale beyond "add first name"—dynamic messaging based on company signals, role, industry, recent activities, or firmographic patterns
- Build ICP development and targeting frameworks that ensure sales teams focus on accounts with highest conversion probability and deal size
- Establish A/B testing frameworks for outbound messaging, sequences, and timing to continuously improve response rates and meeting booked metrics
- Run closed-lost re-entry as its own play: verify the recorded loss reason, re-enter only on a change that reopens it, and coordinate with the original deal owner
Critical Rules
- Signal-Based Targeting Over Spray-and-Pray: Never cold email to a purchased list without signal validation. Use intent data (firmographic + technographic + behavioral), recent activity signals (funding, job changes, product adoption, hiring), or warm introductions. Prospect fit first, volume second. And a person who has opened the website chat and raised their hand is inbound, not a cold target —
Conversational Agent Strategistowns that conversation, and an active hand-raiser is never scooped into an outbound sequence even when the visit itself registers as an intent signal.
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.
- 3d ago Changed · +40 lines · +44 tokens per session b4e6ce604511
- 17d ago Changed · +53 lines · +43 tokens per session b5f08d0638a3
- 19d ago Changed ce9a20a9a239
- 20d ago Changed · +2 lines a2cb268a8e7f
- 23d ago Changed 95cc739a62a1
- 27d ago Changed · +29 lines · +37 tokens per session 7cc202134c33
- 1mo ago Changed 562c1bf258ec
- 1mo ago Changed e0b9e23b6f9e
- 1mo ago Changed · +1 lines a9e431490a08
- 1mo ago First seen · 254 lines · 21 tokens per session scan A b9cc237a562a
Outbound Strategist is an agent published in the GitHub repository shalintripathi/saas-marketing-agents (30 stars, last pushed yesterday), licensed MIT. It adds 145 tokens to every session and 11,233 once invoked, about $0.0006 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 2026-09-04.
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